WO2019091111A1

WO2019091111A1 - Method for obtaining characteristics of controlled drugs, electronic device, and computer readable storage medium

Info

Publication number: WO2019091111A1
Application number: PCT/CN2018/090906
Authority: WO
Inventors: 周瑜; 阮晓雯; 徐亮; 肖京
Original assignee: 平安科技（深圳）有限公司
Priority date: 2017-11-10
Filing date: 2018-06-12
Publication date: 2019-05-16
Also published as: CN108022653B; CN108022653A

Abstract

Disclosed in the present application is method for obtaining characteristics of controlled drugs, which is applied to electronic devices. The method comprises: obtaining original case data of an insurance mechanism database and a medical institution database; preprocessing the original case data to obtain a preprocessed data set; establishing a classification model according to forward characteristics, nature characteristics and backward characteristics of controlled drugs; extracting data of the controlled drugs by means of the classification model; establishing a characteristic extraction model of specific controlled drugs according to specific characteristics of the specific controlled drugs; and returning a characteristic table of the specific controlled drugs by means of the characteristic extraction model. The present application also provides an electronic device and a computer readable storage medium. In the present application, by analyzing big data, a characteristic set of various controlled drugs can be rapidly obtained, thereby greatly improving the characteristic extraction speed and accuracy of different controlled drugs in different regions.

Description

Method for acquiring characteristic of controlled drug, electronic device and computer readable storage medium

This application claims priority to Chinese Patent Application No. 201711107651.0, entitled "Characteristics of Controlling Drugs, Electronic Devices and Computer-Readable Storage Media", filed on November 10, 2017. The content is incorporated herein by reference.

Technical field

The present application relates to the field of communications technologies, and in particular, to a method for acquiring features of a controlled drug, an electronic device, and a computer readable storage medium.

Background technique

With the development of information technology, the amount of data in the industry has increased dramatically. For industries such as medicine and meteorology that need to store a large amount of detection information, it is facing the challenge of rapid analysis of big data. For example, in the pharmaceutical industry, many drugs are strictly controlled because of certain hazards, and the use of these controlled drugs is often difficult to grasp. At present, the screening of drug control violations is mainly based on the corresponding business logic. First, according to the usage rules, the compliance documents are extracted, and then the subtraction method is used to obtain the violation documents. However, the business logic of different drugs is almost the same, and there are still some differences between the lists of controlled drugs in different regions, which leads to switching to different regions. Different controlled drugs are often easy to ignore and despise some importance when extracting features. feature.

Therefore, there is a need to propose a method for quickly and easily obtaining the characteristics of various controlled drugs.

Summary of the invention

In view of this, the present application provides a method for acquiring characteristics of a controlled drug, an electronic device, and a computer readable storage medium. By analyzing the big data, the feature set of various controlled drugs can be quickly obtained, and the control of different regions and different controls is greatly improved. The speed and accuracy of drug extraction.

First, in order to achieve the above object, the present application provides a method for acquiring a feature of a controlled drug, which is applied to an electronic device, and the method includes:

Obtain raw case data from the insurance institution database and the medical institution database;

Pre-processing the original case data to obtain a pre-processed data set;

Establish a classification model based on the forward characteristics, nature characteristics and backward characteristics of the controlled drugs;

Extracting data of the controlled drug by the classification model;

Establishing a specific control drug feature extraction model based on the unique characteristics of a specific controlled drug;

A feature table of the specific controlled drug is returned by the feature extraction model.

In addition, in order to achieve the above object, the present application further provides an electronic device including a memory and a processor, wherein the memory stores a feature acquiring system of a controlled drug that can be run on the processor, and the characteristics of the controlled drug The step of implementing the feature acquisition method of the controlled drug as described above when the system is executed by the processor.

Further, in order to achieve the above object, the present application further provides a computer readable storage medium, wherein the computer readable storage medium stores a feature acquisition system for a controlled drug, and the feature acquisition system of the controlled drug can be at least one processor. Executing to cause the at least one processor to perform the steps of the feature acquisition method of the controlled drug as described above.

