CN112200385A - Medicine evaluation result prediction method and device, electronic equipment and storage medium - Google Patents
Medicine evaluation result prediction method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application provides a method and a device for predicting a drug review result, electronic equipment and a storage medium, and relates to the technical field of data processing, wherein the method comprises the following steps: acquiring the total number of original data with the same value as n fields in a target time period; wherein n is a positive integer greater than 1; acquiring the total number of historical data which are identical to n field values and pass the drug evaluation in a target time period; and calculating a drug evaluation result according to the total number of the original data and the total number of the historical data. Therefore, the prediction cost of the drug evaluation result is reduced, and the prediction accuracy is improved.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method, an apparatus, an electronic device, and a storage medium for predicting a drug review result.
Background
Enterprises need to predict markets and self projects in advance, and more careful strategic planning is facilitated. When an enterprise investigates competitive information, product establishment and drug registration, a question is answered: how likely the target drug will pass the review (i.e., approved for clinical, approved for marketing, etc.) after registration declaration? What time can the review be completed? The drug evaluation conclusion is influenced by many subjective and objective factors, such as: the research and development difficulty of the medicine, the standardization degree of enterprise research and development work, the queuing backlog quantity of the evaluation acceptance number, the human resources and the task fullness of each professional evaluation department, the perfection degree of medicine registration materials, the reply speed of the enterprise after the evaluation department puts forward the requirement of supplementary data, the relevant policies and changes of medicines and the like all influence the evaluation passing rate and the evaluation conclusion. There is no effective method disclosed at present to answer the above-mentioned questions.
Therefore, in view of the lack of an effective evaluation method for enterprises and the difficulty in obtaining all data research and evaluation data, relevant personnel can only rely on limited personal experience and spend a great deal of effort and time for subjective judgment when predicting the drug evaluation passing probability and the evaluation ending date, and objective and reliable ideal results cannot be obtained yet.
Disclosure of Invention
The present application is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, a first objective of the present application is to provide a method for predicting a drug review result, so as to reduce the prediction cost of the drug review result, improve the prediction accuracy, and solve the technical problem that in the prior art, when the probability of passing the drug review and the evaluation completion date are predicted, only limited personal experience is relied on, a great deal of effort and time is spent for subjective judgment, and an objective, reliable and ideal result cannot be obtained yet.
A second objective of the present application is to provide a device for predicting the result of a drug review.
A third object of the present application is to propose a computer device.
A fourth object of the present application is to propose a non-transitory computer-readable storage medium.
A fifth object of the present application is to propose a computer program product.
To achieve the above object, a method for predicting a drug review result is provided in an embodiment of the first aspect of the present application, including:
acquiring the total number of original data with the same value as n fields in a target time period; wherein n is a positive integer greater than 1;
acquiring the total number of historical data which are identical to the n field values and pass the drug evaluation in the target time period;
and calculating a drug evaluation result according to the total number of the original data and the total number of the historical data.
According to the method for predicting the drug evaluation result, the total number of original data with the same value as that of n fields in the target time period is obtained; wherein n is a positive integer greater than 1; acquiring the total number of historical data which are identical to n field values and pass the drug evaluation in a target time period; and calculating a drug evaluation result according to the total number of the original data and the total number of the historical data. Therefore, the prediction cost of the drug evaluation result is reduced, and the prediction accuracy is improved.
In an embodiment of the present application, the obtaining a total number of raw data in a target time period, which is the same as n field values, includes:
acquiring all data in the target time period;
grouping all the data according to the n field values, and dividing the data with the same value as the n field values into a group to obtain the data of the same type of drug varieties;
and calculating the total number of the grouped data of the various similar drug varieties to obtain the total number of the original data.
In an embodiment of the application, the obtaining of the total number of historical data which passes the drug evaluation and has the same value as the n field values in the target time period includes:
acquiring all the data which pass the review in the target time period;
grouping all the data which pass the review according to the n field values, and dividing the data which have the same value with the n field values into a group to obtain the data of the same type of drug varieties which pass the review;
and calculating the total number of the evaluated data of each similar medicine variety after grouping to obtain the total number of the historical data which passes the medicine evaluation.
In one embodiment of the present application, the calculating the drug review result according to the total number of the raw data and the total number of the historical data includes:
acquiring the total number of original data and the total number of historical data which pass the evaluation of each similar drug variety;
and calculating the ratio of the total number of the evaluated historical data of each similar medicine variety to the total number of the original data to obtain the medicine evaluation result.
