WO2019196299A1

WO2019196299A1 - Electronic device, nasopharyngeal cancer risk warning method and computer readable storage medium

Info

Publication number: WO2019196299A1
Application number: PCT/CN2018/102107
Authority: WO
Inventors: 卢少烽; 洪博然; 徐亮; 阮晓雯; 肖京
Original assignee: 平安科技（深圳）有限公司
Priority date: 2018-04-09
Filing date: 2018-08-24
Publication date: 2019-10-17
Also published as: CN108831553A

Abstract

An electronic device, a nasopharyngeal cancer risk warning method and a computer readable storage medium, the method comprising: upon the receipt of a nasopharyngeal cancer screening request from a client to be screened, acquiring, from the nasopharyngeal cancer screening request, characteristic labels of the client to be screened (S10); if the acquisition of the characteristic labels from the nasopharyngeal cancer screening request fails, acquiring, from the nasopharyngeal cancer screening request, client attribute data of the client to be screened (S20); extracting, from a plurality of predetermined service servers, various characteristic data corresponding to the client attribute data of the client to be screened (S30); performing, according to a preset extraction rule of characteristic labels, characteristic label analysis to the extracted various characteristic data to analyze the characteristic labels of the client to be screened (S40); performing quantity analysis to predetermined significant characteristic labels in the characteristic labels of the client to be screened to determine a nasopharyngeal cancer risk grade of the client to be screened (S50); and outputting warning information corresponding to the determined nasopharyngeal cancer risk grade (S60). The described method enables a client to carry out an early prevention or treatment of nasopharyngeal cancer, and effectively improves the prevalence of nasopharyngeal cancer.

Description

Electronic device, nasopharyngeal cancer risk warning method and computer readable storage medium

This application is based on the priority of the Paris Convention, which is entitled to the Chinese patent application entitled "Electronic Device, Nasopharyngeal Cancer Risk Warning Method and Computer-Readable Storage Medium", filed on April 9, 2018, which is entitled to CN 2018103116455. The entire content of the application is incorporated herein by reference.

Technical field

The present application relates to the field of big data analysis technologies, and in particular, to an electronic device, a nasopharyngeal cancer risk warning method, and a computer readable storage medium.

Background technique

Nasopharyngeal carcinoma is a high-grade malignant tumor with the highest incidence of otolaryngology and malignant tumors. The etiology is complicated. The cause of nasopharyngeal carcinoma in traditional medicine is still unclear. Most of the existing researches come from clinical observations (related to heredity, environment, and viruses). However, clinical observations have limited access to personal information. At the same time, existing research usually relies on the professional medical knowledge and personal experience of the researchers. Therefore, the accuracy of the research cannot meet the requirements, and it is impossible to form an objective and accurate screening system for the cause of the disease. .

Therefore, how to achieve objective and accurate screening of the cause of nasopharyngeal cancer, and to achieve early prevention and intervention has become a technical problem to be solved.

Summary of the invention

The main object of the present application is to provide an electronic device, a nasopharyngeal cancer risk warning method, and a computer readable storage medium, which are intended to promptly prevent or treat a customer and improve the condition of nasopharyngeal cancer.

A first aspect of the present application provides an electronic device including a memory and a processor, wherein the memory stores a nasopharyngeal cancer risk warning system operable on the processor, wherein the nasopharyngeal cancer risk warning system is The processor implements the following steps when it executes:

After receiving a nasopharyngeal cancer screening request from a client to be screened, obtaining a characteristic label of the customer to be screened from the nasopharyngeal cancer screening request;

If the feature tag fails to be obtained from the nasopharyngeal cancer screening request, obtaining the customer attribute data of the to-screen customer from the nasopharyngeal cancer screening request;

Extracting, from a plurality of predetermined service servers, various feature data corresponding to the customer attribute data of the to-be-screened client;

Performing feature tag analysis on the extracted feature data according to a preset feature tag extraction rule to analyze the feature tag of the to-screen customer;

Performing quantitative analysis on the predetermined significant feature label in the feature tag of the customer to be screened to determine the risk level of the nasopharyngeal cancer of the customer to be screened;

The warning information corresponding to the determined risk level of nasopharyngeal cancer is output.

