CN102799794B - The self-service evaluating system of life entity physiological situation - Google Patents
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Abstract
The invention discloses the self-service evaluating system of a kind of life entity physiological situation, solve the prevention and control for novel transmissible disease in prior art, the problem that the suspected case that cannot realize full region unified standard differentiates.This invention comprises more than one user terminal apparatus and remote control center; This user terminal apparatus comprises data processing and control module, the first input block be all connected with data processing and control module, the first output unit, the first transmission unit, disbursement and sattlement unit, data detecting unit and user identification unit; Described first transmission unit is connected with remote control center.Present invention also offers the appraisal procedure of said system.The suspected case discrimination standard that the present invention can provide full region unified for 24 hours, when epidemic situation occurs, can realize suspected case rapid screening, reduces epidemic situation spread risk; And can instruct that user is self-service to complete medical treatment and detect and assessment, understand physical condition at any time, carry out prevention from suffering from the diseases and treatment, reduce and to seek medical advice cost.
Description
Technical field
The present invention relates to a kind of towards medical treatment & health field, community in urban areas, be specifically related to the self-service evaluating system of a kind of life entity physiological situation.
Background technology
In the society of high-tech, informationization, market economy fast development, because psychological pressure is overweight, work rhythm is accelerated and irregular life style, bring increasing sub-health state, it have impact on work efficiency and the quality of life of people, also reduce immunity of human body itself, ability of regulation and control and the adaptive faculty to physical environment and social environment, become the latency of various chronic disease and difficult disease.In order to disease preventing and treating effectively, build up health, first, constantly will strengthen the self health consciousness of people.Wish to research and develop a kind of quick, real-time, reliable health and fitness information detector, the physiologic information of body can be tested at any time as clock and watch give the correct time, correct the biased of physiological function in time, make disease early detection, diagnosis and treatment in early days, preventing and treating in not suffering from; Meanwhile, the Crack cause of sub-health state can be made a concrete analysis of by information, and the feature of different disease type, seek effective medical treatment method, make drug therapy, health nutrient and keep fit method more rationally, effectively.
Sub-health state is in the rim condition between health and disease, often the prelude of disease.Be mainly manifested in psychology and physiological function go down or lack of proper care.The acid of normal appearance tired limb, vexed anxiety or notice can not be concentrated, insomnia and dreamful sleep, and palpitaition is uncomfortable in chest, failure of memory, easy to catch cold, and hidrosis is warmed, poor appetite, the symptoms such as hypogona dism.And existing Medical Instruments and experimental index, not yet can detect and make a definite diagnosis, mainly analyzed by way of questionnaires so far, or use extended pattern health check-up (standard health check-up adds psychology, physical examinations), the methods such as luxury health check-up (upper item adds thyroxine, sex hormone, Hemorheology, heart super color, bone density, exercise stress, cardio-pulmonary function) discharge disease, bring very large traumatic pain to patient like this, further increase their psychological burden and financial burden.
But for most disease, people only just can remove examination in hospital when Symptoms is obvious, often because stall for time long, cause losing the optimal treatment stage, patient can only be born for a long time and be tortured with a disease, even cannot treat.
And because existing medical resource is relatively nervous, cost of seeking medical advice is high, regional healthcare level difference is comparatively large, a lot of disease can not realize prevention and therapy in advance.Simultaneously current medical system, for the prevention and control of novel transmissible disease, the suspected case that cannot realize full region unified standard differentiates.
Summary of the invention
The object of the present invention is to provide the self-service evaluating system of a kind of life entity physiological situation, solve the prevention and control for novel transmissible disease in prior art, the problem that the suspected case that cannot realize full region unified standard differentiates; Solve the problem that existing medical system cannot provide self-medicine to serve for patient simultaneously.
To achieve these goals, the technical solution used in the present invention is as follows:
The self-service evaluating system of life entity physiological situation, comprises more than one user terminal apparatus and remote control center; This user terminal apparatus comprises data processing and control module, the first input block be all connected with data processing and control module, the first output unit, the first transmission unit, disbursement and sattlement unit, data detecting unit and user identification unit; Described first transmission unit is connected with remote control center.
Further, described remote control center is primarily of information processing and controlling unit, the remote control center of the second transmission unit be all connected with information processing and controlling unit, the second input block, the second output unit, data operation server, administration authority judgement unit, described second transmission unit is connected with the first transmission unit.
Further, described data processing and control module are also connected with the first storage unit and wireless transmission unit; Described information processing and controlling unit is also connected with the second storage unit and expense collection unit.
The determination methods of the self-service evaluating system of life entity physiological situation, comprises the following steps:
A () judges whether user is new user, is then register; No, then directly log in, and identify userspersonal information P [s];
B () is selected the type of assessment and is judged whether the model of the type upgrades, no, then Renewal model parameter; Then carry out the formulation of test item;
C whether () detecting instrument is normal, no, then send instrument damage alarm; Then carry out next step;
D () adopts disbursement and sattlement unit to carry out disbursement and sattlement;
E () judges whether user puts in place, no, then judge timeout conditions; Be then carry out physiological situation detection, obtain physiological situation and detect data T [N];
F () judges that whether data are errorless, no, then send error in data alarm, and return step (e); Be then carry out physiologic status assessment or suspected case anticipation, and export corresponding case Suspected Degree data Em, assessment result, the detailed description state of an illness, health warning information and suggestion of seeking medical advice;
G () preserves various data, display assessment result and suggestion of seeking medical advice;
H () judges whether to carry out other assessments, be then return step (b); No, then terminate this assessment, and the way of output that detection data, assessment result, the information such as guide of seeking medical advice are selected according to user is exported, then various data are transferred to remote control center;
I the data received arrange by () remote control center after, unified preservation.