In an embodiment of the present application, the method for predicting a drug review result further includes:
calculating the evaluation period of each data in each similar drug variety;
and calculating and obtaining the evaluation conclusion date according to the evaluation period of each piece of data.
In an embodiment of the present application, before obtaining the total number of raw data with the same value as n fields in the target time period, the method further includes:
acquiring public original data from a database;
and processing the public original data according to a pre-established data dictionary to obtain a standard field value related to the drug evaluation prediction.
In an embodiment of the present application, the method for predicting a drug review result further includes:
acquiring an attribute value of each standard field value;
calculating the attribute value of each standard field value according to a correlation analysis formula to obtain a correlation value between the standard field values;
and acquiring the n field values from the standard field values according to the correlation value between the standard field values and a preset threshold value.
To achieve the above object, a second aspect of the present application provides a device for predicting a drug review result, comprising:
the first acquisition module is used for acquiring the total number of original data with the same value as the n fields in a target time period; wherein n is a positive integer greater than 1;
the second acquisition module is used for acquiring the total number of the historical data which is identical to the n field values and passes the drug evaluation in the target time period;
and the calculation module is used for calculating a drug evaluation result according to the total number of the original data and the total number of the historical data.
The device for predicting the drug evaluation result of the embodiment of the application obtains the total number of the original data with the same value as that of n fields in the target time period; wherein n is a positive integer greater than 1; acquiring the total number of historical data which are identical to n field values and pass the drug evaluation in a target time period; and calculating a drug evaluation result according to the total number of the original data and the total number of the historical data. Therefore, the prediction cost of the drug evaluation result is reduced, and the prediction accuracy is improved.
To achieve the above object, a third aspect of the present application provides a computer device, including: a processor; a memory for storing the processor-executable instructions; the processor executes a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the method for predicting the drug review result described in the embodiment of the first aspect.
In order to achieve the above object, a non-transitory computer readable storage medium is provided in a fourth aspect of the present application, and a computer program is stored thereon, where the computer program is configured to, when executed by a processor, implement a method for predicting a drug review result according to an embodiment of the first aspect of the present application.
In order to achieve the above object, an embodiment of a fifth aspect of the present application provides a computer program product, where when executed by an instruction processor, the computer program product implements a method for predicting a drug review result as described in the embodiment of the first aspect of the present application.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
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The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flowchart illustrating a method for predicting a drug review result according to an embodiment of the present disclosure;
FIG. 2 is a diagram illustrating a correlation analysis provided in accordance with an embodiment of the present application;
FIG. 3 is a flowchart illustrating a method for predicting a drug review result according to a second embodiment of the present application;
FIG. 4 is a schematic structural diagram of a device for predicting the result of a drug review according to an embodiment of the present disclosure; and
FIG. 5 illustrates a block diagram of an exemplary computer device suitable for use in implementing embodiments of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
A method, an apparatus, an electronic device, and a storage medium for predicting a drug review result according to embodiments of the present application are described below with reference to the accompanying drawings.
Fig. 1 is a flowchart illustrating a method for predicting a drug review result according to an embodiment of the present disclosure.
As shown in fig. 1, the method for predicting the drug review result includes the following steps:
In the embodiment of the application, the n field values are obtained by processing the public raw data and are related to the drug evaluation prediction and have higher relevance.
In the embodiment of the application, the public raw data are obtained from the database, and are processed according to the pre-established data dictionary to obtain the standard field value related to the drug evaluation prediction.
Specifically, the public raw data is obtained from a national drug administration drug evaluation center website and a company self-established database.
In the embodiment of the application, the attribute value of each standard field value is obtained, the attribute value of each standard field value is calculated according to a correlation analysis formula, correlation values among the standard field values are obtained, and n field values are obtained from the standard field values according to the correlation values among the standard field values and a preset threshold.
Specifically, the public original data is cleaned through a data dictionary built by a company, so as to obtain standard field values related to drug evaluation prediction, such as one or more of the highest registration progress of a drug in China, the highest progress of different enterprises in the drug, disease fields, indications, clinical departments, dosage forms, registration classifications, evaluation conclusions, acceptance numbers, consistency evaluation conditions, innovation types, domestic/import, drug categories, preferential evaluation, special approval, major specialization, breakthrough therapy, standard drug names, standard enterprise names, evaluation ending dates, national drug administration acceptance dates, national drug evaluation center acceptance dates, previous evaluation data of the same drug, previous evaluation data of the same enterprise, and the like.