A second aspect of the present application provides a method for alerting a risk of nasopharyngeal cancer, the method comprising the steps of:

A third aspect of the present application provides a computer readable storage medium storing a nasopharyngeal cancer risk warning system, the nasopharyngeal cancer risk warning system being executable by at least one processor to cause the At least one processor performs the following steps:

The technical solution of the present application, after receiving the nasopharyngeal cancer screening request of the client to be screened, obtains the characteristic label of the customer to be screened according to the nasopharyngeal cancer screening request, specifically screening directly from the nasopharyngeal cancer Obtaining a feature tag of the to-be-screened client in the request, and when the acquisition fails, acquiring various types of the customer attribute information from each predetermined service server according to the customer attribute information in the nasopharyngeal cancer screening request Feature data, and then extracting the feature tag of the customer to be screened from the feature data according to a preset extraction rule; and then predetermining the distinctive feature tag included in the feature tag (having a significant influence on nasopharyngeal cancer) The number of characteristic tags is analyzed to obtain a risk level of the nasopharyngeal cancer of the customer to be screened, and an early warning information corresponding to the obtained risk level of the nasopharyngeal cancer is output to the client. The application firstly uses the large amount of characteristic data of the customer to be screened to obtain the characteristic label, and then determines the risk level of the nasopharyngeal cancer of the customer according to the number of characteristic labels included in the characteristic label that have a great influence on the nasopharyngeal cancer, and gives The customer's corresponding warning information allows the customer to prevent or treat it as soon as possible, thus effectively improving the condition of nasopharyngeal cancer.

DRAWINGS

1 is a schematic flow chart of an embodiment of a method for early warning of nasopharyngeal cancer risk according to the present application;

2 is a schematic flow chart of a second embodiment of a method for early warning of nasopharyngeal cancer risk according to the present application;

3 is a schematic flow chart of a confirmation scheme of a predetermined distinctive feature label in the nasopharyngeal cancer risk warning method of the present application;

4 is a schematic diagram of an operating environment of an embodiment of a nasopharyngeal cancer risk warning system according to the present application;

5 is a program block diagram of an embodiment of a nasopharyngeal cancer risk warning system according to the present application;

6 is a block diagram showing the program of the second embodiment of the nasopharyngeal cancer risk warning system of the present application.

detailed description

The principles and features of the present application are described in the following with reference to the accompanying drawings, which are only used to explain the present application and are not intended to limit the scope of the application.

As shown in FIG. 1 , FIG. 1 is a schematic flow chart of an embodiment of a method for early warning of nasopharyngeal cancer risk according to the present application.

In this embodiment, the nasopharyngeal cancer risk warning method comprises:

Step S10, after receiving a nasopharyngeal cancer screening request of the client to be screened, obtaining a feature label of the customer to be screened from the nasopharyngeal cancer screening request;

The nasopharyngeal cancer screening request includes customer attribute data (eg, a certificate number, or a name and a document number) of the customer to be screened and/or a feature tag of the customer to be screened; wherein the feature tag includes basic feature information (eg, The city belongs to the northwestern part of China, the eating habits of the cities in which it belongs are spicy, the PM2.5 (fine particles) of the city is often exceeded, the bad weather in the city is too high, and the harmful gas positions are engaged, etc., and the preference information is preferred (for example, prefer to sleep late) , preference for tobacco and alcohol, preference for grilled food, preference for outdoor sports, etc.), behavioral information (for example, more games, more overtime, more take-outs, more medical visits, etc.) and/or social relationship information (for example, unmarried, Living alone, not always in contact with friends, etc.). After receiving the nasopharyngeal cancer screening request from the client to be screened, the screening server first checks whether the customer's feature tag is included in the request, that is, obtains the customer's feature tag from the client.

Step S20, if the feature tag fails to be obtained from the nasopharyngeal cancer screening request, obtaining the customer attribute data of the to-screen customer from the nasopharyngeal cancer screening request;

If the nasopharyngeal cancer screening request does not include the customer's feature tag, the feature tag is not obtained from the nasopharyngeal cancer screening request (ie, the feature tag is failed to be acquired), then the nasopharyngeal cancer screening is performed. The customer attribute data of the to-screen customer is obtained from the request, to obtain the feature label of the customer to be screened according to the customer attribute data of the screening customer.