Compared with the prior art, the electronic device, the feature acquiring method of the controlled drug, and the computer readable storage medium proposed by the present application first obtain the original case data of the insurance institution database and the medical institution database; and then, the original case The data is pre-processed to obtain a pre-processed data set; secondly, a classification model is established according to the forward characteristics, the nature characteristics and the backward characteristics of the controlled drugs; and then, the data of the controlled drugs is extracted by the classification model; Then, a specific controlled drug feature extraction model is established according to the unique characteristics of the specific controlled drug; finally, the feature table of the specific controlled drug is returned by the feature extraction model. Through the electronic device, the feature acquisition method of the controlled drug and the computer readable storage medium proposed by the present application, the analysis of the big data can quickly acquire the feature sets of various controlled drugs, and greatly improve the feature extraction of different controlled drugs in different regions and regions. Speed and accuracy are more convenient, accurate and faster than the prior art.

DRAWINGS

1 is a schematic diagram of an optional application environment of each embodiment of the present application;

2 is a schematic diagram of an optional hardware architecture of an electronic device of the present application;

3 is a schematic diagram of a program module of a first embodiment of a feature acquisition system for a controlled drug of the present application;

4 is a schematic flow chart of a first embodiment of a method for acquiring characteristics of a controlled drug of the present application;

5 is a schematic flow chart of a second embodiment of a method for acquiring characteristics of a controlled drug of the present application;

6 is a schematic flow chart of a third embodiment of a method for acquiring characteristics of a controlled drug of the present application;

7 is a schematic flow chart of a fourth embodiment of a method for acquiring characteristics of a controlled drug of the present application;

FIG. 8 is a schematic flow chart of a fifth embodiment of a method for acquiring characteristics of a controlled drug of the present application.

The implementation, functional features and advantages of the present application will be further described with reference to the accompanying drawings.

Detailed ways

In order to make the objects, technical solutions, and advantages of the present application more comprehensible, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not intended to be limiting. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present application without departing from the inventive scope are the scope of the present application.

It should be noted that the descriptions of "first", "second" and the like in the present application are for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. . Thus, features defining "first" or "second" may include at least one of the features, either explicitly or implicitly. In addition, the technical solutions between the various embodiments may be combined with each other, but must be based on the realization of those skilled in the art, and when the combination of the technical solutions is contradictory or impossible to implement, it should be considered that the combination of the technical solutions does not exist. Nor is it within the scope of protection required by this application.

Referring to FIG. 1 , it is a schematic diagram of an optional application environment of each embodiment of the present application.

In the present embodiment, the present application is applicable to an application environment 1, which includes, but is not limited to, an insurance institution database 10 (referred to as "insurance institution" in FIG. 1) and a medical institution database 11 (in FIG. 1). Referring to the "medical institution" for short, the network 12 and the electronic device 13, it should be noted that in other embodiments, the insurance institution database 10 and the medical institution database 11 may be an insurance institution server or a medical institution server.

In an embodiment, the electronic device 13 may be a mobile device, a smart phone, a notebook computer, a digital broadcast receiver, a PDA (Personal Digital Assistant), a PAD (Tablet), etc., and a desktop device such as a desktop computer. A fixed terminal such as a notebook, a server, or the like. In the present embodiment, the electronic device 13 is used as an application server. The application server may be a computing device such as a rack server, a blade server, a tower server, or a rack server. The application server may be a stand-alone server or a server cluster composed of multiple servers. The database, the implementation of different professional companies are different, the main database type is Oracle, there will also be various types of databases such as PostgreSQL, MySQL. The network 12 may be an intranet, an Internet, a Global System of Mobile communication (GSM), a Wideband Code Division Multiple Access (WCDMA), a 4G network, Wireless or wired networks such as 5G networks, Bluetooth, Wi-Fi, and call networks.