Further, performing relevance analysis on the standard field value, and solving the relevance of every two data according to a relevance coefficient formula; wherein, the formula of the correlation coefficient is as follows: r ═ Cov (X, Y)/(σ X σ Y); wherein X is a variable; y is a variable; cov (X, Y) is the covariance of X and Y; σ X is the standard deviation of X; σ Y is the standard deviation of Y.
N field values with the highest correlation degree can be obtained according to calculation, and the significant correlation is generally considered to exist in | r | > 0.95; the | r | > is more than or equal to 0.8, which is considered to be highly relevant; 0.5 ≦ r | <0.8 is considered moderately relevant; low degree of correlation is considered as if | r | is more than or equal to 0.3 and less than 0.5; the relationship of r <0.3 is extremely weak, and is considered to be irrelevant; therefore, n field values participating in calculation are selected on the basis that r is more than or equal to 0.5, wherein the preset threshold value can be selected and set according to the application scene.
For example, as shown in fig. 2, field values x1 and x2, a value of each attribute of x1 such as chemical 1, chinese medicine 3, etc., a value of each attribute of x2 such as application consistency evaluation 2, etc., and a field value y, a value of each attribute of y obtained by review 1, review 2, etc., are calculated by the above formula, and a value of a related score of x1 and x2 and y can be obtained.
And finally, acquiring the total number of the original data with the same value as the n fields in a target time period, wherein the target time period can be selected according to the application scene needs, such as 1 year, 3 years and the like.
In the embodiment of the application, all data in a target time period are obtained, all data are grouped according to n field values, the data which are the same as the n field values are divided into a group, the data of the similar drug varieties are obtained, and the total number of the grouped data of the similar drug varieties is calculated to obtain the total number of the original data.
For example, a total number of all data in a target time period, such as the n field values in the last three years, is obtained: firstly, acquiring the total number of all data, then grouping the data according to n field values obtained by the correlation analysis, grouping the data with equal n field values into a group to obtain the same type of drug varieties, and respectively solving the total number of the grouped data.
And 102, acquiring the total number of the historical data which is identical to the value of the n fields and passes the drug evaluation in the target time period.
In the embodiment of the application, all the data which pass the evaluation in the target time period are obtained, all the data which pass the evaluation are grouped according to n field values, the data which are the same as the n field values are divided into a group, the data of the similar drug varieties which pass the evaluation are obtained, the total number of the grouped data of the similar drug varieties which pass the evaluation is calculated, and the total number of the historical data which pass the drug evaluation is obtained.
For example, the total number of all the data which are identical in the n field values and pass the evaluation in the last three years is obtained, all the data which pass the evaluation are firstly obtained, then the data are grouped according to the n field values obtained by the correlation analysis, the data with the n field values which are identical are divided into a group, the same type of medicine variety is obtained, and finally the total number of the grouped data is respectively obtained.
And 103, calculating a drug evaluation result according to the total number of the original data and the total number of the historical data.
In the embodiment of the application, the total number of the original data and the total number of the historical data which are evaluated are obtained, the ratio of the total number of the historical data which are evaluated to the total number of the original data of each similar drug is calculated, and the drug evaluation result is obtained.
Specifically, all data are traversed in sequence, and the total number of all data of the same variety and the total number of data of the same variety which pass the evaluation are obtained; the approval pass rate for each variety is the total number of passed reviews/total number.
According to the method for predicting the drug evaluation result, the total number of original data with the same value as that of n fields in the target time period is obtained; wherein n is a positive integer greater than 1; acquiring the total number of historical data which are identical to n field values and pass the drug evaluation in a target time period; and calculating a drug evaluation result according to the total number of the original data and the total number of the historical data. Therefore, the prediction cost of the drug evaluation result is reduced, and the prediction accuracy is improved.
Based on the description of the above embodiment, as shown in fig. 3, after step 103, the method may further include:
And step 202, calculating and obtaining the evaluation conclusion date according to the evaluation period of each piece of data.
Specifically, the data is grouped according to n field values obtained by the correlation analysis, the data with equal n field values is grouped into a group, after the similar drug varieties are obtained, the evaluation period of each piece of data in the similar drug varieties can be calculated, the evaluation period is the evaluation ending date-the registration reporting date, the evaluation periods of the similar drug varieties are sorted, and the evaluation conclusion date is calculated, for example, the evaluation conclusion date is the median of the evaluation period of the similar drug varieties.