Step S30, extracting, from a plurality of predetermined service servers, various feature data corresponding to the customer attribute data of the to-screen customer;

The screening server communicates with a plurality of predetermined business servers (eg, a bank server, a medical server, an insurance server, an instant messaging server, a game server, a weather server, a takeaway server, and/or a resume server, etc.); After the customer attribute data of the customer is to be screened, the screening server extracts various characteristic data corresponding to the customer attribute data of the to-screen customer from a plurality of predetermined business servers (for example, bank loan amount and repayment information) , outpatient medical record information "for example, the number of visits in a preset time, the type of disease, the duration of each illness, etc.", insurance information "for example, the industry, gender, age, marital status, occupation, etc." Information about the use of the instant messaging tool account "for example, information such as the communication tool daily login time information, daily online duration, etc.", game information "for example, daily game login time information, daily game online duration, etc.", weather information "for example , in the last three years, the number of days in which PM2.5 (fine particles) is seriously exceeded," Selling food information "for example, time of day information takeout point, the type of takeaway takeout every day, etc.", fill in the information on your resume, "for example, hobbies, personality, work experience and other information").

Step S40: Perform feature tag analysis on the extracted feature data according to a preset feature tag extraction rule to analyze the feature tag of the to-screen customer;

The extraction rule of the feature tag is preset in the screening server, and after extracting various feature data corresponding to the to-screen customer, the extraction rule is used to perform feature tag analysis on the extracted feature data, thereby analyzing The feature tag of the customer to be screened.

Step S50, performing quantitative analysis on the predetermined significant feature label in the feature tag of the customer to be screened, and determining the risk level of the nasopharyngeal cancer of the customer to be screened;

Which feature tags are pre-determined in the screening server as distinctive feature tags (ie, feature tags that have a significant impact on nasopharyngeal carcinoma), and the pre-determined salient feature tags are feature tags that have a significant impact on nasopharyngeal carcinoma; After the customer's characteristic label is to be screened, the predetermined significant characteristic label contained therein is quantitatively analyzed, and the nasopharyngeal cancer risk level of the customer to be screened is determined according to the quantitative analysis (for example, the nasopharyngeal cancer risk level includes low The higher the risk level of nasopharyngeal cancer, the higher the risk level of nasopharyngeal cancer, the greater the chance that the client to be screened may have nasopharyngeal cancer.

Step S60, outputting early warning information corresponding to the determined risk level of nasopharyngeal cancer.

The screening server outputs corresponding preset warning information (ie, some precautions and health advice, etc.) according to the determined risk level of nasopharyngeal cancer. For example, the warning information corresponding to the low-risk level can be “****** customers, without significant features of nasopharyngeal cancer. It is recommended to conduct a nasopharyngeal cancer risk screening every year, do more indoor aerobic exercise, and participate less. Strenuous exercise outdoors, go to bed early and get up early; the warning information corresponding to the medium risk level can be “****** customers, with significant features of nasopharyngeal cancer, suggest further medical examinations, do more indoor aerobic exercise Less participation in outdoor strenuous exercise, early sleep and get up early; the warning information corresponding to the high-risk level can be “****** customers, you are a high-risk target for nasopharyngeal cancer, and must do further medical examination within 7 days” ;and many more.

The technical solution of the embodiment, after receiving the nasopharyngeal cancer screening request of the client to be screened, according to the nasopharyngeal cancer screening request, obtaining the characteristic label of the customer to be screened, specifically directly screening from the nasopharyngeal cancer Obtaining a feature tag of the to-screen customer in the request, and, when the acquisition fails, acquiring, according to the customer attribute information in the nasopharyngeal cancer screening request, each of the customer attribute information corresponding to each of the predetermined service servers Characteristic data, and extracting the feature tag of the customer to be screened from the feature data according to a preset extraction rule; and then predetermining the distinctive feature tag included in the feature tag (having a significant influence on nasopharyngeal cancer) The number of characteristic tags is analyzed to obtain a risk level of the nasopharyngeal cancer of the customer to be screened, and an early warning information corresponding to the obtained risk level of the nasopharyngeal cancer is output to the client. In this embodiment, the feature tag is obtained according to a large amount of characteristic data of the customer to be screened, and the risk level of the nasopharyngeal cancer of the client is determined according to the number of feature tags included in the feature tag that have a great influence on the nasopharyngeal cancer. Give customers the corresponding warning information so that customers can prevent or treat them early, thus effectively improving the prevalence of nasopharyngeal cancer.

