CN107577826B - Classification of diseases coding method and system based on raw diagnostic data - Google Patents
Classification of diseases coding method and system based on raw diagnostic data Download PDFInfo
- Publication number
- CN107577826B CN107577826B CN201711013514.0A CN201711013514A CN107577826B CN 107577826 B CN107577826 B CN 107577826B CN 201711013514 A CN201711013514 A CN 201711013514A CN 107577826 B CN107577826 B CN 107577826B
- Authority
- CN
- China
- Prior art keywords
- result
- matching
- keyword
- tree
- concept
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 title claims abstract description 90
- 201000010099 disease Diseases 0.000 title claims abstract description 89
- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000006243 chemical reaction Methods 0.000 claims abstract description 39
- 238000012545 processing Methods 0.000 claims abstract description 34
- 230000011218 segmentation Effects 0.000 claims abstract description 14
- 238000011156 evaluation Methods 0.000 claims abstract description 11
- 238000003745 diagnosis Methods 0.000 claims description 81
- 238000005520 cutting process Methods 0.000 claims description 11
- 230000008569 process Effects 0.000 claims description 10
- 230000004048 modification Effects 0.000 claims description 9
- 238000012986 modification Methods 0.000 claims description 9
- 230000002159 abnormal effect Effects 0.000 claims description 7
- 239000000203 mixture Substances 0.000 claims description 7
- 230000001131 transforming effect Effects 0.000 claims description 6
- 235000013399 edible fruits Nutrition 0.000 claims description 5
- 208000024891 symptom Diseases 0.000 claims description 5
- 230000001174 ascending effect Effects 0.000 claims description 4
- 230000009466 transformation Effects 0.000 claims description 4
- 210000003484 anatomy Anatomy 0.000 claims description 3
- 230000000694 effects Effects 0.000 claims description 3
- 230000000750 progressive effect Effects 0.000 claims description 3
- 241000700605 Viruses Species 0.000 claims description 2
- 230000001580 bacterial effect Effects 0.000 claims description 2
- 230000007812 deficiency Effects 0.000 claims description 2
- 230000007170 pathology Effects 0.000 claims description 2
- 230000035479 physiological effects, processes and functions Effects 0.000 claims description 2
- 238000001914 filtration Methods 0.000 abstract 1
- 208000027530 Meniere disease Diseases 0.000 description 9
- 230000036541 health Effects 0.000 description 8
- 206010061216 Infarction Diseases 0.000 description 7
- 230000007574 infarction Effects 0.000 description 7
- 210000004227 basal ganglia Anatomy 0.000 description 6
- 238000011161 development Methods 0.000 description 5
- 230000018109 developmental process Effects 0.000 description 5
- 238000013473 artificial intelligence Methods 0.000 description 4
- 210000004556 brain Anatomy 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 3
- 238000010276 construction Methods 0.000 description 3
- 239000003814 drug Substances 0.000 description 3
- 238000007726 management method Methods 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 206010069020 Basal ganglia infarction Diseases 0.000 description 2
- 206010010356 Congenital anomaly Diseases 0.000 description 2
- 241000521257 Hydrops Species 0.000 description 2
- 208000020128 Mitral stenosis Diseases 0.000 description 2
- 206010030113 Oedema Diseases 0.000 description 2
- 201000001943 Tricuspid Valve Insufficiency Diseases 0.000 description 2
- 206010044640 Tricuspid valve incompetence Diseases 0.000 description 2
- 206010046851 Uveitis Diseases 0.000 description 2
- 210000000133 brain stem Anatomy 0.000 description 2
- 230000006378 damage Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 208000006887 mitral valve stenosis Diseases 0.000 description 2
- 238000003058 natural language processing Methods 0.000 description 2
- 230000036285 pathological change Effects 0.000 description 2
- 231100000915 pathological change Toxicity 0.000 description 2
- 230000037361 pathway Effects 0.000 description 2
- 231100000572 poisoning Toxicity 0.000 description 2
- 230000000607 poisoning effect Effects 0.000 description 2
- 239000013598 vector Substances 0.000 description 2
- 241000894006 Bacteria Species 0.000 description 1
- 208000002691 Choroiditis Diseases 0.000 description 1
- 206010011409 Cross infection Diseases 0.000 description 1
- 241001269238 Data Species 0.000 description 1
- 241000233866 Fungi Species 0.000 description 1
- 208000026350 Inborn Genetic disease Diseases 0.000 description 1
- 206010022941 Iridocyclitis Diseases 0.000 description 1
- 206010028980 Neoplasm Diseases 0.000 description 1
- 206010035664 Pneumonia Diseases 0.000 description 1
- 208000003971 Posterior uveitis Diseases 0.000 description 1
- 239000004614 Process Aid Substances 0.000 description 1
- 208000009205 Tinnitus Diseases 0.000 description 1
- 208000012886 Vertigo Diseases 0.000 description 1
- 208000027418 Wounds and injury Diseases 0.000 description 1
- 230000001154 acute effect Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 201000004612 anterior uveitis Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 201000008247 brain infarction Diseases 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 201000011510 cancer Diseases 0.000 description 1
- 206010008118 cerebral infarction Diseases 0.000 description 1
- 208000026106 cerebrovascular disease Diseases 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 239000007795 chemical reaction product Substances 0.000 description 1
- 230000035606 childbirth Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000005034 decoration Methods 0.000 description 1
- 208000028659 discharge Diseases 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 208000002173 dizziness Diseases 0.000 description 1
- 210000003027 ear inner Anatomy 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000005194 fractionation Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 208000016361 genetic disease Diseases 0.000 description 1
- 230000005802 health problem Effects 0.000 description 1
- 208000036260 idiopathic disease Diseases 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 208000014674 injury Diseases 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 210000003734 kidney Anatomy 0.000 description 1
- 210000004072 lung Anatomy 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 210000000056 organ Anatomy 0.000 description 1
- 238000010827 pathological analysis Methods 0.000 description 1
- 230000009984 peri-natal effect Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 230000035935 pregnancy Effects 0.000 description 1
- 238000004801 process automation Methods 0.000 description 1
- 238000013138 pruning Methods 0.000 description 1
- 230000005180 public health Effects 0.000 description 1
- 230000000306 recurrent effect Effects 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 238000002560 therapeutic procedure Methods 0.000 description 1
- 231100000886 tinnitus Toxicity 0.000 description 1
- 230000017105 transposition Effects 0.000 description 1
Landscapes
- Medical Treatment And Welfare Office Work (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
Abstract
The invention discloses the classification of diseases coding method based on raw diagnostic data and system, include the following steps：Receive input data：Input data includes：Raw diagnostic data；The raw diagnostic data of input is pre-processed；Retrieved in criteria classification coding ICD 10, judge whether to obtain as a result, if it is not, then then filtering out optimal result from the handling result for matching tree to raw diagnostic data progress word segmentation processing, association's conversion processing and search matching tree processing；The coding accuracy of assessment result, exports coding result and accuracy evaluation result.Efficiently solve the problems, such as that the standard diagnostics coding that each medical institutions use is skimble-scamble.
Description
Technical field
The present invention relates to medical information technical field and field of artificial intelligence, more particularly to one kind to be examined based on original
The classification of diseases coding method of disconnected data and system.