Further, judges in described step (e) timeout conditions concrete steps as: judge whether time-out, be, then carry out time-out reset, return step (a); No, then reminding user puts in place as early as possible, returns step (e).
Again further, the concrete steps that in described step (f), whether differentiation data are errorless are:
(1) to physical detection data bag T [N], first according to checking algorithm, check bit calculating is carried out to valid data position in the packet received, then contrast with the check bit data in packet, differentiate that whether the check bit calculating acquisition is consistent with the check bit numerical value in packet, be, then data transmission is errorless, enters discriminating data step (2); No, then data transmission is wrong, sends error in data alarm, again reads instrument detection data;
(2) each number of significant digit certificate in physical detection data bag T [N] is read, the i.e. every physical detection data of user, then the maximum value limit and the minimum value limit that detect data and whether exceed respective items object human detection data is differentiated in packet, be, then Data Detection is wrong, send error in data alarm, return step (e) and re-start error items Data Detection; No, then data are errorless, continue physiologic status assessment or suspected case anticipation in step (f).
Further, in described step (f), physiologic status assessment or suspected case anticipation concrete steps are:
(1) system m item assessment request is accepted;
(2) physical detection data Tm [N] and the personal information data Pm [S] of m item assessment request user is read;
(3) detection data Tm [N] is normalized, digitized processing is carried out to userspersonal information P [S], and data are packaged as assessment models input data layout Bm [N+S];
(4) read m item and assess corresponding assessment models LMBP (Bm [N+S]), assess, export assessment result Em;
(5) differentiate whether Em is less than smallest evaluation threshold epsilon m, is then Em zero setting; No, then carry out next step;
(6) Em is greater than to the assessment request of ill discrimination threshold, exports assessment result, and export detailed state of an illness description and suggestion of seeking medical advice according to Em value size.
In addition, described life entity physiologic status assessment and suspected case discrimination model are multiple discrimination model for different evaluation requirement; The different assessment models differentiating type employing different structure, for often kind of typical disease all sets up an assessment models, improves discrimination precision with this, reduces model complexity, improves model and sets up and identification effect; For inferior health evaluation requirement, then choose multiple typical disease discrimination model, assess respectively, obtain multiple assessment result, thus analyze the risk of sub-health status and various disease.
Described life entity physiologic status assessment and the appraisal procedure of suspected case discrimination model are detect data Tm [N], userspersonal information Pm [S] for raw data with the Vital status of case, first to data normalization and digitizing, then assessment data Bm [N+S] is packaged as, finally choose corresponding assessment models LM-BP (Bm [N+S]) to assess, export case Suspected Degree Em; Wherein 0≤Em≤1, numerical value is larger, represents that the doubtful degree of case is higher, the state of an illness is heavier; System exports the detailed state of an illness according to Em numerical value and describes and suggestion of seeking medical advice.
In user terminal apparatus, described data processing and control module, process for the information measured physiological status, differentiate, classify; Data detecting unit, comprises the every physiological status surveying instrument of life entity, for obtaining every key message of user's physiological status, as: blood pressure, blood sugar, blood fat, routine blood test, cardiogram, heart rate, eyesight, hearing, body weight, body temperature, measurements of the chest, waist and hips etc.; First input block, carries out information input, monitoring video information input etc. for user; First output unit, for the display, data-printing, data output copy etc. of data, facilitates user to understand oneself present physiological situation; First transmission unit, after receiving information transmission request, transmits data by control program requirement, facilitates information transmission to remote control center; User identification unit, for identifying the identity of different users, sets different rights of using to different users, distinguishes physiological status metrical information and the result of different users; Disbursement and sattlement unit, for user's cash and clearing of paying of swiping the card; First storage unit, stores for carrying out difference to the information of physiological status; Wireless transmission unit, for detecting data, assessment result, the information transmission such as suggestion of seeking medical advice to the individual digital equipment of user.
In remote control center, described information processing and controlling unit, stores for the data such as detection data, physiological situation assessment result, the pre-judged result of suspected case obtaining all life informations, process and display device obtains, gathers; Second transmission unit, for the various information transmission between remote control center and user terminal apparatus; Second input block, carries out information input, monitoring video information input etc. for supvr; Second output unit, for the display, data-printing, data output copy etc. of data; Data operation server, for the treatment of system data; Administration authority judgement unit, for distinguishing different system supvr identity, determines administration authority; Second storage unit, for storing the physiology situation information after the test of each user terminal; Expense collection unit, for the statistics of business condition, understands the frequency of utilization of each user terminal apparatus.