As shown in FIG. 2, FIG. 2 is a schematic flow chart of a second embodiment of the method for early warning of nasopharyngeal cancer risk.

In this embodiment, the nasopharyngeal cancer risk level includes a low risk level, a medium risk level, and a high risk level, and the step S50 includes:

Step S51, analyzing whether there is a predetermined significant feature tag in the feature tag of the to-be-screened client;

The screening server first determines whether the feature tag of the to-be-screened customer contains a predetermined significant feature tag based on all the distinctive feature tags determined in advance.

Step S52, if there is no predetermined significant feature label, determining that the nasopharyngeal cancer risk level of the customer to be screened is a low risk level;

If all of the feature tags of the customer to be screened do not include any of the predetermined distinctive feature tags, it is determined that the nasopharyngeal cancer risk level of the customer to be screened is a low risk level.

Step S53, if there is a predetermined significant feature tag, whether the number of the predetermined significant feature tags included in the analysis is greater than a preset number, or the number of predetermined significant feature tags included in the analysis accounts for all predetermined significant features. Whether the percentage of the total number of labels is greater than a preset percentage;

If it is determined that the feature tag of the to-be-screened client contains a predetermined significant feature tag, the risk level of the nasopharyngeal cancer is further determined according to the quantity of the predetermined significant feature tag included in the feature tag of the to-be-screened client; Specifically, comparing the number of the predetermined significant feature tags included in the feature tag of the to-screen customer to a preset number, or, by using the predetermined distinctive feature tag included in the feature tag of the to-screen customer The percentage of the number of all predetermined significant feature tags is compared to a preset percentage (eg, 80%).

Step S54, if the number of the predetermined significant feature tags is greater than the preset number, or the percentage of the predetermined significant feature tags is greater than the preset percentage by the total number of all the predetermined significant feature tags. Determining the risk level of nasopharyngeal cancer of the customer to be screened as a high risk level;

Step S55, if the number of the predetermined significant feature tags included is less than or equal to the preset number, or the percentage of the predetermined significant feature tags included in the total number of all the predetermined significant feature tags is less than or equal to the pre- If the percentage is set, it is determined that the risk level of the nasopharyngeal cancer of the customer to be screened is the medium risk level.

As shown in FIG. 3, FIG. 3 is a schematic flowchart of a confirmation scheme of a predetermined distinctive feature label in the present application, where the determining step of the predetermined significant feature label includes:

Step S1, selecting a first preset number of customers, and obtaining customer attribute data of each selected customer;

The screening server selects a first predetermined number (for example, 10,000) of customers from the customer database (selected customers are customers who have determined whether they have nasopharyngeal cancer), and obtains customer attribute information of the customers (for example, the ID number) , or, name and ID number).

Step S2, extracting various feature data corresponding to customer attribute data of each customer from a plurality of predetermined service servers;

For each of the first predetermined number of customers, the screening server extracts feature data corresponding to the customer from each of the plurality of predetermined service servers to extract each customer in each predetermined Characteristic data in the business server. The characteristic data is historical behavior data of the customer in the business server (for example, bank loan amount and repayment status information, outpatient medical record information, for example, the number of medical treatments in a preset time period, the type of disease, the disease each time) Duration, etc.", insurance information "for example, industry, gender, age, marital status, occupation, etc.", information on the use of instant messaging tool accounts "for example, information such as daily login time of communication tools, daily online duration, etc." , game information "for example, daily game landing time information, daily game online time and other information", weather information "for example, in the last three years, PM2.5 (fine particles) seriously exceeded the number of days", take-out order information "for example, every day The time information of the takeaway, the type of takeaway that is taken out every day, etc.", the information filled in the resume of the job search "for example, hobbies, personality, work experience, etc.").