In an embodiment, the insurance institution database 10 and the medical institution database 11 are communicatively coupled to one or more of the electronic devices 13 (only one of which is shown) through the network 12 to enable the electronic device 13 to pass The network 12 performs data transmission and interaction with the insurance institution database 10 and the medical institution database 11.

Referring to FIG. 2, it is a schematic diagram of an optional hardware architecture of the electronic device 13 of the present application.

In an embodiment, the electronic device 13 may include, but is not limited to, a memory 130, a network interface 131, and a processor 132 that are communicably connected to each other through a system bus. It is noted that FIG. 2 only shows the electronic device 13 having the components 130-132, but it should be understood that not all illustrated components may be implemented, and more or fewer components may be implemented instead.

In an embodiment, the memory 130 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (eg, SD or DX memory, etc.), random access Memory (RAM), static random access memory (SRAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), magnetic memory, magnetic disk, optical disk, and the like. In some embodiments, the memory 130 may be an internal storage unit of the electronic device 13, such as a hard disk or a memory of the electronic device 13. In other embodiments, the memory 130 may also be an external storage device of the electronic device 13, such as a plug-in hard disk equipped on the electronic device 13, a smart memory card (SMC), and a secure digital device. (Secure Digital, SD) card, flash card, etc. Of course, the memory 130 may also include both an internal storage unit of the electronic device 13 and an external storage device thereof. In the present embodiment, the memory 130 is generally used to store an operating system installed in the electronic device 13 and various types of application software, such as program codes of the feature acquisition system 200 for controlling drugs. In addition, the memory 130 can also be used to temporarily store various types of data that have been output or are to be output.

In an embodiment, the network interface 131 may include a wireless network interface or a wired network interface, which is generally used to establish a communication connection between the electronic device 13 and other electronic devices.

In an embodiment, the processor 132 may be a Central Processing Unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor 132 is typically used to control the overall operation of the electronic device 13. In this embodiment, the processor 132 is configured to run program code or processing data stored in the memory 130, such as the feature acquiring system 200 that runs the controlled drug.

So far, the hardware structure and functions of the devices related to this application have been described in detail. Hereinafter, various embodiments of the present application will be made based on the above description.

First, the present application proposes a feature acquisition system 200 for a controlled drug.

Referring to FIG. 3, it is a program module diagram of the first embodiment of the feature acquisition system 200 of the present invention.

In one embodiment, the controlled drug feature acquisition system 200 includes a series of computer program instructions stored on the memory 130 that, when executed by the processor 132, can implement the features of various embodiments of the present application. Get the operation. In some embodiments, the feature acquisition system 200 that governs medications can be divided into one or more modules based on the particular operations implemented by the various portions of the computer program instructions. For example, in FIG. 3, the feature acquisition system 200 of the controlled drug may be divided into an acquisition module 201, a processing module 202, an establishment module 203, a calling module 204, a storage module 205, and a display module 206. among them:

The obtaining module 201 is configured to obtain original case data of the insurance institution database 10 and the medical institution database 11.

Specifically, all the case data of the insurance institution database 10 and the medical institution database 11 of each level are received, and the insurance institution database 10 can be a database of the insurance company of the city. The case data in the database mainly includes a policy and a receipt, and the medical institution database 11 can be For the database/server of hospitals and medical centers in the city, according to the different medical insurance policies, the original case data of hospitals, medical centers, etc. including provinces, municipalities, districts and other regions can be obtained according to the needs.

The processing module 202 is configured to perform pre-processing on the original case data to obtain a pre-processed data set.