The design of life entity physiologic status assessment and suspected case discrimination model mainly comprises the following steps:
(1) obtain confirmed cases data sample in the past, each data sample all comprises the information of two aspects: (1a) every physiologic information detects data, as body temperature, blood pressure, blood sugar, body weight, electrocardio, body fluid testing result etc.; (1b) whether often drunk etc. individual subscriber situation descriptor, as sex, the age, occupation, ill history, whether smoking.Wherein (1a) is as major parameter, and (1b) is as auxiliary parameter.
(2) sample data collected is processed.First data are quantized, to identify in a computer.Physiologic information detects data all with international standards of medical education unit record, and userspersonal information's situation is recorded as: (2a) sex: the male sex is designated as 1, and women is designated as 0; (2b) age presses real age record; (2c) occupation Arabian mathematics 1 ~ 10 record; (2d) ill history: be designated as 1, without being designated as 0; (2e) whether smoking: be designated as 1, is noly designated as 0; (2f) whether often drunk: to be designated as 1, be noly designated as 0. to these data vectors, successively as an element input of one group of data.Often organize data in the data file so obtained and can be divided into 14 fields, the 1st field is case numbering; 2nd field represents confirmed result, and 0 is sub-health state, and other is default corresponding typical disease numbering; 3rd ~ 8 fields represent that every physiologic information detects data; 9th ~ 14 represent personal information data.
(3) from sample database, choose typical data sample N number of, extract M (M<N) individual sample wherein immediately as training sample, remaining all kinds of (N-M) individual sample is as testing authentication data.
(4) according to case data characteristics, the assessment models of corresponding construction is set up.
(5) model training, the process of (5a) netinit; (5b) input M sample data successively to train designed assessment models, calculate each parameter of model; (5c) the number of samples m learnt is recorded.If m < is M, then goes to step (2) and continue to calculate, if m=M, then terminate training; (5d) according to modified weight formula correction model parameter, export according to new model parameter calculation, if model fails to meet the requirements of precision index or m < M, then perform step (5b) and continue training, otherwise terminate training.
(6) assessment models testing authentication, after assessment models is by training, organizes test sample book data input network, just can obtain corresponding output by (N-M).
(7) interpretation of result, the Output rusults predicted by comparison model is analyzed with the result originally made a definite diagnosis, and can obtain misdiagnosis rate, differentiates whether misdiagnosis rate meets the demands, and is then complete modeling; No, then return step (4), according to model structure correction formula amendment model structure, re-establish model, re-training.
The present invention compared with prior art, has the following advantages and beneficial effect:
(1) the present invention is provided with remote control center, can remote update, interpolation, deletion, each terminal of restructuring normally work normal physiological condition required with reference to detect data, suspected case physiological situation with reference to detecting data, case differentiate in indices weight parameter; Complex data computational service can be provided for each end device simultaneously; The data such as user's physiological situation testing result, physiological situation assessment result, suspected case testing result obtained in normal work for each terminal store, and facilitate terminal to access at any time; And according to the reported data of each terminal, statistics can be generated, to particular propagation disease, suspected case distribution situation can be understood at any time;
(2) the present invention's suspected case discrimination standard that full region can be provided unified, when epidemic situation occurs, can realize suspected case rapid screening, reduces epidemic situation spread risk; And user can be instructed to carry out prevention from suffering from the diseases and treatment in time, reduce cost of seeking medical advice;
(3) life entity physiological situation assessment of the present invention and suspected case decision method, can ask according to user, carry out the physiological situation detection of specific project, the suspected case anticipation of other health Evaluation of specific section, specified disease;
(4) present invention employs multiple assessment models structure, each suspected case can be realized and train respectively, differentiate respectively, the problem that when avoiding multiple-case simultaneously anticipation, network complexity is high, anticipation precision is low;
(5) test function of the present invention is comprehensive, and test operation is convenient, and test event is more, can down loading updating;
(6) the present invention can differentiate the physiology situation information of the specific life entity of acquired sign, obtains life entity physiologic status assessment result, and can carry out the suspected case anticipation of typical disease according to physiology situation information;
(7) the present invention directly can obtain the life entity physiological state information of measurement mechanism, also the life entity physiological state information stored in read/write memory medium, also can accept the numerical information of sign life entity physiological state information of user's input and the descriptive fuzzy physiological state information of spoken and written languages simultaneously, the fuzzy physiological state information descriptive to spoken and written languages can process, extract key message;
(8) the present invention can provide self-medicine service in 24 hours, can instruct that user is self-service to complete medical treatment and detect and health Evaluation, helps user to understand self health status at any time, carries out prevention from suffering from the diseases and treatment, reduce and to seek medical advice cost.
Accompanying drawing explanation
Fig. 1 is the structured flowchart of user terminal apparatus in the present invention.
Fig. 2 is structured flowchart of the present invention.
Fig. 3 is the process flow diagram of user terminal apparatus data processing in the present invention.
Fig. 4 is the process flow diagram of medium-long range control center of the present invention data processing.
Fig. 5 is the assessment of life entity physiological situation and suspected case anticipation process flow diagram in the present invention.
Fig. 6 is the assessment of life entity physiological situation and suspected case anticipation model modeling process flow diagram in the present invention.
Fig. 7 is neural network assessment models structural representation in the present invention.