Background technology
With country's deepening continuously and deepen to the layout of public medical and medical reform, medical services are comprehensive to be carried
Rise and urgent requirement is proposed to medical information.In medical insurance, public health service, hospital informatioization management etc., medical treatment
Informationization is from the process automation management development of completion overall process record to internet+health medical treatment；In present big data and people
Under work intelligence background, medical information progressively touches the core business of medical treatment, provides diagnosis and treatment process aid decision and instructs to join
Examine.In medical profession and medical teaching, academic exchange, data analysis or even medical artificial intelligence's aid decision, standardization
Information (diagnosis, case history, checks, medicine etc.) record and to exchange be the essential even most important thing, wherein it is primary just
It is the standardization of diagnosis：Standard diagnostics encode (ICD).
(1) the non-constant disunity of understanding and grasping of the coder for standard code, causes coding result to have differences.
Diagnosis in first page of illness case reports, before medical insurance reimbursement in case history filing, medical record, by case of hospital room coder according to
According to《The international statistical classification of diseases and related health problems》(being commonly called as handbook), and defend the standard diagnostics coded word of planning commission's issue
Allusion quotation, encodes the diagnosis on first page of illness case.What this work was manually performed, therefore manpower is limited by, encoding human
Member's quality, the deciphering to standard and the assurance degree to the huge content of encoder dictionary, and length of service and experience, so that daily
Manually to the limited amount of code, and there is a situation where erroneous judgement, and encode fineness and criterion also varies with each individual, not
With hospital data contrast with exchange, these problems will be highlighted and are exaggerated, in addition between hospital of same institute different coding person,
Same coder's different times, encode that there is also difference.
(2) the actual diagnosis of personalized, the self-defined input of doctor is not exclusively compatible with standard diagnostics code database.
Since the diagnosis actually used in doctor's routine work cannot be completely covered in the diagnosis included in diagnosis coding storehouse, and
And for same diagnosis, due to the particularity of Chinese, in the extreme diversity and industry of same the concept of diseases form of presentation simultaneously
There is no unified standard this specification of medical terms, the title literary style and structure of the diagnosis that different doctors uses also phase not to the utmost
Together, multifarious, the alias used has very big difference；Renewal plus standard diagnostics dictionary takes a long time (national standard
ICD10, last time update 2009 apart from this renewal just 8 years as long as in the past 2017), it can not cover in time most emerging
Disease and its title；And the clinical practice requirement of doctor is not reached for the parting of particular diagnosis, the level of detail of standard code；
Or doctor write diagnose when, can add some additional detailed informations when writing and diagnosing, and standard diagnostics code database
The difference classification brought for extraneous information can not embody；Furthermore doctor also can use some only when writing diagnosis
Have it is known in the industry write a Chinese character in simplified form referred to as or abbreviation, and these are nor standard diagnostics coding place is included.Essentially,
Doctor is the detailed record disease information from diagnosis and treatment operational angle, rather than for sorting code number, so incompatible feelings
Condition is inveteracy inevitable, and above-mentioned a variety of causes result in most doctors in hospital's practical business and fill in diagnosis
When using procedure selection or hand-written coding, all there are many mistakes.
(3) standard diagnostics code database version disunity.
Mainly in national standard diagnosis coding storehouse, 09 year version combines respectively the code database that current hospital each on the market uses
The demand modifications and extensions of a hospital oneself, which are spread out, to be stretched, and the part of modifications and extensions does not have special organisations and institutions' unification
Management and distribution, the version between hospital and hospital can not be completely compatible, in addition have hospital use earlier country mark
Quasi- version change spreads out what is stretched, even if so causing same disease, the coding used in Different hospital cannot guarantee that
Complete unity, and each hospital part stretched of being spread out by national standard version just less can guarantee that unanimously.
(4) in the accuracy of Medical Record Statistics data, medical treatment, teaching, the accuracy of scientific research data retrieval, disease packet DRGS
Medical insurance pre-payment (DRGS-PPS) sound development etc., diagnosis coding correct and that standard is unified is the base of everything
Plinth.
In " 13 " national population health Informatization Development planning, whether in the population health of the unified authority of structure
Information platform, health medical treatment data standard, still improve all kinds of basic businesses of population health information using upper on this basis, with
And the high-level application after being built in medical information platform, medical information warehouse, including but not limited to medical dynamic monitoring, disease
Control, medical research, medical artificial intelligence, aid decision etc., diagnostic message cross-platform exchange and are needed after collecting across mechanism
Can keep stablizing with it is consistent, and be based on four reasons noted earlier, diagnostic message is being collected, interact, collected, in analytic process
Need by substantial amounts of code standardized work.
The content of the invention
The purpose of the present invention is exactly to solve the above-mentioned problems, there is provided a kind of classification of diseases based on raw diagnostic data is compiled
Code method and system, input according to doctor and diagnose, with reference to the parsing participle and semantic understanding to case history, automatic reference standard diagnosis
Storehouse ICD-10 is encoded；Its correct possibility can be assessed coding result.
To achieve these goals, the present invention adopts the following technical scheme that：
Classification of diseases coding method based on raw diagnostic data, includes the following steps：
Step (1)：Receive input data：The input data includes：Raw diagnostic data；
Step (2)：The raw diagnostic data of input is pre-processed；
Step (3)：The pre-processed results obtained with step (2) are in GB/T 14396-2016《Classification of diseases and code》And
Retrieved in international disease criterion sorting code number ICD-10, judge whether to obtain as a result, if obtain as a result, if direct exports coding
As a result；If it is not, enter step (4)；
Step (4)：Word segmentation processing, association's conversion processing and search matching tree are carried out to pretreated raw diagnostic data
Processing, then filters out optimal result from the handling result of matching tree, enters step (5)；
Step (5)：The coding accuracy of assessment result, exports coding result and accuracy evaluation result.
The pretreatment of the step (2) includes：Punctuation mark is removed, variant Chinese character is converted into upright letters, by double byte character
Be converted to half-angle character.
The word segmentation processing, refers to：Will sentence segmentation be segmented for several diagnosis keywords, the diagnosis keyword,
Including representing the qualifier for limiting modification and the primary keyword for representing disease；The qualifier refers to descriptive nature, position or journey
Spend the word of type；The primary keyword refers to the word for describing disease, abnormal structure, abnormal body or abnormal symptom；
Association's conversion processing, refers to：Obtained qualifier will be segmented and primary keyword is marked in medical semantic network
On, association's conversion is carried out respectively to qualifier and primary keyword using medical semantic network, by former primary keyword and former main key
The new primary keyword obtained after word association conversion is carried out with the new qualifier obtained after former qualifier and former qualifier association conversion
Permutation and combination, finally obtains all combinations between primary keyword and qualifier in raw diagnostic data；
Such as：Basal ganglia infarction, is Basal ganglia and infarct after word segmentation processing, is converted by semantic network, Basal ganglia association
Basal ganglia, brain stem and brain are converted into, infarct association is converted into infarct and infraction, then the knot after this two keywords and conversion
The result of fruit combination includes：Combination 1：Basal ganglia _ infraction, combination 2：Basal ganglia _ infarct, combination 3：Brain stem _ infraction, combination 4：Brain
Dry _ infarct, combination 5：Brain _ infraction, combination 6：Brain _ infarct.Combination 1 to 6 is exactly all combinations.