Specifically, the preprocessing method includes:

1. Sort the original case data in chronological order; you can also segment the data in time, such as [Ti, Ti+1], i=0, 1...N-1;

2. Standardize the sorted data, including: unified quantitative method, unified measurement unit, and unified performance form. The unified quantification method is mainly for enumerated data, such as the patient's response to specific viruses and diseases (immunity, high resistance, resistance, sensation). The unified unit of measurement is mainly for numerical data, such as the dose use of the controlled drug. , package, gram, milliliter, etc. as a unit, the classification of data of different nature uses a unified unit of measurement, for example, the measurement of milliliters, liters is uniformly measured in milliliters as a unit of measurement, and the measurement of milligrams, grams, kilograms is uniformly measured in milligrams. Unit; unified performance form is mainly for multi-expression data, such as date data, which can be expressed as YYYY-MM-DD or other forms such as MM-DD-YYYY, which is unified by YYYY-MM-DD. Formal identity.

3. Denoise the normalized data, establish a cleaning model to filter the data, and delete the data that does not meet the judgment criteria:

The cleaning model includes a plurality of judgment criteria, for example, clearing garbled data in the original data, clearing data that is obviously not in accordance with common sense or obviously comparing the preset conditions, and clearing data irrelevant to the influence of the controlled drug use. Clear the duplicate data and clear the data of patients with serious data loss;

A filter is created based on each criterion, and the data is filtered synchronously/asynchronously with a plurality of said filters.

The establishing module 203 is configured to establish a classification model according to the forward characteristics, the nature features, and the backward characteristics of the controlled drugs.

Specifically, the classification model is established based on the time dimension, and the reference point is selected at a specific time point, and the data acquired at the reference point is used as a natural feature, and the data before the reference point is used as a forward feature, and the data obtained after the reference point is used. As a backward feature;

Specifically, data of the general characteristics of the controlled drugs in different time periods are selected as training samples to train the classification model;

Specifically, the classification model may provide a set of attributes for the decision tree classifier, and the decision tree model may classify the data by making a series of decisions based on the attribute set; selecting the tree classifier, selecting the tree classifier to use and Similar techniques for decision tree classifiers classify data. Different from the decision tree, the selection tree contains special selection nodes, and the selection node has multiple branches. In addition, artificial neural networks, case inferences, nearest neighbor methods, support vector machines, and random forests can be used.

The calling module 204 is configured to extract data of the controlled medication by using the classification model.

Specifically, the data classified by the classification model has a label for controlling drugs, for example, "insulin", "glucose" and the like are extracted, and at this time, all the data are data related to the controlled drugs. ;

Specifically, a controlled drug database can be established for the extracted data to facilitate subsequent use and query.

In an embodiment, the establishing module 203 is further configured to establish a specific controlled drug feature extraction model according to the unique characteristics of the specific controlled drug.

Specifically, different controlled drugs have different characteristics, including but not limited to application scenarios, reimbursement conditions and reimbursement quotas according to medical insurance policies, drug use status, specificity of different places, etc., according to different controlled drugs The unique characteristics of the specific controlled drug feature extraction model can be used to extract the characteristics of the specific controlled drug. For example, diabetes has evolved from a geriatric disease to a disease that is common to children, young people, and the elderly. Although it is the same disease, the management of different age groups is different. For example, different time for disease management, different time for treatment, different physical conditions, different blood glucose management standards, because of these differences, different ages It has different characteristics in the use of insulin. Therefore, it is possible to establish the characteristic extraction basis of insulin based on age as a characteristic feature. Based on age, the expression is established according to the different usage conditions of different ages of insulin, and the feature extraction model is established based on the expression. According to the feature extraction model, all insulin-related data in the controlled drug database can be found.

Specifically, the feature extraction model may be a classification model, such as a two-category model, and the two-category model is defined to include two categories, a positive category and a negative category, respectively representing specific controlled drugs and other drugs, such as a positive class. Insulin, the negative class is other controlled drugs.

The two-category model is used to predict (classify) the test samples in the controlled drug database, and sometimes there are cases of classification errors. For example, when the characteristic features of the two controlled drugs appear intersection, the feature extraction model needs to be corrected. For example, a classification scoring model can be added.