Fig. 8 is the change curve of natural logarithm value with frequency of training of neural network assessment models error in the present invention.
Embodiment
Below in conjunction with drawings and Examples, the invention will be further described, and embodiments of the present invention include but not limited to the following example.
Embodiment
As shown in Figure 1, 2, the self-service evaluating system of life entity physiological situation, comprises more than one user terminal apparatus and remote control center; This user terminal apparatus comprises data processing and control module, the first input block be all connected with data processing and control module, the first output unit, the first transmission unit, data detecting unit, the first storage unit and user identification unit; Described first transmission unit is connected with remote control center.
Above-mentioned data processing and control module, process for the information measured physiological status, differentiate, classify; As: ARM, FPGA, DSP, ADC chip, DAC chip etc.
First input block, carries out information input, monitoring video information input etc. for user; As: touch-screen, keyboard, mouse, camera, voice etc.
First output unit, for the display, data-printing, data output copy etc. of data; As: display screen, sound equipment, data file, printer etc.
First transmission unit, after receiving information transmission request, transmits data by control program requirement; As: internet, wireless communication networks, fiber optic network etc.
Data detecting unit, comprises the every physiological status surveying instrument of life entity, for obtaining every key message of user's physiological status, as: blood pressure, blood sugar, blood fat, routine blood test, cardiogram, heart rate, eyesight, hearing, body weight, body temperature, measurements of the chest, waist and hips etc.; Surveying instrument is as sphygmomanometer, blood glucose meter, cardiotach ometer, blood oxygen saturation detector, routine blood test detector, chair type weighing scale, clinical thermometer, height tester, electronic grip meter, bone mineral density detector, body fluid detector etc.
User identification unit, for identifying the identity of different users, sets different rights of using to different users, distinguishes physiological status metrical information and the result of different users; As: ID (identity number) card information, user name and password, fingerprint recognition etc.
Disbursement and sattlement unit, for user's cash and clearing of paying of swiping the card; Automatic machine, POS, mobile payment etc.
First storage unit, stores for carrying out difference to discrimination model parameter, user terminal working procedure, userspersonal information, user's physiological state information etc.; As: PC hard disk, mobile memory etc.
Wireless transmission unit, for will data, assessment result, the information transmission such as suggestion of seeking medical advice be detected to the individual digital equipment of user, as: wireless transmitter module.
Described remote control center comprises information processing and controlling unit, the second transmission unit be all connected with information processing and controlling unit, the second input block, the second output unit, data operation server, expense collection unit, administration authority judgement unit and the second storage unit form, and described second transmission unit is connected with the first transmission unit.
Information processing and controlling unit, for the ruuning situation to each user terminal, various assessment models sets up and update status, running situation control and supervise, and obtains simultaneously, data such as detection data, physiological situation assessment result, the pre-judged result of suspected case that process and display device obtain store, gather to life information; As: ARM, FPGA, DSP etc.
Second transmission unit, for the various information transmission between remote control center and user terminal apparatus; As: internet, wireless communication networks, fiber optic network etc. and accordingly transmission server and switch.
Second input block, carries out information input, monitoring video information input etc. for supvr; As: touch-screen, keyboard, mouse, voice, monitoring camera are first-class.
Second output unit, for the display, data-printing, data output copy etc. of data; As: display screen, sound equipment, audible and visual alarm, data file, printer etc.
Data operation server, for the treatment of system data; As: large server.
Administration authority judgement unit, for distinguishing different system supvr identity, determines administration authority; As: ID (identity number) card information, user name and password, fingerprint recognition etc.
Second storage unit, for storing the physiology situation information after the test of each user terminal, as: large data memory set.
Expense collection unit, for the statistics of business condition, understands the frequency of utilization of each user terminal apparatus.
As shown in Figure 3, the determination methods of the self-service evaluating system of life entity physiological situation be made up of said apparatus, comprises the following steps:
A () is confirmed user by user identification unit, if new user, then need to be undertaken registering logging in by the first input block again; If old user, then can directly log in, the personal information P [s] of user identification unit Direct Recognition user;
Whether b () user selects oneself to need the type of assessment by the first input block, and be up-to-date by the model of data processing and control module determination the type, if not, then Renewal model data; If up-to-date, then directly can formulate test item;
C () user is paid by disbursement and sattlement unit;
D () data processing and control module detect detecting instrument, if there is problem, then can send instrument damage alarm; If detecting instrument is normal, then carry out next step;
E () data processing and control module judge whether user puts in place, if do not arrive detecting instrument place, then judge timeout conditions; If put in place, then carry out detecting corresponding physiological data and obtain physiological situation and detect data T [N];
F () data processing and control module judge that whether the data detected are errorless thereupon, if wrong, will send error in data alarm, and return step (e); If errorless, then carry out physiologic status assessment or suspected case anticipation, and draw suspected case physiological characteristic weight data α [N];
(g) first storage unit each data are preserved, and by output unit display assessment result and suggestion of seeking medical advice;
H () judges whether to carry out other assessments, be then return step (b); No, then terminate this assessment, and by the first transmission unit, various data are transferred to remote control center;
I () remote control center receives the data after the assessment of each user terminal apparatus by the second transmission unit, and after data being arranged, unification is saved in the second storage unit.Finally can select according to user, whether detection data and assessment result are sent to individual subscriber digital device by wireless transmission unit.