Described search matching tree processing, refers to：Institute between the primary keyword and qualifier that are obtained according to association's conversion processing
There is combination, the corresponding matching tree that leaf is completely covered of each combination is searched out from matching forest, the result is that a matching tree,
Several matchings are set or without result；
It is described to filter out optimal result from the handling result of matching tree, refer to：
Step (a1)：The keyword quantity on matching tree that will match to carries out descending arrangement and compares respectively, if ranking
First it is identical with second or identical with what is ranked the first have it is multiple,
Compared with the keyword quantity split then is carried out ascending order arrangement with the keyword ratio of number of matching tree；If
Obtain rank the first it is identical with second or identical with what is ranked the first have multiple, then enter step (a2)；
Step (a2)：Qualifier and primary keyword are overlapped in the conversion distance of medical semantic network, superposition is tied
Fruit carries out descending arrangement and compares, if rank the first it is identical with second or identical with what is ranked the first have it is multiple, into
Enter step (a3)；
Step (a3)：The matching degree of matching tree is calculated, the matching degree of the matching tree is equal to the main pass that participle obtains
The quantity of keyword and the primary keyword ratio of number for matching tree, carry out ratio of number descending arrangement and compare；If rank the first
It is identical with second or identical with what is ranked the first have multiple, then terminate；
In step (a1)-step (a3), if optimal result only has one, that is, the result to rank the first only has one
(second place and follow-up all different from first place), then it represents that current matching tree is Optimum Matching tree.
The coding accuracy of the assessment result, exists from the matching degree of result and raw diagnostic data, diagnosis keyword
Converting for medical semantic network diagnoses order of the keyword with diagnosing keyword in standard diagnostics in distance, raw diagnostic data
Three angles of otherness are assessed；
The matching degree of the result and raw diagnostic data, is：In all matching tree results matched, count first
Calculate every group of qualifier being syncopated as and primary keyword total quantity and set the ratio between the qualifier included and primary keyword total quantity with matching
Value, is the first ratio；Secondly the ratio of number of primary keyword of the primary keyword quantity being syncopated as with matching tree is calculated, is
Second ratio；Second ratio and the first ratio, are the matching degree of result and raw diagnostic data.
The diagnosis keyword is in the conversion distance of medical semantic network：Keyword will each be diagnosed medical semantic
Network is transformed into the path length that the corresponding diagnosis keyword of matching tree passes through and is denoted as a transformation ratio, calculates all match
Diagnosis keyword the sum of transformation ratio natural logrithm, as diagnose conversion distance of the keyword in medical semantic network.
By taking basal ganglia infarction as an example, correspond in cerebral infarction, Basal ganglia is transformed into brain, and conversion weight is 0.3, infarct conversion
To infarct, conversion weight is 1, then using the form calculus weighted superposition result of the sum of natural logrithm as ln (0.3)+ln (1)=-
1.204。
The otherness of order of the keyword with diagnosing keyword in ICD-10 is diagnosed in the raw diagnostic data, is：It is first
First calculate same position of the diagnosis keyword in raw diagnostic data and in ICD-10 position sequence difference, then calculate
The sum of absolute value of sequence difference of all diagnosis keywords.
As a result coding accuracy evaluation formula：
Y=wTX+b；
Wherein, y is estimation accuracy, and X is vector (x1,x2,x3), wherein x1Represent of result and raw diagnostic data
With degree, x2Represent diagnosis keyword in the conversion distance of medical semantic network, x3Represent to diagnose keyword in raw diagnostic data
Otherness with the order that keyword is diagnosed in ICD-10.
Represent three process datas, w in matching processTFor the transposition of vectorial w, w vectors are (w1,w2,w3), wherein, w1,
w2,w3, b is constant.
In the step (1),
Raw diagnostic data, scope include：The discharge diagnosis of diagnosis, first page of illness case in patient file, pathological diagnosis with
And the external cause diagnosis of Injuries and poisoning；
The side information includes：Age, gender, site of pathological change, disease property, peri-operation period, hospital infection disease, sheet
It is secondary diagnosis and treatment purpose, main diagnostic message, inspection, pathology, imaging information, familial, heredity, oldness, sequelae, congenital
Property disease, operation or the mode of production.
Qualifier, including：Position, disease property, orientation, disease parting, degree etc., for example, it is left side, right side, acute, first
Nature, icteric, leaf etc. on lung.
Keyword is diagnosed, including：Disease, abnormal structure's component etc., such as pneumonia, deformity, wandering kidney etc..
The cutting structure formed between disease and disease, such as A diseases cause B diseases with B diseases (parallel construction), A diseases
Sick (modification limiting structure), A diseases (B diseases), (progressive structure) etc..
The word segmentation processing, refers to according to GB/T 14396-2016《Classification of diseases and code》And international disease criterion point
Class encodes ICD-10 and full cutting is carried out to the raw diagnostic data after data cleansing, and each word is closed as diagnosis in cutting result
Keyword；Keyword is diagnosed, including：Represent the qualifier for limiting modification and the primary keyword for representing disease；
Semantic network is a kind of structured way that knowledge is represented with figure；In a semantic network, information is expressed
For one group of node, node is connected with each other by the directed line of one group of tape label, for representing the relation between node.
The medical treatment semantic network is the semantic network of medical field, and the node body of medical semantic network is medical domain
Concept, the medical domain concept node are connected with other medical domain concept nodes, and each medical domain concept node is again
It is connected with the disease concept form of expression node of itself；Each medical domain concept node is also general with property concept node, degree
Read node, position concept node or the connection of body concept node；Relation between the node of the medical treatment semantic network is medical treatment
Relation between field concept.
The medical field concept, including：The physiology region of anatomy, body tissue, component, disease exception, bacterial virus, disease
Reason, disease property；
Relation between medical domain concept, including：Correlation, transforming relationship, correlation weight, transforming relationship power
The relation of weight and concept to specific manifestation.
Correlation between medical domain concept and concept, including：The including, is affiliated of concept, abstract or specific manifestation；
Transforming relationship between medical domain concept and concept, including：Concept it is close or identical；
By medical semantic network, the association and conversion of real concept, so as to expand search range and the association's model of concept
Enclose；And the corresponding specific manifestation of each concept is various informative, and not only include the title of written specification, also cover reality
The colloquial title used, thus specification term and the incompatible of practical application of official standard diagnosis of having prevented and conflicting is asked
Topic.
More than one for disease quantity in raw diagnostic data, diagnosis cutting result can be divided into two or more portions
Point, each part includes a primary keyword and corresponding qualifier.During matching, various pieces are searched as a group input
Rope matching tree.Some standard diagnostics include multiple diseases, are made of so it matches leaf some, and each part has
Qualifier and keyword, and include the relation between each several part.Relation includes, with concurrently, leading between each several part
Cause, not companion, exclude etc..
Matching forest includes several matching trees, each matching tree, including：Tree root, trunk, branch and leaf；Described
Tree root with tree represents diagnosis concept, shows as ICD codings；The trunk of the matching tree represents the performance diagnosis name of diagnosis concept
Claim；(usual standard diagnostics only have one for the concrete composition part of the branch expression diagnosis concept performance diagnosis name of the matching tree
A disease, trunk is one；When shelves standard diagnostics include multiple diseases, trunk is corresponding number)；The tree of the matching tree
Leaf represents the qualifier and primary keyword of the concrete composition part of diagnosis concept performance diagnosis name.
Match the forming process of forest：Single standard diagnostics are a concepts, and concept includes several forms of expression；Often
Kind of the form of expression have the structure of oneself, the conceptual entity included and comprising each conceptual entity between correlation；Each
Concept that standard diagnostics represent, structure, the conceptual entity included and comprising each conceptual entity between correlation tree
Structure represents, is defined as matching tree, and the matching tree of all standard diagnostics forms matching forest, also, is referred to according to icd standard
South, in forest is matched, there are priority and inclusion relation between matching tree.
The form of expression, such as：Title.