Therefore, the labor and time spent on predicting the date of obtaining the evaluation conclusion are reduced, and the prediction accuracy is improved.
In order to implement the above embodiments, the present application further provides a device for predicting a drug review result.
Fig. 4 is a schematic structural diagram of a device for predicting a drug review result according to an embodiment of the present disclosure.
As shown in fig. 4, the device for predicting the drug evaluation result comprises: a first acquisition module 410, a second acquisition module 420, and a calculation module 430.
A first obtaining module 410, configured to obtain a total number of original data in a target time period, where the total number of the original data is the same as n field values; wherein n is a positive integer greater than 1.
And a second obtaining module 420, configured to obtain a total number of historical data that passes the drug evaluation and is the same as the n field values in the target time period.
And the calculating module 430 is configured to calculate a drug review result according to the total number of the original data and the total number of the historical data.
The device for predicting the drug evaluation result of the embodiment of the application obtains the total number of the original data with the same value as that of n fields in the target time period; wherein n is a positive integer greater than 1; acquiring the total number of historical data which are identical to n field values and pass the drug evaluation in a target time period; and calculating a drug evaluation result according to the total number of the original data and the total number of the historical data. Therefore, the prediction cost of the drug evaluation result is reduced, and the prediction accuracy is improved.
It should be noted that the explanation of the embodiment of the method for predicting a drug review result is also applicable to the device for predicting a drug review result of the embodiment, and is not repeated herein.
In order to implement the foregoing embodiments, the present application also provides a computer device, including: a processor, and a memory for storing processor-executable instructions.
Wherein, the processor executes a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the method for predicting the drug review result as proposed in the foregoing embodiments of the present application.
To achieve the above embodiments, the present application also proposes a non-transitory computer-readable storage medium, in which instructions are executed by a processor to enable the processor to execute the method for predicting the drug review result proposed by the foregoing embodiments of the present application.
In order to implement the foregoing embodiments, the present application also proposes a computer program product, wherein when the instructions of the computer program product are executed by a processor, the computer program product executes a prediction method for implementing the drug review result proposed by the foregoing embodiments of the present application.
FIG. 5 illustrates a block diagram of an exemplary computer device suitable for use in implementing embodiments of the present application. The computer device 12 shown in fig. 5 is only an example and should not bring any limitation to the function and scope of use of the embodiments of the present application.
As shown in FIG. 5, computer device 12 is in the form of a general purpose computing device. The components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
Computer device 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally perform the functions and/or methodologies of the embodiments described herein.
The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with the computer system/server 12, and/or with any devices (e.g., network card, modem, etc.) that enable the computer system/server 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Moreover, computer device 12 may also communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public Network such as the Internet) via Network adapter 20. As shown, network adapter 20 communicates with the other modules of computer device 12 via bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with computer device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 16 executes various functional applications and data processing by executing programs stored in the system memory 28, for example, to implement the prediction method of the drug review result mentioned in the foregoing embodiments.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (10)
1. A method for predicting a drug review result is characterized by comprising the following steps:
acquiring the total number of original data with the same value as n fields in a target time period; wherein n is a positive integer greater than 1;
acquiring the total number of historical data which are identical to the n field values and pass the drug evaluation in the target time period;
and calculating a drug evaluation result according to the total number of the original data and the total number of the historical data.
2. The method for predicting the result of drug review of claim 1 wherein obtaining the total number of raw data having the same value as n fields within the target time period comprises:
acquiring all data in the target time period;
grouping all the data according to the n field values, and dividing the data with the same value as the n field values into a group to obtain the data of the same type of drug varieties;
and calculating the total number of the grouped data of the various similar drug varieties to obtain the total number of the original data.
3. The method for predicting the result of drug review of claim 2, wherein the obtaining the total number of historical data that passes drug review and has the same value as the n field values in the target time period comprises:
acquiring all the data which pass the review in the target time period;
grouping all the data which pass the review according to the n field values, and dividing the data which have the same value with the n field values into a group to obtain the data of the same type of drug varieties which pass the review;
and calculating the total number of the evaluated data of each similar medicine variety after grouping to obtain the total number of the historical data which passes the medicine evaluation.