Judges in described step (e) timeout conditions concrete steps as: judge whether time-out, be, then carry out time-out reset, return step (a); No, then reminding user puts in place as early as possible, returns step (e).
And physiologic status assessment or suspected case anticipation concrete steps are in described step (f):
(1) system m item assessment request is accepted;
(2) physical detection data Tm [N] and the personal information data Pm [S] of m item assessment request user is read;
(3) detection data Tm [N] is normalized, digitized processing is carried out to userspersonal information P [S], and data are packaged as assessment models input data layout Bm [N+S];
(4) read m item and assess corresponding assessment models LMBP (Bm [N+S]), assess, export assessment result Em;
(5) differentiate whether Em is less than smallest evaluation threshold epsilon m, is then Em zero setting; No, then carry out next step;
(6) Em is greater than to the assessment request of ill discrimination threshold, exports assessment result, and export detailed state of an illness description and suggestion of seeking medical advice according to Em value size.
As shown in Figure 4, the flow process that remote control center is safeguarded user terminal apparatus, specific as follows:
(1) judge whether keeper is new management person, is then register; No, then directly carry out logging in and authority differentiation;
(2) look into see if there is critical alarm notice, be then carry out critical alarm process; No, then carry out the selection of system maintenance type;
(3) judge whether keeper has permission, no, then reselect system maintenance type; Then carry out system status information reading;
(4) judge to safeguard whether item locks, and is, then reselect system maintenance type; No, then send to each user terminal apparatus and safeguard notice, start and safeguard countdown;
(5) edit, submit maintenance content to;
(6) judge to safeguard whether countdown terminates, no, then again judge to safeguard whether countdown terminates; Then send to each user terminal apparatus and safeguard notice;
(7) updating maintenance content is uploaded to each user terminal apparatus;
(8) judge whether each user terminal apparatus completes renewal, no, be then confirmed whether to upgrade time-out; That then service data preserved in updating maintenance record;
(9) cancel each user terminal apparatus and safeguard notice;
(10) being confirmed whether that carrying out other safeguards, is then return step (2) and reselect system maintenance type; No, then terminate this system maintenance.
Judge whether in described step (8) that the concrete steps upgrading time-out are: judge whether to upgrade time-out, be, then send and safeguard failed alarm notification, and return step (5); No, then return step (7).
As shown in Figure 5,6, in the present embodiment, life physiologic status assessment and suspected case discrimination model are a kind of based on LM-BP neural network model, and the design procedure of this model is:
(1) obtain confirmed cases data sample in the past, each data sample all comprises the information of two aspects: (1a) every physiologic information detects data, as body temperature, blood pressure, blood sugar, body weight, electrocardio, body fluid testing result etc.; (1b) whether often drunk etc. individual subscriber situation descriptor, as sex, the age, occupation, ill history, whether smoking.Wherein (1a) is as major parameter, and (1b) is as auxiliary parameter.
(2) sample data collected is processed.First data are quantized, to identify in a computer.Physiologic information detects data all with international standards of medical education unit record, and userspersonal information's situation is recorded as: (2a) sex: the male sex is designated as 1, and women is designated as 0; (2b) age presses real age record; (2c) occupation Arabian mathematics 1 ~ 10 record; (2d) ill history: be designated as 1, without being designated as 0; (2e) whether smoking: be designated as 1, is noly designated as 0; (2f) whether often drunk: to be designated as 1, be noly designated as 0. to these data vectors, successively as an element input of one group of data.Often organize data in the data file so obtained and can be divided into 14 fields, the 1st field is case numbering; 2nd field represents confirmed result, and 0 is sub-health state, and other is default corresponding typical disease numbering; 3rd ~ 8 fields represent that every physiologic information detects data; 9th ~ 14 represent personal information data.
(3) from sample database, choose typical data sample N number of, extract M (M<N) individual sample wherein immediately as training sample, remaining all kinds of (N-M) individual sample is as testing authentication data.
(4) according to case data characteristics, the neural network of corresponding construction is set up.
(5) neural metwork training step is the process of (5a) netinit; (5b) input M sample data successively to train designed neural network, calculate each parameter of model; (5c) the number of samples m learnt is recorded.If m < is M, then goes to step (2) and continue to calculate, if m=M, then terminate training; (5d) according to modified weight formula correction model parameter, export according to new model parameter calculation, if model fails to meet the requirements of precision index or m < M, then perform step (5b) and continue training, otherwise terminate training.
(6) neural network testing authentication, after neural network is by training, organizes test sample book data input network, just can obtain corresponding output by (N-M).
(7) interpretation of result, the Output rusults predicted by comparing cell is analyzed with the result originally made a definite diagnosis, and can obtain misdiagnosis rate, differentiates whether misdiagnosis rate meets the demands, and is then complete modeling; No, then return step (4), according to model structure correction formula amendment model structure, re-establish model, re-training.