Every kind of form of expression has the structure of oneself：Side by side, progressive explanation, cause and effect etc.；
Every kind of form of expression has the conceptual entity included of oneself：Symptom, disease, operation etc.；
Every kind of form of expression has the correlation between each conceptual entity included of oneself：Keyword and qualifier, limit
Periodical repair decorations etc.；
The root of the matching tree represents diagnosis concept, its concept shows as ICD codings；Such as：What Meniere disease was stated
Concept is that a kind of pathological change is labyrintine hydrops, clinical manifestation be the rotatory vertigo of recurrent exerbation, fluctuation Hearing,
Tinnitus and the idiopathic disease of inner ear of the swollen sense of ear muffle.
Since concept is abstract things, it is necessary to which a unique mark corresponds to mark, title is one kind of concept
Performance, and exactly the corresponding of each disease identifies standard diagnostics coding ICD, also just becomes the unique of each concept naturally
Mark, that is, the performance of the concept of diseases.
Such as the concept of Meniere disease expressed above, in standard diagnostics, the ICD of Meniere disease is encoded to H81.000,
So this ICD codings H81.000 is exactly the performance of the concept of Meniere disease, meanwhile, the root as the matching tree of Meniere disease.
The trunk of the matching tree represents diagnosis concept performance diagnosis name；Such as：The performance title of Meniere disease concept
There are Meniere disease, auditory vertigo and labyrintine hydrops.
The branch of the matching tree represents the concrete composition part of diagnosis concept performance diagnosis name, such as mitral stenosis
With tricuspid insufficiency, there are two branches, be respectively mitral stenosis and tricuspid insufficiency；
The leaf of the matching tree represents the qualifier of the concrete composition part of diagnosis concept performance diagnosis name and main pass
Keyword；Such as：Auditory vertigo, primary keyword are dizziness, and qualifier is auditory.
Word segmentation processing is carried out to medical record data file using natural language processing program ansj_seg, the result after processing is
The data of structuring；The word separated is stored classifiedly according to generic, such as is categorized as personnel, organ, the time, place,
Frequency, symptom, operation, medicine, medical history etc. store classifiedly the data as structuring；From the data of structuring, extract and examine
Disconnected relevant information, such as：The cause of disease such as perinatal period, bacterium, fungi such as gestation, childbirth, puerperium, family's disease, genetic disease
Or the disease property such as congenital, posteriority, damage, the external cause of poisoning, the cytomorphology classification of cancer.
Described search matching tree processing, there is three kinds of situations,
The first is without as a result, the result of output is sky, then the reason for prompting is without matching result is original diagnostic information
Deficiency.
Second is to have as a result, the optimal result selected that finally sorts only has one, then using this optimal result as
Final matching results export.
The third be have as a result, the optimal result selected have it is multiple, then output result for sky, and prompt without matching result
The reason for be have multiple matching degrees it is identical as a result, and using multiple optimal results as prompt message a part export；Need
Re-enter diagnosis and more detailed information is provided in original basis.
Classification of diseases coded system based on raw diagnostic data, including：Memory, processor and storage are on a memory
And the computer instruction run on a processor, when the computer instruction is executed by processor, complete following steps：
Step (1)：Receive input data：The input data includes：Raw diagnostic data；
Step (2)：The raw diagnostic data of input is pre-processed；
Step (3)：The pre-processed results obtained with step (2) are in GB/T 14396-2016《Classification of diseases and code》And
Retrieved in international disease criterion sorting code number ICD-10, judge whether to obtain as a result, if obtain as a result, if direct exports coding
As a result；If it is not, enter step (4)；
Step (4)：Word segmentation processing, association's conversion processing and search matching tree are carried out to pretreated raw diagnostic data
Processing, then filters out optimal result from the handling result of matching tree, enters step (5)；
Step (5)：The coding accuracy of assessment result, exports coding result and accuracy evaluation result.
A kind of computer-readable recording medium, is stored thereon with computer instruction, and the computer instruction is held by processor
During row, following steps are completed：
Step (1)：Receive input data：The input data includes：Raw diagnostic data；
Step (2)：The raw diagnostic data of input is pre-processed；
Step (3)：The pre-processed results obtained with step (2) are in GB/T 14396-2016《Classification of diseases and code》And
Retrieved in international disease criterion sorting code number ICD-10, judge whether to obtain as a result, if obtain as a result, if direct exports coding
As a result；If it is not, enter step (4)；
Step (4)：Word segmentation processing, association's conversion processing and search matching tree are carried out to pretreated raw diagnostic data
Processing, then filters out optimal result from the handling result of matching tree, enters step (5)；
Step (5)：The coding accuracy of assessment result, exports coding result and accuracy evaluation result.
Beneficial effects of the present invention：
1. solve doctor's raw diagnostic correspond to standard diagnostics can only be by being accomplished manually, mainly by coder
The medical knowledge and coding specification knowledge understood by itself, could complete the problem of this works.It is partial to breach needs
By language understanding, the difficulty thought deeply by medical knowledge.Solve the unfettered limitation of term that doctor inputs diagnosis, no doctor
Vocabulary standard, which can refer to caused same diagnosis concept, but several diagnosis names and substantial amounts of different specific literary styles, so that
The problem of difficulty is compareed with standard diagnostics.
2. efficiently solve the problems, such as that the standard diagnostics coding that each medical institutions use is skimble-scamble.Autocoding is used
Afterwards, raw diagnostic is corresponded on same set of standard diagnostics coding, and criteria for classification is unified, is protected during medical institutions' data exchange
Card standard is unified.
3. criteria for classification is stablized, it is unstable to solve coder's criteria for classification caused by being subject to the description word of raw diagnostic
It is fixed, it is corresponding several times before and after same diagnosis to encode the problem of inconsistent.
4. using computer program autocoding, the human resources of flood tide are not only saved, and the very big amplitude of efficiency carries
Height, accuracy compares h coding's raising and criteria for classification is unified.The diagnosis number that a province (such as Shandong Province) produces in theory
Amount can be completed to encode within several hours.
5. automatic diagnosis coding advantageously ensures that medical treatment, teaching, the data-searching accuracy of scientific research, and disease packet
The development of DRGS.In " 13 " national population health Informatization Development planning, the population health information of unified authority is built
Platform, health medical treatment data standard, autocoding will play power-assisted effect.
6. since autocoding is quick and classification is stablized, it can be achieved that high-volume encodes original case history in a short time
Classification, can be that the big data application of medical field and artificial intelligence quickly prepare and arranges data, based on the field
Function plays the role of irreplaceable.
Brief description of the drawings
Fig. 1 is standard diagnostics Auto-matching flow chart；
Fig. 2 is semantic network structure diagram；
Fig. 3 is matching tree construction schematic diagram；
Embodiment
The invention will be further described with embodiment below in conjunction with the accompanying drawings.
As shown in Figure 1, the classification of diseases coding method based on raw diagnostic data, includes the following steps：
Step (1)：Receive input data：The input data includes：Raw diagnostic data；
Step (2)：The raw diagnostic data of input is pre-processed；
Step (3)：The pre-processed results obtained with step (2) are in GB/T 14396-2016《Classification of diseases and code》And
Retrieved in international disease criterion sorting code number ICD-10, judge whether to obtain as a result, if obtain as a result, if direct exports coding
As a result；If it is not, enter step (4)；
Step (4)：Word segmentation processing, association's conversion processing and search matching tree are carried out to pretreated raw diagnostic data
Processing, then filters out optimal result from the handling result of matching tree, enters step (5)；
Step (5)：The coding accuracy of assessment result, exports coding result and accuracy evaluation result.