Specifically, the two-class model is used to classify the test samples in the controlled drug database, each object in the positive class is referred to as a positive instance, and each object in the negative class is referred to as a negative instance. The situation arises as follows: If an instance is a positive class and is predicted to be a positive class, it is called a true positive (TP), and if the instance is a negative class is predicted to be a positive class, it is called a false positive class (False postiVe , FP). Correspondingly, if the instance is a negative class is predicted to be a negative class, it is called a true negative class (TN), and a positive instance is predicted to be a negative class (false negative, FN).

In the classification scoring model, the number of corresponding classifications is represented by TP, FN, FP, and TN, respectively: TP: positive instance prediction is positive class number; FN: positive instance prediction is negative class number; FP: negative instance prediction The number of positive classes; TN: The negative instance is predicted to be the number of negative classes.

In addition, the classification scoring model can also calculate the following three parameters: sensitivity: the proportion of instances in the positive class that are correctly predicted to be positive, ie TP/(TP+FN). Specificity: The proportion of instances in the negative class that are correctly predicted to be negative, ie TN/(TN+FP). Positive Predictive Value (PPV): The proportion of positive instances in the instance predicted to be positive, ie TP/(TP+FP).

According to the above classification scoring model, the classification results can be evaluated. According to the evaluation results, the classification situation can be understood and the feature extraction model can be optimized.

In an embodiment, the invoking module 204 is further configured to return a feature table of the specific controlled drug by using the feature extraction model.

In particular, the features affecting the use of a particular controlled drug include a plurality of features, and a profile of a particular controlled drug can be established based on the data returned by the feature, including a plurality of influencing factors for the use of the particular controlled drug. For example, in the case of insulin, the data in the feature table may include information about patient-predicted pre-existing parameters, including patient condition parameters, such as a history of drug allergy or sensitivity, other currently administered drugs in the patient's tissue. , age, weight, height, renal function, or liver function test data; parameters related to the current state of the patient's body (ie, patient condition parameters), such as blood pressure, heart rate, heart rate, temperature, blood oxygen, respiratory rate, or ventilation frequency; Parameters, usage of controlled drugs, frequency of use, cost, time of drug use, current drug, drug class, drug allergy history and sensitivity; comorbid case data, including acute comorbidities and chronic comorbidities, eg acute comorbidities Including hypoglycemia and hyperglycemia, chronic complications include eye lesions, kidney disease, neuropathy, cardiovascular disease, and foot lesions.

The storage module 205 is configured to store data acquired by the acquisition module 201, the processing module 202, the establishment module 203, and the calling module 204. For example, the storage module 205 can be used to store a feature table of a specific controlled drug. The storage module 205 includes a readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (eg, SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read only memory. (ROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), magnetic memory, magnetic disk, optical disk, and the like.

The display module 206 is configured to display an intermediate result and a final result in the working process of the feature acquiring system 200 of the controlled drug. The display module 206 includes a display device such as an LCD or an LED, and the display module 206 can be used to display the specific control. A list of the characteristics of the drug.

In addition, the present application also proposes a method for acquiring characteristics of a controlled drug.

Referring to FIG. 4, it is a schematic flowchart of the first embodiment of the method for acquiring characteristics of the controlled drug of the present application. In the present embodiment, the order of execution of the steps in the flowchart shown in FIG. 4 may be changed according to different requirements, and some steps may be omitted.

In step S110, the original case data of the insurance institution database 10 and the medical institution database 11 are obtained.

Specifically, all the case data of the insurance institution database 10 and the medical institution database 11 of each level are received, and the insurance institution database 10 can be a database of the insurance company of the city. The case data in the database mainly includes a policy and a receipt, and the medical institution database 11 can be For the hospitals and medical centers of this city, according to the different medical insurance policies, the original case data of hospitals, medical centers, etc. including provinces, municipalities, districts and other regions can be obtained as needed.

In step S120, the original case data is preprocessed to obtain a preprocessed data set.

Specifically, the preprocessing manner includes sorting, normalizing, denoising, etc. the original case data in chronological order.

Step S130, establishing a classification model according to the forward characteristics, the nature characteristics and the backward characteristics of the controlled drugs.