When life entity physiologic status assessment and suspected case discrimination model receive assessment request, first evaluation type is confirmed, as: m item, then read m item and detect corresponding detection data Tm [N] and userspersonal information Pm [S], and data are normalized and digitized processing, data are packaged as assessment data Bm [N+S], then choose m item and assess corresponding neural network model LM-BP (Bm [N+S]) and assess, export case Suspected Degree Em; Wherein 0≤Em≤1, numerical value is larger, represents that the doubtful degree of case is higher, the state of an illness is heavier; System can export the detailed state of an illness according to Em numerical value and describe and suggestion of seeking medical advice.
In the present embodiment, choose 100 cases as data sample, comprising the health check-up data of coronary heart disease confirmed cases, rheumatic heart disease confirmed cases, hypertension confirmed cases, healthy personnel (personnel of three kinds of equal nothings of disease).Information in case data comprise the length of smoking, day drinking amount, whether whether whether hypertension history, the ill duration of rheumatism (year), blood pressure (high pressure), blood pressure (low pressure), heart rate, WBC, RBC, palpitaition duration uncomfortable in chest (year), pectoralgia perspire, oedema or edema, these 13 information of headache and dizzy, also comprises the confirmed result of whether coronary heart disease, whether hypertension, whether rheumatic heart disease.
The BP neural network of three-decker is adopted in the present embodiment, namely input layer, hidden layer, an output layer is comprised, according to data sample determination input layer number be 13, output layer nodes is 3, is 13 according to training result determination the number of hidden nodes, network structure signal is as shown in Figure 7.The natural logarithm value of model error value with frequency of training change curve as shown in Figure 8.
From 100 case data, random selecting 60 case data samples are to model training, 60 case data make a definite diagnosis result as table 1 (wherein 1 represent ill, 0 represent not ill):
Table 1
| Case sequence number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |
| Coronary heart disease | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Rheumatic heart disease | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Hypertension | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 |
| Case sequence number | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 |
| Coronary heart disease | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Rheumatic heart disease | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Hypertension | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| Case sequence number | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 |
| Coronary heart disease | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Rheumatic heart disease | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 |
| Hypertension | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 |
| Case sequence number | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 |
| Coronary heart disease | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Rheumatic heart disease | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Hypertension | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
After completing with the neural network model of the case data training in table 1, disease carries out network detection with this model again to above-mentioned 60 cases, and the result of determination of last network is as table 2:
Table 2
| Case sequence number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| Coronary heart disease | 0.999 | 1.003 | 1.005 | 1.005 | 1.002 | 0.998 | 1.000 | 0.994 | 1.000 | 1.000 |
| Rheumatic heart disease | 0.002 | -0.001 | 0.003 | -0.008 | 0.000 | 0.000 | 0.000 | 0.000 | 0.001 | 0.007 |
| Hypertension | 0.999 | -0.004 | 0.009 | 0.003 | 1.000 | 1.000 | 0.004 | 0.006 | 0.999 | -0.001 |
| Case sequence number | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 |
| Coronary heart disease | 1.000 | 1.003 | 0.998 | 1.001 | 1.000 | 0.999 | 1.013 | 0.988 | 1.007 | 1.000 |
| Rheumatic heart disease | -0.006 | -0.001 | 0.000 | -0.001 | 0.000 | -0.003 | 0.000 | -0.002 | 0.009 | 0.000 |
| Hypertension | 0.003 | 0.999 | 1.001 | 1.000 | 0.999 | 1.000 | -0.015 | 0.015 | 1.000 | 1.000 |
| Case sequence number | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 |
| Coronary heart disease | 1.003 | 1.000 | 0.996 | 1.005 | 0.993 | 0.999 | 1.001 | 0.993 | 0.996 | 0.999 |
| Rheumatic heart disease | 0.003 | 0.004 | 0.005 | -0.006 | -0.001 | 0.004 | -0.001 | -0.008 | -0.008 | 0.003 |
| Hypertension | -0.002 | 0.002 | -0.006 | 0.007 | -0.014 | -0.007 | 0.000 | 1.002 | 0.000 | 0.003 |
| Case sequence number | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 |
| Coronary heart disease | 0.000 | -0.004 | -0.002 | 0.000 | 0.002 | -0.002 | -0.001 | 0.001 | -0.002 | 0.006 |
| Rheumatic heart disease | 0.994 | 0.995 | 1.000 | 1.004 | 1.000 | 1.005 | 1.005 | 1.005 | 0.994 | 0.997 |
| Hypertension | -0.006 | 0.000 | -0.002 | -0.006 | 0.003 | 0.007 | 0.007 | 0.002 | -0.006 | 0.000 |
| Case sequence number | 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 |
| Coronary heart disease | 0.007 | -0.001 | 0.002 | -0.009 | -0.004 | -0.007 | 0.012 | 0.002 | 0.000 | 0.000 |
| Rheumatic heart disease | 0.004 | 0.003 | 0.001 | 0.000 | -0.008 | -0.006 | 0.008 | 0.000 | -0.003 | 0.003 |
| Hypertension | 0.998 | 1.004 | 1.000 | 0.997 | 0.997 | 1.001 | 0.995 | 1.005 | 1.003 | 1.001 |
| Case sequence number | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 |
| Coronary heart disease | -0.001 | 0.001 | -0.005 | 0.002 | 0.003 | 0.012 | 0.000 | -0.006 | -0.006 | 0.000 |
| Rheumatic heart disease | -0.004 | 0.002 | -0.004 | -0.005 | 0.002 | 0.004 | 0.000 | 0.005 | 0.003 | -0.003 |
| Hypertension | -0.002 | 0.004 | 0.000 | -0.006 | -0.003 | -0.006 | -0.002 | 0.009 | 0.006 | -0.001 |
Contrast case confirmed result and model prediction result, can see export numerical error be less than ± 1%, according to numerical value be greater than 0.8 namely regard as completely ill, numerical value be less than 0.2 be namely judged as completely not ill, it is 100% that this network model detects accuracy to 60 cases, and model has been set up.