As shown in Fig. 2, medical semantic network is made of the oriented relation between node and node, node includes conceptual entity
And point of specific manifestation, conceptual entity include the disease, region of anatomy, body tissue, component, disease in the classification of medical field
Matter etc..Oriented relation between node include comprising, belong to, specific manifestation, abstract, nearly justice etc..Such as in figure：Uveitis is
One conceptual entity, is inclusion relation with conceptual entities such as posterior uveitis, anterior uveitis；It is scorching with pigmented film eye and tunica vasculose
It is nearly justice/synonymy；Uveitis belongs to illness in eye at the same time.
As shown in figure 3, each standard diagnostics matching tree construction includes：Concept is diagnosed, specific manifestation form is standard diagnostics
Coding, can be using pictute as tree root；Diagnosis concept specifically show title, can be one be also likely to be it is multiple, can be with shape
As being described as trunk；For each title, multiple diseases or referred to as some, each disease or part may be wherein included
Can using pictute as trunk, between disease and disease in other words part part between pass of the relationship description between branch
System；And keyword possessed by each disease or part can be described as leaf.When the combination of some keywords can be covered completely
The keyword (leaf) of lid disease (branch), then mean that matching has suffered this disease；In matching at the same time between each disease
Relation character standardization diagnosis in each disease or partial relation, then represent matching in this disease title, Jin Erbiao
What is reached is the content of this concept of diseases statement or the subclass of a subdivision.
System and method is included with lower module and algorithm：
1. the characteristic matching network in standard diagnostics storehouse：
Single standard diagnostics essence is a concept, and concept has many forms, is segmented again under every kind of form of expression
Connecting each other between conceptual entity, and the conceptual entity of subdivision and structure, the conceptual entity of subdivision is in medical semantic network
In there is same or similar conceptual entity to associate and convert again, so, the concept and its knot that each standard diagnostics represent
Structure and comprising concept can be represented with the structure of tree, form a matching tree, and the matching tree of all standard diagnostics is formed
Match forest.
Matching forest is combined with medical semantic network, constitutes new characteristic matching network：By semantic network, realize
Association and conversion, so that search and association's scope are expanded, and can be by the matching of characteristic matching completion standard diagnostics.
2. diagnosis is split and conceptual entity identification module：
Natural language processing is carried out to raw diagnostic, after character pre-processing, medical concept entity is identified, gives standard
Diagnostic characteristic matching network is used to be marked on matching network.
In identification process, medical concept dictionary is refined from actual traffic data, thus in medical field ratio
General dictionary is related to more professional and deep.The structure of diagnosis is parsed in medical concept entity procedure is identified, judges to examine
Disconnected is reasonable and normative, for lifting accuracy in the matching process.
3. the matching algorithm of diagnosis：
Keyword and structure after splitting will be diagnosed, will be projected on matching network, by the association and conversion of semantic network,
The conceptual entity that will likely be expressed is tagged in matching network on semantic network, and then the conceptual entity of these marks passes through matching
Tree finds the standard diagnostics for the condition that meets.Standard diagnostics to meeting condition, establishing criteria are diagnosed to original diagnostic information and knot
The degree of structure covering, associates conversion pathway length, and the priority and belonging relation of standard diagnostics, selection on semantic network
Go out most suitable matching diagnosis.
4. encode accuracy evaluation module:
During matching and diagnosing, by optimal matching result, by coupling path, and raw diagnostic and matched mark
The information level of coverage and similarity degree of quasi- diagnosis are recorded.By above-mentioned each factor with different weights collect calculating one it is credible
Value, the foundation as this matched correctness of assessment.
5. result output module：
As a result output module, is output in interactive interface or specified file or in database.
The step of autocoding of the present invention, is as follows:
1a obtains the diagnosis of input from interface.
1b is obtained from database and diagnosed, and corresponding diagnosis and therapy recording, patient file.If there is patient file, then hand over
Handled by participle program,
Raw diagnostic by diagnostic analysis and conceptual entity identification module, is carried out natural language processing, with medicine semantic net by 2
Based on network, by all possible cutting and identification conceptual entity mode list, and by cutting and recognition result it is unreasonable or
Incomplete result progress beta pruning, each conceptual entity then analyzed to rational cutting and recognition result, and in concept
Whether the syntactic structure that diagnosis is judged in the structure of entity composition is rational structure, verifies cutting identification conceptual entity in turn
Reasonability.
Standard diagnostics matching module below is transferred to select different matching schemes to be matched according to the structure after cutting.
3 standard diagnostics matching modules are by the cutting recognition result and structural information of raw diagnostic, in the matching of standard diagnostics
On network, scanned for according to matching algorithm.Each diagnosis concept and modification limit concept in search procedure, successively by tool
Body surface is existing to concept, and concept to association and approximation, include concept, concept and conceptual combinations to standard diagnostics specific manifestation, standard
Diagnose search and conversion pathway of the specific manifestation to standard diagnostics concept.
Meanwhile the degree that includes that can record conversions concepts is searched in conversion process, searching route length, raw diagnostic is split
The concept that the concept that goes out is included with end product meet and level of coverage.
If 4 are not reached as a result, or drawing multiple due to matching module caused by necessary information missing in raw diagnostic
Level of coverage is identical but conceptually differ more diagnosis if, just need to extract side information from patient file at this time.
Patient file is segmented by participle program and is converted into the document of structuring, is therefrom extracted with diagnosing relevant necessary letter
Breath, is supplemented in the concept of raw diagnostic fractionation, is scanned for again in matching network.
5 accuracy evaluation modules will be searched in matching process, be associated the path of conversion, be searched for matched path, original to examine
The disconnected concept matching degree for splitting concept and standard diagnostics, the reasonability of raw diagnostic structure and journey similar to standard diagnostics structure
Degree, collects calculating by different weights, the accuracy according to result of calculation assessment to code.
Although above-mentioned be described the embodiment of the present invention with reference to attached drawing, model not is protected to the present invention
The limitation enclosed, those skilled in the art should understand that, on the basis of technical scheme, those skilled in the art are not
Need to make the creative labor the various modifications that can be made or deformation still within protection scope of the present invention.
Claims (8)
1. the classification of diseases coding method based on raw diagnostic data, it is characterized in that, include the following steps：
Step (1)：Receive input data：The input data includes：Raw diagnostic data；
Step (2)：The raw diagnostic data of input is pre-processed；
Step (3)：The pre-processed results obtained with step (2) are in GB/T 14396-2016《Classification of diseases and code》It is and international
Retrieved in disease criterion sorting code number ICD-10, judge whether to obtain as a result, if obtain as a result, if direct exports coding knot
Fruit；If it is not, enter step (4)；
Step (4)：Pretreated raw diagnostic data is carried out at word segmentation processing, association's conversion processing and search matching tree
Reason, then filters out optimal result from the handling result of matching tree, enters step (5)；
It is described to filter out optimal result from the handling result of matching tree, refer to：
Step (a1)：The keyword quantity on matching tree that will match to carries out descending arrangement and compares respectively, if ranked the first
It is identical with second or identical with what is ranked the first have it is multiple,
Compared with the keyword quantity split then is carried out ascending order arrangement with the keyword ratio of number of matching tree；If obtain
Rank the first it is identical with second or identical with what is ranked the first have multiple, then enter step (a2)；
Step (a2)：Qualifier and primary keyword are overlapped in the conversion distance of medical semantic network, to stack result into
The arrangement of row descending is compared, if rank the first it is identical with second or identical with what is ranked the first have multiple, enter step
Suddenly (a3)；
Step (a3)：The matching degree of matching tree is calculated, the matching degree of the matching tree is equal to the primary keyword that participle obtains
Quantity with match tree primary keyword ratio of number, to ratio of number carry out descending arrangement compare；If rank the first with
Second it is identical or identical with what is ranked the first have multiple, then terminate；
In step (a1)-step (a3), if optimal result only has one, that is, the result to rank the first only has one, then table
It is Optimum Matching tree to show current matching tree；
Step (5)：The coding accuracy of assessment result, exports coding result and accuracy evaluation result.