4. The method of claim 3, wherein the calculating the drug review result based on the raw data total and the historical data total comprises:
acquiring the total number of original data and the total number of historical data which pass the evaluation of each similar drug variety;
and calculating the ratio of the total number of the evaluated historical data of each similar medicine variety to the total number of the original data to obtain the medicine evaluation result.
5. The method for predicting the result of a drug review of claim 3 further comprising:
calculating the evaluation period of each data in each similar drug variety;
and calculating and obtaining the evaluation conclusion date according to the evaluation period of each piece of data.
6. The method for predicting the result of drug review of claim 1, further comprising, before obtaining the total number of raw data having the same value as n fields within the target time period:
acquiring public original data from a database;
and processing the public original data according to a pre-established data dictionary to obtain a standard field value related to the drug evaluation prediction.
7. The method for predicting the result of a drug review of claim 6 further comprising:
acquiring an attribute value of each standard field value;
calculating the attribute value of each standard field value according to a correlation analysis formula to obtain a correlation value between the standard field values;
and acquiring the n field values from the standard field values according to the correlation value between the standard field values and a preset threshold value.
8. An apparatus for predicting a drug review result, the apparatus comprising:
the first acquisition module is used for acquiring the total number of original data with the same value as the n fields in a target time period; wherein n is a positive integer greater than 1;
the second acquisition module is used for acquiring the total number of the historical data which is identical to the n field values and passes the drug evaluation in the target time period;
and the calculation module is used for calculating a drug evaluation result according to the total number of the original data and the total number of the historical data.
9. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the program when executed by the processor implementing the method of predicting a drug review result of any of claims 1-7.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the program when executed by a processor implements a method for predicting a drug review result according to any of claims 1-7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113723826A (en) * | 2021-06-11 | 2021-11-30 | 北京华彬立成科技有限公司 | Method and device for monitoring early information of imitation drugs, electronic equipment and storage medium |
| CN114388061A (en) * | 2022-01-13 | 2022-04-22 | 水木未来(北京)科技有限公司 | Target evaluation method and device, electronic equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030110058A1 (en) * | 2001-12-11 | 2003-06-12 | Fagan Andrew Thomas | Integrated biomedical information portal system and method |
| CN104781458A (en) * | 2012-10-01 | 2015-07-15 | 独立行政法人科学技术振兴机构 | Approval prediction device, approval prediction method, and program |
| US20150323921A1 (en) * | 2013-07-10 | 2015-11-12 | Kabushiki Kaisha Toshiba | Operation schedule optimizing device, operation schedule optimizing method, and operation schedule optimizing program |
| CN107305658A (en) * | 2016-04-21 | 2017-10-31 | 北京京东尚科信息技术有限公司 | Method and system for management work stream in information system |
| CN110610425A (en) * | 2018-06-14 | 2019-12-24 | 杭州振牛信息科技有限公司 | Capital processing method and device and computer equipment |
-
2020
- 2020-10-29 CN CN202011181647.0A patent/CN112200385A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030110058A1 (en) * | 2001-12-11 | 2003-06-12 | Fagan Andrew Thomas | Integrated biomedical information portal system and method |
| CN104781458A (en) * | 2012-10-01 | 2015-07-15 | 独立行政法人科学技术振兴机构 | Approval prediction device, approval prediction method, and program |
| US20150323921A1 (en) * | 2013-07-10 | 2015-11-12 | Kabushiki Kaisha Toshiba | Operation schedule optimizing device, operation schedule optimizing method, and operation schedule optimizing program |
| CN107305658A (en) * | 2016-04-21 | 2017-10-31 | 北京京东尚科信息技术有限公司 | Method and system for management work stream in information system |
| CN110610425A (en) * | 2018-06-14 | 2019-12-24 | 杭州振牛信息科技有限公司 | Capital processing method and device and computer equipment |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113723826A (en) * | 2021-06-11 | 2021-11-30 | 北京华彬立成科技有限公司 | Method and device for monitoring early information of imitation drugs, electronic equipment and storage medium |
| CN114388061A (en) * | 2022-01-13 | 2022-04-22 | 水木未来(北京)科技有限公司 | Target evaluation method and device, electronic equipment and storage medium |
| CN114388061B (en) * | 2022-01-13 | 2024-09-20 | 水木未来(北京)科技有限公司 | Target evaluation method and device, electronic equipment and storage medium |
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