Then remaining 40 case data are adopted to verify model prediction accuracy.40 checking case data make a definite diagnosis result as table 3 (wherein 1 represents ill, and 0 represents not ill):
Table 3
| Case sequence number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| Coronary heart disease | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Rheumatic heart disease | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Hypertension | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |
| Case sequence number | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 |
| Coronary heart disease | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 |
| Rheumatic heart disease | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| Hypertension | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| Case sequence number | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 |
| Coronary heart disease | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Rheumatic heart disease | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 |
| Hypertension | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 |
| Case sequence number | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 |
| Coronary heart disease | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Rheumatic heart disease | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Hypertension | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Detected above-mentioned 40 cases by prototype network, result of determination is as table 4:
Table 4
| Case sequence number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| Coronary heart disease | 0.999 | 1.010 | 1.047 | 0.991 | 1.023 | 0.970 | 0.998 | 1.003 | 1.009 | 0.999 |
| Rheumatic heart disease | 0.002 | -0.011 | -0.040 | 0.016 | -0.019 | 0.023 | 0.001 | -0.007 | 0.009 | -0.001 |
| Hypertension | 0.871 | 0.173 | 0.080 | 0.032 | 1.046 | -0.029 | -0.031 | 0.988 | 0.001 | 0.000 |
| Case sequence number | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 |
| Coronary heart disease | 1.027 | 0.917 | 1.019 | 1.214 | 0.959 | 1.008 | 1.045 | 1.024 | 1.025 | 0.102 |
| Rheumatic heart disease | -0.027 | 0.099 | -0.014 | -0.174 | 0.049 | -0.003 | -0.037 | -0.018 | -0.019 | 0.885 |
| Hypertension | 0.755 | -0.096 | 0.601 | 0.452 | -0.013 | -0.011 | 0.060 | 0.157 | 0.001 | 0.159 |
| Case sequence number | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 |
| Coronary heart disease | -0.004 | 0.021 | 0.015 | 0.005 | 0.011 | -0.032 | -0.003 | -0.007 | 0.000 | -0.002 |
| Rheumatic heart disease | 1.001 | 0.988 | 0.989 | 0.995 | 0.977 | 1.026 | -0.007 | -0.004 | 0.008 | 0.012 |
| Hypertension | -0.002 | -0.035 | -0.010 | 0.023 | 0.107 | 0.042 | 0.992 | 0.998 | 0.994 | 0.958 |
| Case sequence number | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 |
| Coronary heart disease | 0.002 | 0.006 | 0.168 | 0.063 | 0.003 | -0.005 | -0.004 | 0.002 | 0.113 | 0.005 |
| Rheumatic heart disease | -0.007 | -0.005 | 0.181 | -0.006 | 0.002 | -0.006 | -0.002 | -0.004 | 0.022 | 0.007 |
| Hypertension | 1.004 | 1.010 | 0.676 | 0.010 | 0.003 | 0.000 | -0.003 | 0.004 | 0.032 | 0.015 |
Contrast 40 case confirmed results and model result of determination, be greater than 0.8 namely think completely ill according to numerical value, numerical value is less than 0.2 and is namely judged as completely not ill, then have 3 case result of determination mistakes, namely accuracy rate is 92.5%.Substantially disease anticipation accuracy requirement is met.
Because the present embodiment modeling sample is less, model anticipation accuracy is relatively on the low side, and under the prerequisite that sample is abundant, accuracy can improve further.
After having verified, just this model parameter can be transferred to user terminal apparatus, user terminal apparatus obtains the personal information of a certain user and physiological detection information as table 5 again:
Table 5
In user terminal apparatus, life entity state estimation is-0.048,1.007,0.0862 for coronary heart disease, rheumatic heart disease, hypertensive assessment output numerical value, according to decision rule, numerical value is greater than 0.8 and namely thinks completely ill, numerical value be less than 0.2 be namely judged as completely not ill, then can obtain result of determination is 0,1,0, and namely this user suffers from rheumatic heart disease.According to this, user terminal apparatus just can differentiate that result is that user recommends seek medical advice guidance and life guide, and data, information are sent to remote control center and store, select according to user simultaneously, whether detection data and assessment result are sent to individual subscriber digital device by wireless transmission unit, assessment terminates.
According to above-described embodiment, just the present invention can be realized well.