Step S3, performing feature tag analysis on the extracted feature data according to a preset feature tag extraction rule, to analyze feature tags of each client;

After extracting various feature data of each customer, the screening server performs feature tag analysis on each feature data of each customer according to a preset extraction rule, thereby obtaining a feature tag of each customer. The feature tag includes basic feature information (for example, the city belongs to the northwestern region of China, the eating habits of the city to which it belongs is spicy, the PM2.5 (fine particles) of the city is often exceeded, the bad weather in the city is too high, and the hazardous gas positions are engaged, etc.) Habit information (for example, preference for late sleep, preference for tobacco and alcohol, preference for grilled food, preference for outdoor sports, etc.), behavioral information (eg, more games, more overtime, more take-outs, more medical visits, etc.) and/or Social relationship information (for example, unmarried, living alone, not always in contact with friends, etc.).

Step S4, determining, according to a predetermined mapping relationship between the nasopharyngeal cancer and the customer attribute data, an abnormal customer having a nasopharyngeal cancer and a normal client not having a nasopharyngeal cancer among the first predetermined number of customers;

The customer database records the disease label of each customer. The screening server can determine which of the first preset number of customers is an abnormal customer with nasopharyngeal cancer, and which is the customer based on the customer's customer attribute data. A normal client who does not have nasopharyngeal cancer.

Step S5: using the feature tag corresponding to each normal client and the feature tag corresponding to each abnormal client as a training sample of the preset salient feature analysis model, and training the salient feature analysis model by using each of the training samples to determine various types. Ranking of the importance of feature tags in the salient feature analysis model;

That is, all the feature tags corresponding to each normal client are used as a training sample, and all the feature tags corresponding to each abnormal client are used as a training sample, and the feature tags corresponding to the normal client are corresponding to the characteristics of the abnormal customer without nasopharyngeal cancer. The label corresponds to nasopharyngeal carcinoma, so that there is a first predetermined number of training samples to train the preset salient analysis model, and the order of importance of each feature label in the salient feature analysis model is obtained through training. Preferably, the salient feature analysis model used in the embodiment is a gradient promotion decision tree model, and the training step of the salient feature analysis model includes: training the salient feature analysis model by using each of the training samples to construct multiple trees. Iterative decision tree, select the top N decision trees with the highest accuracy as the final training result of the model; output the importance order of all feature tags in the salient feature analysis model according to the selected decision tree (if a feature tag and whether or not If the disease is irrelevant, it is not included in the decision tree model).

In step S6, various feature tags are subjected to preset type analysis according to the order of importance in the salient feature analysis model, and a significant feature tag having a significant influence on nasopharyngeal carcinoma is analyzed.

The preset type analysis, for example, BI (Business Intelligence) analysis, can verify whether the different values of each feature tag have a significant difference in the prevalence of nasopharyngeal cancer by BI analysis, if a The difference in the value of the characteristic label has a significant difference in the prevalence of nasopharyngeal carcinoma, and it is determined that the characteristic label is a significant characteristic label.

The feature tag extraction rule preset in the nasopharyngeal cancer risk warning method of the present application is: setting a corresponding tag threshold for various feature data types of consecutive values;

For example, the labeling threshold of the number of days in which the city PM2.5 (fine particles) exceeds the standard in the last three years may be 60 days. When the number of days in which the city PM2.5 has exceeded the standard in the last three years is greater than the corresponding label threshold “60 days” On behalf of the city, PM2.5 often exceeds the standard; the tag threshold corresponding to the number of days above the blue warning level of the city in the last three years may be 55 days. When the city is in the last three years, the number of days above the blue warning level is greater than the corresponding number. When the threshold is "55 days", it means that the bad weather in the city is too much; the number of days in the last year after sleeping at 23:00 in the last year may be 100 days. When the number of days in the last year is more than 23:00, the number of days is greater than the corresponding one. When the label threshold is "100 days", it means that it is preferred to sleep late; the labeling threshold of the number of take-outs of the barbecue in the most recent year may be 80 times. When the number of take-outs of the barbecue in the most recent year is greater than the corresponding label threshold "80 times" At the time, it means that the barbecue food is preferred; the label threshold corresponding to the number of medical treatments in the last year may be 30 times, when the number of medical treatments in the most recent year is greater than the corresponding label threshold "30 times" , representing the number of medical treatments.