Step S140, extracting data of the controlled drug by using the classification model.

Step S150, establishing a specific control drug feature extraction model according to the unique characteristics of the specific controlled drug.

Step S160, returning a feature table of the specific controlled drug by the feature extraction model.

As shown in FIG. 5, it is a schematic flow chart of a second embodiment of the method for acquiring characteristics of the controlled drug of the present application. In this embodiment, in the step S120 in the first embodiment of the method for acquiring the characteristics of the controlled drug, the pre-processing step further includes the following steps:

In step S210, the original case data is sorted in chronological order.

Specifically, the original case data is sorted in chronological order, and the data may also be time-segmented, such as [Ti, Ti+1], i=0, 1...N-1.

In step S220, the sorted data is normalized.

Specifically, the normalization process includes: a unified quantization method, a unified measurement unit, and a unified expression form. The unified quantification method is mainly for enumerated data, such as the patient's response to specific viruses and diseases (immunity, high resistance, resistance, sensation). The unified unit of measurement is mainly for numerical data, such as the dose use of the controlled drug. , package, gram, milliliter, etc. as a unit, the classification of data of different nature uses a unified unit of measurement, for example, the measurement of milliliters, liters is uniformly measured in milliliters as a unit of measurement, and the measurement of milligrams, grams, kilograms is uniformly measured in milligrams. Unit; unified performance form is mainly for multi-expression data, such as date data, which can be expressed as YYYY-MM-DD or other forms such as MM-DD-YYYY, which is unified by YYYY-MM-DD. Formal identity.

In step S230, the normalized data is denoised.

Specifically, the denoising includes establishing a cleaning model to filter data, and deleting data that does not meet the criteria for judging:

The cleaning model includes a plurality of judgment criteria, for example, clearing garbled data in the original data, clearing data that is obviously not in accordance with common sense or obviously comparing the preset conditions, and clearing data irrelevant to the influence of the controlled drug use. Clear the duplicate data and clear the data of patients with severe data loss.

Specifically, a filter is established according to each criterion, and data is filtered synchronously/asynchronously with a plurality of said filters.

Feature acquisition method for controlled drugs; feature acquisition method for controlled drugs; feature acquisition method for controlled drugs; feature acquisition method for controlled drugs

FIG. 6 is a schematic flow chart of a third embodiment of a method for acquiring characteristics of a controlled drug of the present application. In this embodiment, the step S150 of the first embodiment of the method for acquiring the characteristics of the controlled drug includes the following steps:

Step S510, establishing a two-category model according to the unique characteristics of the specific controlled drug.

Specifically, the two-category model is defined to include two categories, a positive class and a negative class, respectively representing specific controlled drugs and other drugs, such as a positive class of insulin and a negative class of other controlled drugs.

Step S520, classifying the test samples in the controlled drug database using the two-category model.

Specifically, each object in the positive class is called a positive instance, and each object in the negative class is called a negative instance. In this case, there are four cases, as follows: If an instance is a positive class and is predicted as A positive class, called a true positive (TP), is called a positive class (False postiVe, FP) if the instance is a negative class. Correspondingly, if the instance is a negative class is predicted to be a negative class, it is called a true negative class (TN), and a positive instance is predicted to be a negative class (false negative, FN).

In step S530, the classification result is evaluated using a classification scoring model to optimize the two classification model.

Specifically, using the two-category model to predict (classify) the test samples in the controlled drug database, sometimes a classification error occurs, for example, when the unique characteristics of the two controlled drugs appear intersection, the feature extraction model needs to be performed at this time. Corrected. For example, a classification scoring model can be added.

In the classification scoring model, the number of corresponding classifications is represented by TP, FN, FP, and TN, respectively: TP: positive instance prediction is positive number; FN: positive instance prediction is negative class number; FP: negative instance prediction The number of positive classes; TN: The negative instance is predicted to be the number of negative classes.