Claims (9)
1. the self-service evaluating system of life entity physiological situation, is characterized in that, comprises more than one user terminal apparatus and remote control center; This user terminal apparatus comprises data processing and control module, the first input block be all connected with data processing and control module, the first output unit, the first transmission unit, disbursement and sattlement unit, data detecting unit and user identification unit; Described first transmission unit is connected with remote control center;
The appraisal procedure of data processing and control module is as follows:
A () judges whether user is new user, is then register; No, then directly log in, and identify userspersonal information P [s];
B () is selected the type of assessment and is judged whether the model of the type upgrades, no, then Renewal model parameter; Then carry out the formulation of test item;
C whether () detecting instrument is normal, no, then send instrument damage alarm; Then carry out next step;
D () adopts disbursement and sattlement unit to carry out disbursement and sattlement;
E () judges whether user puts in place, no, then judge timeout conditions; Be then carry out physiological situation detection, obtain physiological situation and detect data T [N];
F () judges that whether data are errorless, no, then send error in data alarm, and return step (e); Be then carry out physiologic status assessment or suspected case anticipation, and export corresponding case Suspected Degree data Em, assessment result, the detailed description state of an illness, health warning information and suggestion of seeking medical advice;
G () preserves various data, display assessment result and suggestion of seeking medical advice;
H () judges whether to carry out other assessments, be then return step (b); No, then terminate this assessment, and the way of output that detection data, assessment result, the information such as guide of seeking medical advice are selected according to user is exported, then various data are transferred to remote control center;
(i) after the data received arrange by remote control center, unified preservation.
2. the self-service evaluating system of life entity physiological situation according to claim 1, it is characterized in that, described remote control center is primarily of information processing and controlling unit, the second transmission unit be all connected with information processing and controlling unit, the second input block, the second output unit, data operation server, administration authority judgement unit form, and described second transmission unit is connected with the first transmission unit.
3. the self-service evaluating system of life entity physiological situation according to claim 2, is characterized in that, described data processing and control module are also connected with the first storage unit and wireless transmission unit.
4. the self-service evaluating system of life entity physiological situation according to claim 3, is characterized in that, described information processing and controlling unit is also connected with the second storage unit and expense collection unit.
5. the self-service evaluating system of life entity physiological situation according to any one of Claims 1 to 4, is characterized in that, judges in described step (e) timeout conditions concrete steps as: judge whether time-out, be, then carry out time-out reset, return step (a); No, then reminding user puts in place as early as possible, returns step (e).
6. the self-service evaluating system of life entity physiological situation according to claim 5, is characterized in that, differentiates that the whether errorless concrete steps of data are in described step (f):
(1) to physical detection data bag T [N], first according to checking algorithm, check bit calculating is carried out to valid data position in the packet received, then contrast with the check bit data in packet, differentiate that whether the check bit calculating acquisition is consistent with the check bit numerical value in packet, be, then data transmission is errorless, enters discriminating data step (2); No, then data transmission is wrong, sends error in data alarm, again reads instrument detection data;
(2) each number of significant digit certificate in physical detection data bag T [N] is read, the i.e. every physical detection data of user, then the maximum value limit and the minimum value limit that detect data and whether exceed respective items object human detection data is differentiated in packet, be, then Data Detection is wrong, send error in data alarm, return step (e) and re-start error items Data Detection; No, then data are errorless, continue physiologic status assessment or suspected case anticipation in step (f).
7. the self-service evaluating system of life entity physiological situation according to claim 6, is characterized in that, in described step (f), physiologic status assessment or suspected case anticipation concrete steps are:
(1) system m item assessment request is accepted;
(2) physical detection data Tm [N] and the personal information data Pm [S] of m item assessment request user is read;
(3) detection data Tm [N] is normalized, digitized processing is carried out to userspersonal information P [S], and data are packaged as neural network assessment models input data layout Bm [N+S];
(4) read m item and assess corresponding neural network assessment models LMBP (Bm [N+S]), assess, export assessment result Em;
(5) differentiate whether Em is less than smallest evaluation threshold epsilon m, is then Em zero setting; No, then carry out next step;
(6) Em is greater than to the assessment request of ill discrimination threshold, exports assessment result, and export detailed state of an illness description and suggestion of seeking medical advice according to Em value size.
8. the self-service evaluating system of life entity physiological situation according to claim 7, is characterized in that, described life entity physiologic status assessment and suspected case discrimination model are multiple discrimination model for different evaluation requirement.
9. the self-service evaluating system of life entity physiological situation according to claim 8, it is characterized in that, described life entity physiologic status assessment and the appraisal procedure of suspected case discrimination model are detect data Tm [N], userspersonal information Pm [S] for raw data with the Vital status of case, first to data normalization and digitizing, then assessment data Bm [N+S] is packaged as, finally choose corresponding neural network assessment models LM-BP(Bm [N+S]) assess, export case Suspected Degree Em; System exports the detailed state of an illness according to Em numerical value and describes and suggestion of seeking medical advice.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1428128A (en) * | 2001-12-27 | 2003-07-09 | 丽台科技股份有限公司 | Automated remote control method and system for assessing autonomic nervous system function |
| CN102231173A (en) * | 2011-06-22 | 2011-11-02 | 上海圆天电子科技有限公司 | Remote human body sub-health assessment instrument based on network technology |
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