2. the classification of diseases coding method based on raw diagnostic data as claimed in claim 1, it is characterized in that, at the participle
Reason, refers to：It is that several diagnosis keywords, the diagnosis keyword, including expression limit modification by sentence segmentation to be segmented
Qualifier and the primary keyword for representing disease；The qualifier refers to the word of descriptive nature, position or degree type；The master
Keyword refers to the word for describing disease, abnormal structure, abnormal body or abnormal symptom；
Association's conversion processing, refers to：Obtained qualifier will be segmented and primary keyword is marked on medical semantic network, profit
Association's conversion is carried out respectively to qualifier and primary keyword with medical semantic network, former primary keyword and former primary keyword are associated
The new primary keyword obtained after conversion carries out arrangement group with the new qualifier obtained after former qualifier and former qualifier association conversion
Close, finally obtain all combinations between primary keyword and qualifier in raw diagnostic data.
3. the classification of diseases coding method based on raw diagnostic data as claimed in claim 2, it is characterized in that,
Described search matching tree processing, refers to：All groups between the primary keyword and qualifier that are obtained according to association's conversion processing
Close, the corresponding matching tree that leaf is completely covered of each combination is searched out from matching forest, the result is that a matching sets, is some
A matching is set or without result.
4. the classification of diseases coding method based on raw diagnostic data as claimed in claim 1, it is characterized in that,
The coding accuracy of the assessment result, from the matching degree of result and raw diagnostic data, diagnosis keyword in medical treatment
Semantic network converts the difference that order of the keyword with diagnosing keyword in standard diagnostics is diagnosed in distance, raw diagnostic data
Three angles of property are assessed；
The matching degree of the result and raw diagnostic data, is：In all matching tree results matched, calculate first every
Qualifier and primary keyword total quantity that group is syncopated as set the ratio of the qualifier included and primary keyword total quantity with matching, i.e.,
For the first ratio；Secondly the ratio of number of primary keyword of the primary keyword quantity being syncopated as with matching tree is calculated, is second
Ratio；Second ratio and the first ratio, are the matching degree of result and raw diagnostic data；
The diagnosis keyword is in the conversion distance of medical semantic network：Keyword will each be diagnosed in medical semantic network
Be transformed into the path length that the corresponding diagnosis keyword of matching tree passes through and be denoted as a transformation ratio, calculate it is all match examine
The sum of transformation ratio natural logrithm of disconnected keyword, as diagnoses conversion distance of the keyword in medical semantic network；
The otherness of order of the keyword with diagnosing keyword in ICD-10 is diagnosed in the raw diagnostic data, is：Count first
Calculate same position of the diagnosis keyword in raw diagnostic data and in ICD-10 position sequence difference, then calculate all
Diagnose the sum of absolute value of sequence difference of keyword.
5. the classification of diseases coding method based on raw diagnostic data as claimed in claim 1, it is characterized in that,
The medical treatment semantic network is the semantic network of medical field, and the node body of medical semantic network is general for medical domain
Read, the medical domain concept node is connected with other medical domain concept nodes, each medical domain concept node but with
The disease concept form of expression node connection of itself；Each medical domain concept node also with property concept node, degree concept
Node, position concept node or the connection of body concept node；Relation between the node of the medical treatment semantic network is medical treatment neck
Relation between the concept of domain；
The medical field concept, including：The physiology region of anatomy, body tissue, component, disease exception, bacterial virus, pathology,
Disease property；
Relation between medical domain concept, including：Correlation, transforming relationship, correlation weight, transforming relationship weight and
Relation of the concept to specific manifestation；
Correlation between medical domain concept and concept, including：The including, is affiliated of concept, abstract or specific manifestation；
Transforming relationship between medical domain concept and concept, including：Concept it is close or identical.
6. the classification of diseases coding method based on raw diagnostic data as claimed in claim 1, it is characterized in that,
More than one for disease quantity in raw diagnostic data, diagnosis cutting result can be divided into two or more parts, often
A part includes a primary keyword and corresponding qualifier；During matching, various pieces scan for as the input of group
With tree；
Matching forest includes several matching trees, each matching tree, including：Tree root, trunk, branch and leaf；The matching tree
Tree root represent diagnosis concept, show as ICD coding；The trunk of the matching tree represents the performance diagnosis name of diagnosis concept；
The branch of the matching tree represents the concrete composition part of diagnosis concept performance diagnosis name；The leaf of the matching tree represents to examine
The qualifier and primary keyword of the concrete composition part of disconnected concept performance diagnosis name；
Match the forming process of forest：Single standard diagnostics are a concepts, and concept includes several forms of expression；Every kind of table
Existing form have the structure of oneself, the conceptual entity included and comprising each conceptual entity between correlation；Each standard
Diagnose represent concept, structure, the conceptual entity included and comprising each conceptual entity between correlation tree structure
To represent, matching tree is defined as, and the matching tree of all standard diagnostics forms matching forest, also, according to icd standard guide,
In forest is matched, there are priority and inclusion relation between matching tree；
The form of expression, including：Title；
Every kind of form of expression has the structure of oneself, including：Side by side, progressive explanation or cause and effect；
Every kind of form of expression has the conceptual entity included of oneself, including：Symptom, disease or operation；
Every kind of form of expression has the correlation between each conceptual entity included of oneself, including：Keyword and qualifier or
Limit modification；
The root of the matching tree represents diagnosis concept, its concept shows as ICD codings；
Described search matching tree processing, there is three kinds of situations,
The first is without as a result, the result of output is sky, then the reason for prompting is without matching result is original diagnostic information deficiency；
Second is to have as a result, the optimal result selected that finally sorts only has one, then using this optimal result as final
Matching result exports；
The third be have as a result, the optimal result selected have it is multiple, then output result for sky, and prompt the original without matching result
Because being to have multiple matching degrees identical as a result, and being exported multiple optimal results as a part for prompt message；Need weight
New input diagnoses and more detailed information is provided in original basis.
7. the classification of diseases coded system based on raw diagnostic data, it is characterized in that, including：Memory, processor and it is stored in
The computer instruction run on memory and on a processor, when the computer instruction is executed by processor, completes following step
Suddenly：
Step (1)：Receive input data：The input data includes：Raw diagnostic data；
Step (2)：The raw diagnostic data of input is pre-processed；
Step (3)：The pre-processed results obtained with step (2) are in GB/T 14396-2016《Classification of diseases and code》It is and international
Retrieved in disease criterion sorting code number ICD-10, judge whether to obtain as a result, if obtain as a result, if direct exports coding knot
Fruit；If it is not, enter step (4)；
Step (4)：Pretreated raw diagnostic data is carried out at word segmentation processing, association's conversion processing and search matching tree
Reason, then filters out optimal result from the handling result of matching tree, enters step (5)；
It is described to filter out optimal result from the handling result of matching tree, refer to：
Step (a1)：The keyword quantity on matching tree that will match to carries out descending arrangement and compares respectively, if ranked the first
It is identical with second or identical with what is ranked the first have it is multiple,
Compared with the keyword quantity split then is carried out ascending order arrangement with the keyword ratio of number of matching tree；If obtain
Rank the first it is identical with second or identical with what is ranked the first have multiple, then enter step (a2)；
Step (a2)：Qualifier and primary keyword are overlapped in the conversion distance of medical semantic network, to stack result into
The arrangement of row descending is compared, if rank the first it is identical with second or identical with what is ranked the first have multiple, enter step
Suddenly (a3)；
Step (a3)：The matching degree of matching tree is calculated, the matching degree of the matching tree is equal to the primary keyword that participle obtains
Quantity with match tree primary keyword ratio of number, to ratio of number carry out descending arrangement compare；If rank the first with
Second it is identical or identical with what is ranked the first have multiple, then terminate；
In step (a1)-step (a3), if optimal result only has one, that is, the result to rank the first only has one, then table
It is Optimum Matching tree to show current matching tree；
Step (5)：The coding accuracy of assessment result, exports coding result and accuracy evaluation result.