According to the above classification scoring model, the classification results can be evaluated. According to the evaluation results, the classification situation can be understood, and the feature extraction model (two classification model) can be optimized.

FIG. 7 is a schematic flow chart of a fourth embodiment of a method for acquiring characteristics of a controlled drug of the present application. In this embodiment, the method for acquiring the characteristics of the controlled drug further includes the following steps after the step S140 of the first embodiment:

Step S610, establishing a controlled drug database for the extracted data.

Specifically, a controlled drug database can be established for the extracted data to facilitate subsequent use and query. The database, the implementation of different professional companies are different, the main database type is Oracle, there will also be various types of databases such as PostgreSQL, MySQL.

Referring to FIG. 8 , it is a schematic flowchart of a fifth embodiment of a method for acquiring characteristics of a controlled drug of the present application. In the present embodiment, the steps S710-S760 of the feature acquisition method of the controlled drug are similar to the steps S110-S160 of the first embodiment, except that the method further includes steps S770-S780. The order of execution of the steps in the flowchart shown in FIG. 10 may be changed according to different requirements, and some steps may be omitted.

In step S710, the original case data of the insurance institution database 10 and the medical institution database 11 are obtained.

Step S720, preprocessing the original case data to obtain a preprocessed data set.

Step S730, establishing a classification model according to the forward characteristics, the nature characteristics and the backward characteristics of the controlled drugs.

Step S740, extracting data of the controlled drug by using the classification model.

Step S750, establishing a specific control drug feature extraction model according to the unique characteristics of the specific controlled drug.

Step S760, returning a feature table of the specific controlled drug by the feature extraction model.

Step S770, storing the feature table.

Specifically, the storage module 205 can be used to store a feature table of a particular controlled drug. The storage module 205 includes a readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (eg, SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read only memory. (ROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), magnetic memory, magnetic disk, optical disk, and the like.

Step S780, displaying the feature table to the user.

Specifically, the intermediate result and the final result in the working process of the feature acquisition system 200 of the controlled drug are displayed to the user through the display device.

The serial numbers of the embodiments of the present application are merely for the description, and do not represent the advantages and disadvantages of the embodiments.

Through the description of the above embodiments, those skilled in the art can clearly understand that the foregoing method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be through hardware, but in many cases, the former is better. Implementation. Based on such understanding, the technical solution of the present application, which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM, disk, The optical disc includes a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the methods described in the various embodiments of the present application.

The above is only a preferred embodiment of the present application, and thus does not limit the scope of the patent application, and the equivalent structure or equivalent process transformation made by the specification and the drawings of the present application, or directly or indirectly applied to other related technical fields. The same is included in the scope of patent protection of this application.

Claims

A method for acquiring a feature of a controlled drug is applied to an electronic device, characterized in that the method comprises:

Obtain raw case data from the insurance institution database and the medical institution database;

Pre-processing the original case data to obtain a pre-processed data set;

Establish a classification model based on the forward characteristics, nature characteristics and backward characteristics of the controlled drugs;

Extracting data of the controlled drug by the classification model;

Establishing a specific control drug feature extraction model based on the unique characteristics of a specific controlled drug;

A feature table of the specific controlled drug is returned by the feature extraction model.
The method for acquiring a characteristic of a controlled drug according to claim 1, wherein the pre-processing comprises the following steps:

Sorting the original case data in chronological order;

Normalize the sorted data; and

Denormalize the normalized data.
The method for obtaining a feature of a controlled drug according to claim 2, wherein the normalization process comprises the following steps:

Uniform quantization method for enumerated data;

a unified unit of measure for numerical data; and

A unified representation of data for multiple expressions.
The method for acquiring a characteristic of a controlled drug according to claim 2, wherein the denoising step comprises:

Establish a cleaning model; and

The normalized data is filtered according to the cleaning model.
The method for acquiring a characteristic of a controlled drug according to any one of claims 1 to 4, wherein the establishing the classification model further comprises the steps of:

Establish a classification model based on the time dimension; and

The data of the general characteristics of the controlled drugs in different time periods are selected as training samples to train the classification model;

The classification model is a decision tree classifier.
The method for obtaining a characteristic of a controlled drug according to any one of claims 1 to 4, further comprising the following steps after extracting the data of the controlled drug by the classification model:

Establish a controlled drug database for the extracted data.
The method for acquiring a characteristic of a controlled drug according to any one of claims 1 to 4, wherein the feature extraction model is a two-category model, and the two-category model includes two categories: a positive class and a negative class, Positive and negative classes represent specific controlled drugs and other drugs, respectively.
The method for acquiring a feature of a controlled drug according to any one of claims 1 to 4, wherein the method for acquiring the feature further comprises the following steps:

Storing intermediate data of each step and the feature table; and

The feature table is displayed to the user through the display device.
An electronic device, comprising: a memory, a processor, and a memory acquiring a feature acquiring system of a controlled drug running on the processor, wherein the feature acquiring system of the controlled drug is The processor implements the following steps when executed:

Obtain raw case data from the insurance institution database and the medical institution database;

Pre-processing the original case data to obtain a pre-processed data set;

Establish a classification model based on the forward characteristics, nature characteristics and backward characteristics of the controlled drugs;

Extracting data of the controlled drug by the classification model;

Establishing a specific control drug feature extraction model based on the unique characteristics of a specific controlled drug;

A feature table of the specific controlled drug is returned by the feature extraction model.
The electronic device of claim 9, wherein the pre-processing comprises the steps of:

Sorting the original case data in chronological order;

Normalize the sorted data; and

Denormalize the normalized data.
The electronic device of claim 10, wherein the normalization process comprises the following steps:

Uniform quantization method for enumerated data;

a unified unit of measure for numerical data; and

A unified representation of data for multiple expressions.
The electronic device of claim 10, wherein the denoising step comprises:

Establish a cleaning model; and

The normalized data is filtered according to the cleaning model.
The electronic device according to any one of claims 9 to 12, wherein the establishing the classification model further comprises the steps of:

Establish a classification model based on the time dimension; and

The data of the general characteristics of the controlled drugs in different time periods are selected as training samples to train the classification model;

The classification model is a decision tree classifier.
The electronic device according to any one of claims 9 to 12, further comprising the following steps after extracting the data of the controlled drug by the classification model:

Establish a controlled drug database for the extracted data.
The electronic device according to any one of claims 9 to 12, wherein the feature extraction model is a two-category model, and the two-category model includes two categories of a positive class and a negative class, the positive class and the negative class. The classes represent specific controlled drugs and other drugs.
The electronic device according to any one of claims 9 to 12, wherein the method for acquiring features further comprises the following steps:

Storing intermediate data of each step and the feature table; and

The feature table is displayed to the user through the display device.
A computer readable storage medium storing a feature acquisition system for a controlled drug, the feature acquiring system of the controlled drug being executable by at least one processor to cause the at least one processor to perform the following step:

Obtain raw case data from the insurance institution database and the medical institution database;

Pre-processing the original case data to obtain a pre-processed data set;

Establish a classification model based on the forward characteristics, nature characteristics and backward characteristics of the controlled drugs;

Extracting data of the controlled drug by the classification model;

Establishing a specific control drug feature extraction model based on the unique characteristics of a specific controlled drug;

A feature table of the specific controlled drug is returned by the feature extraction model.
The computer readable storage medium of claim 17 wherein said preprocessing comprises the steps of:

Sorting the original case data in chronological order;

Normalize the sorted data; and

Denormalize the normalized data.
The computer readable storage medium of claim 18, wherein the normalization process comprises the steps of:

Uniform quantization method for enumerated data;

a unified unit of measure for numerical data; and

A unified representation of data for multiple expressions.
The computer readable storage medium of claim 18, wherein the denoising step comprises:

Establish a cleaning model; and

The normalized data is filtered according to the cleaning model.