8. a kind of computer-readable recording medium, it is characterized in that, computer instruction is stored thereon with, the computer instruction is located
When managing device execution, following steps are completed：
Step (1)：Receive input data：The input data includes：Raw diagnostic data；
Step (2)：The raw diagnostic data of input is pre-processed；
Step (3)：The pre-processed results obtained with step (2) are in GB/T 14396-2016《Classification of diseases and code》It is and international
Retrieved in disease criterion sorting code number ICD-10, judge whether to obtain as a result, if obtain as a result, if direct exports coding knot
Fruit；If it is not, enter step (4)；
Step (4)：Pretreated raw diagnostic data is carried out at word segmentation processing, association's conversion processing and search matching tree
Reason, then filters out optimal result from the handling result of matching tree, enters step (5)；
It is described to filter out optimal result from the handling result of matching tree, refer to：
Step (a1)：The keyword quantity on matching tree that will match to carries out descending arrangement and compares respectively, if ranked the first
It is identical with second or identical with what is ranked the first have it is multiple,
Compared with the keyword quantity split then is carried out ascending order arrangement with the keyword ratio of number of matching tree；If obtain
Rank the first it is identical with second or identical with what is ranked the first have multiple, then enter step (a2)；
Step (a2)：Qualifier and primary keyword are overlapped in the conversion distance of medical semantic network, to stack result into
The arrangement of row descending is compared, if rank the first it is identical with second or identical with what is ranked the first have multiple, enter step
Suddenly (a3)；
Step (a3)：The matching degree of matching tree is calculated, the matching degree of the matching tree is equal to the primary keyword that participle obtains
Quantity with match tree primary keyword ratio of number, to ratio of number carry out descending arrangement compare；If rank the first with
Second it is identical or identical with what is ranked the first have multiple, then terminate；
In step (a1)-step (a3), if optimal result only has one, that is, the result to rank the first only has one, then table
It is Optimum Matching tree to show current matching tree；
Step (5)：The coding accuracy of assessment result, exports coding result and accuracy evaluation result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711013514.0A CN107577826B (en) | 2017-10-25 | 2017-10-25 | Classification of diseases coding method and system based on raw diagnostic data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711013514.0A CN107577826B (en) | 2017-10-25 | 2017-10-25 | Classification of diseases coding method and system based on raw diagnostic data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107577826A CN107577826A (en) | 2018-01-12 |
| CN107577826B true CN107577826B (en) | 2018-05-15 |
Family
ID=61038424
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711013514.0A Active CN107577826B (en) | 2017-10-25 | 2017-10-25 | Classification of diseases coding method and system based on raw diagnostic data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107577826B (en) |
Families Citing this family (35)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108182977A (en) * | 2018-02-05 | 2018-06-19 | 南方医科大学顺德医院(佛山市顺德区第人民医院) | Patient diagnosis coding method and system |
| CN108446260A (en) * | 2018-02-06 | 2018-08-24 | 天津艾登科技有限公司 | The method and system of automation disease code conversion are carried out based on semantic approximate match algorithm |
| CN108710636B (en) * | 2018-04-09 | 2024-02-23 | 平安科技（深圳）有限公司 | Medical record screening method, terminal equipment and computer readable storage medium |
| CN108766544A (en) * | 2018-05-29 | 2018-11-06 | 袁勇 | A kind of clinical criteria diagnostic message matching process by the payment of disease score value |
| CN108830352A (en) * | 2018-06-16 | 2018-11-16 | 深圳市前海安测信息技术有限公司 | Medical insurance interface is to code method and computer installation, readable storage medium storing program for executing |
| CN109448860B (en) * | 2018-09-10 | 2024-09-17 | 平安科技（深圳）有限公司 | Disease data mapping method, device, computer equipment and storage medium |
| CN109543718B (en) * | 2018-10-27 | 2024-04-09 | 平安医疗健康管理股份有限公司 | Method and device for modifying disease type description |
| CN109522688B (en) * | 2018-10-27 | 2023-10-13 | 平安医疗健康管理股份有限公司 | Renal failure qualification authentication method, equipment and server based on data processing |
| CN109545297A (en) * | 2018-10-30 | 2019-03-29 | 平安医疗健康管理股份有限公司 | A kind of disease coding method and calculating equipment based on big data |
| CN109785959A (en) * | 2018-12-14 | 2019-05-21 | 平安医疗健康管理股份有限公司 | A kind of disease code method and apparatus |
| CN109994215A (en) * | 2019-04-25 | 2019-07-09 | 清华大学 | Disease automatic coding system, method, device and storage medium |
| CN110321556A (en) * | 2019-06-14 | 2019-10-11 | 中山职业技术学院 | A kind of method and its system of doctor's diagnosis and treatment medical insurance control expense intelligent recommendation scheme |
| CN110660459B (en) * | 2019-08-30 | 2024-07-09 | 腾讯科技（深圳）有限公司 | Method, device, server and storage medium for medical records quality control |
| CN112687385B (en) * | 2019-10-18 | 2024-07-16 | 医渡云（北京）技术有限公司 | Disease stage identification method and device |
| CN111191415A (en) * | 2019-12-16 | 2020-05-22 | 山东众阳健康科技集团有限公司 | Operation classification coding method based on original operation data |
| CN111241806B (en) * | 2019-12-23 | 2020-11-24 | 望海康信(北京)科技股份公司 | Method and system for checking consistency of diagnosis of electronic medical record and medical record home page |
| CN111161817B (en) * | 2019-12-31 | 2023-09-19 | 医渡云（北京）技术有限公司 | Medical data standardized processing method, device, medium and electronic equipment |
| CN111209387B (en) * | 2019-12-31 | 2022-02-18 | 上海亿锎智能科技有限公司 | Retrieval analysis method and system based on MedDRA |
| CN111444333B (en) * | 2020-04-25 | 2023-08-11 | 上海健交科技服务有限责任公司 | Coding mapping method for insurance medicine and clinical medicine |
| CN111666754B (en) * | 2020-05-28 | 2023-02-03 | 深圳平安医疗健康科技服务有限公司 | Entity identification method and system based on electronic disease text and computer equipment |
| CN112735544B (en) * | 2020-12-30 | 2024-07-23 | 杭州依图医疗技术有限公司 | Medical record data processing method, device and storage medium |
| CN112700825B (en) * | 2020-12-30 | 2024-03-05 | 杭州依图医疗技术有限公司 | Medical data processing method, device and storage medium |
| CN112560400B (en) * | 2020-12-30 | 2024-06-14 | 杭州依图医疗技术有限公司 | Medical data processing method, device and storage medium |
| CN112837765B (en) * | 2021-01-12 | 2023-01-20 | 山东众阳健康科技集团有限公司 | Automatic coding method and system for disease diagnosis |
| CN112836006B (en) * | 2021-01-12 | 2022-09-23 | 山东众阳健康科技集团有限公司 | Multi-diagnostic intelligent coding method, system, medium and equipment |
| CN112861535B (en) * | 2021-01-18 | 2023-11-14 | 众阳健康科技集团有限公司 | Surgical classification coding method and system based on diagnosis and treatment data |
| CN112836512B (en) * | 2021-01-27 | 2022-07-19 | 山东众阳健康科技集团有限公司 | ICD-11 coding retrieval method based on natural semantic processing and knowledge graph |
| CN112818079B (en) * | 2021-02-05 | 2024-07-30 | 武汉大学 | Medical keyword warehouse-in and diagnosis method and storage medium |
| CN113593709B (en) * | 2021-07-30 | 2022-09-30 | 江先汉 | Disease coding method, system, readable storage medium and device |
| CN113936787A (en) * | 2021-09-17 | 2022-01-14 | 山东健康医疗大数据有限公司 | Method and tool for realizing hospital diagnosis service data splitting and correcting |
| CN115017868A (en) * | 2022-05-30 | 2022-09-06 | 中电药明数据科技(成都)有限公司 | Method and device for standardizing clinical diagnostic data |
| CN114822865B (en) * | 2022-06-27 | 2022-11-11 | 天津幸福生命科技有限公司 | Diagnostic data identification method and device, electronic equipment and storage medium |
| CN116166698B (en) * | 2023-01-12 | 2023-09-01 | 之江实验室 | Method and system for quickly constructing queues based on general medical terms |
| CN116206749B (en) * | 2023-01-18 | 2025-03-21 | 首都医科大学附属北京儿童医院 | Disease coding conversion method and system |
| CN117708268A (en) * | 2023-11-01 | 2024-03-15 | 中国人民解放军总医院第四医学中心 | Method and device for automatic matching of diagnosis names and codes in real-world data platform |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105069123A (en) * | 2015-08-13 | 2015-11-18 | 易保互联医疗信息科技（北京）有限公司 | Automatic coding method and system for Chinese surgical operation information |
| CN105069124A (en) * | 2015-08-13 | 2015-11-18 | 易保互联医疗信息科技（北京）有限公司 | Automatic ICD (International Classification of Diseases) coding method and system |
| CN106844308A (en) * | 2017-01-20 | 2017-06-13 | 天津艾登科技有限公司 | A kind of use semantics recognition carries out the method for automating disease code conversion |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080288292A1 (en) * | 2007-05-15 | 2008-11-20 | Siemens Medical Solutions Usa, Inc. | System and Method for Large Scale Code Classification for Medical Patient Records |
| CN104156415B (en) * | 2014-07-31 | 2017-04-12 | 沈阳锐易特软件技术有限公司 | Mapping processing system and method for solving problem of standard code control of medical data |
| CN105468743B (en) * | 2015-11-25 | 2018-12-28 | 钟岑 | A kind of intelligent diagnostics Operation encoding search method |
-
2017
- 2017-10-25 CN CN201711013514.0A patent/CN107577826B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105069123A (en) * | 2015-08-13 | 2015-11-18 | 易保互联医疗信息科技（北京）有限公司 | Automatic coding method and system for Chinese surgical operation information |
| CN105069124A (en) * | 2015-08-13 | 2015-11-18 | 易保互联医疗信息科技（北京）有限公司 | Automatic ICD (International Classification of Diseases) coding method and system |
| CN106844308A (en) * | 2017-01-20 | 2017-06-13 | 天津艾登科技有限公司 | A kind of use semantics recognition carries out the method for automating disease code conversion |
Non-Patent Citations (1)
| Title |
|---|
| 基于文本分析的自动化疾病编码方法;鲍庆升 等;《计算机系统应用》;20151231;第24卷(第12期);第265-268页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107577826A (en) | 2018-01-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107577826B (en) | Classification of diseases coding method and system based on raw diagnostic data | |
| CN107705839A (en) | Disease automatic coding and system | |
| CN107731269A (en) | Disease code method and system based on raw diagnostic data and patient file data | |
| CN111414393B (en) | Semantic similar case retrieval method and equipment based on medical knowledge graph | |
| CN113241135B (en) | Disease risk prediction method and system based on multi-modal fusion | |
| US11200379B2 (en) | Ontologically driven procedure coding | |
| CN106202955B (en) | Diagnosis associated packets method and system based on intellectual coded adaptation | |
| CN108831559A (en) | A Chinese electronic medical record text analysis method and system | |
| CN110337645A (en) | The processing component that can be adapted to | |
| CN112151188A (en) | Intelligent disease prediction system based on medical knowledge map | |
| CN110364234A (en) | Electronic medical records intelligent storage analysis retrieval system and method | |
| CN108182972A (en) | The intelligent coding method and system of Chinese medical diagnosis on disease based on participle network | |
| CN110085308B (en) | Diagnosis and treatment department classification method based on fusion deep learning | |
| CN117316466B (en) | Clinical decision method, system and equipment based on knowledge graph and natural language processing technology | |
| CN110321556A (en) | A kind of method and its system of doctor's diagnosis and treatment medical insurance control expense intelligent recommendation scheme | |
| CN111477320B (en) | Treatment effect prediction model construction system, treatment effect prediction system and terminal | |
| CN112466462B (en) | An EMR Information Association and Evolution Method Based on Graph Deep Learning | |
| CN117787282A (en) | Doctor-patient text intelligent extraction method based on large language model | |
| CN109858550B (en) | Machine identification method for potential process failure mode | |
| CN116956869A (en) | Text normalization method, device, electronic equipment and storage medium | |
| CN111192667A (en) | Method for prompting operation code based on intelligent operation | |
| CN110060749B (en) | Intelligent diagnosis method of electronic medical record based on SEV-SDG-CNN | |
| Chen et al. | Semantic analysis of free text and its application on automatically assigning ICD-9-CM codes to patient records | |
| Jiang et al. | Fine-tuning BERT-based models for plant health bulletin classification | |
| CN116259422A (en) | Method, system, medium and equipment for generating ophthalmic disease diagnosis and treatment opinions based on virtual data enhancement |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP03 | Change of name, title or address |
Address after: 250101 12 / F, building 1, Aosheng building, 1166 Xinluo street, hi tech Zone, Jinan City, Shandong Province Patentee after: SHANDONG MSUNHEALTH TECHNOLOGY GROUP Co.,Ltd. Address before: Xinluo Avenue high tech Zone of Ji'nan City, Shandong province 250000 No. 1166 orsus No. 1 building, 12 floor Patentee before: SHANDONG MSUNSOFT Co.,Ltd. |
|
| CP03 | Change of name, title or address | ||
| CP01 | Change in the name or title of a patent holder |
Address after: 250101 Floor 12, Building 1, Aosheng Building, No. 1166, Xinluo Street, High tech Zone, Jinan, Shandong Patentee after: Zhongyang Health Technology Group Co.,Ltd. Address before: 250101 Floor 12, Building 1, Aosheng Building, No. 1166, Xinluo Street, High tech Zone, Jinan, Shandong Patentee before: SHANDONG MSUNHEALTH TECHNOLOGY GROUP Co.,Ltd. |
|
| CP01 | Change in the name or title of a patent holder |