Setting a corresponding label range for various feature data types that are not continuous values;

For example, the label range corresponding to the city includes: a collection of cities in the northwest region, a collection of cities in the North China region, a collection of cities in the Central China region, a collection of cities in the South China region, etc., when the city to which the customer belongs belongs to the city in the northwest region city collection, The label information corresponding to the city to which the customer belongs is “belonging to Northwest China”; the label range corresponding to the eating habits of the city includes: spicy city collection, partial greasy city collection, partial light city collection, partial sweet/salty city combination, etc. When the city belongs to the city in the spicy city collection, the label information corresponding to the city to which the customer belongs is “the eating habit is spicy”; the label range corresponding to the position includes: the collection of harmful gas positions, non-volatile and harmful The collection of gas positions, the collection of positions that are prone to generate volatile harmful gases, and the collection of harmless gas positions, etc. When the customer is engaged in a post belonging to a collection of hazardous gas positions, the label information corresponding to the industry in which the customer is engaged is “working with harmful gases”. post".

According to the mapping relationship between various characteristic data types and tag thresholds of continuous values, the tag information corresponding to the feature data of various continuous values of each client is determined, and the mapping relationship between various feature data types and tag ranges according to non-continuous values is determined. The tag information corresponding to the feature data of various non-continuous values of each client is determined.

In addition, the present application also proposes a nasopharyngeal cancer risk warning system.

Please refer to FIG. 4 , which is a schematic diagram of an operating environment of an embodiment of the nasopharyngeal cancer risk warning system 10 of the present application.

In the present embodiment, the nasopharyngeal cancer risk warning system 10 is installed and operated in the electronic device 1. The electronic device 1 may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a server. The electronic device 1 may include, but is not limited to, a memory 11, a processor 12, and a display 13. Figure 4 shows only the electronic device 1 with components 11-13, but it should be understood that not all illustrated components may be implemented, and more or fewer components may be implemented instead.

The memory 11 may be an internal storage unit of the electronic device 1 in some embodiments, such as a hard disk or memory of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in hard disk equipped on the electronic device 1, a smart memory card (SMC), and a secure digital (SD). Card, flash card, etc. Further, the memory 11 may also include both an internal storage unit of the electronic device 1 and an external storage device. The memory 11 is used to store application software and various types of data installed in the electronic device 1, such as program codes of the nasopharyngeal cancer risk warning system 10. The memory 11 can also be used to temporarily store data that has been output or is about to be output.

The processor 12, in some embodiments, may be a Central Processing Unit (CPU), microprocessor or other data processing chip for running program code or processing data stored in the memory 11, such as performing nasopharyngeal carcinoma Risk warning system 10, etc.

The display 13 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch sensor, or the like in some embodiments. The display 13 is for displaying information processed in the electronic device 1 and a user interface for displaying visualization. The components 11-13 of the electronic device 1 communicate with one another via a system bus.

Please refer to FIG. 5, which is a program block diagram of a preferred embodiment of the nasopharyngeal cancer risk warning system 10 of the present application. In the present embodiment, the nasopharyngeal cancer risk warning system 10 can be divided into one or more modules, one or more modules being stored in the memory 11 and being processed by one or more processors (this embodiment is a processor) 12) Executed to complete the application. For example, in FIG. 5, the nasopharyngeal cancer risk warning system 10 can be divided into a first acquisition module 101, a second acquisition module 102, an extraction module 103, a first analysis module 104, a second analysis module 105, and an early warning output module 106. . The module referred to in the present application refers to a series of computer program instruction segments capable of performing a specific function, and is more suitable than the program for describing the execution process of the nasopharyngeal cancer risk warning system 10 in the electronic device 1, wherein:

The first obtaining module 101 is configured to obtain, after receiving a nasopharyngeal cancer screening request of the client to be screened, the feature label of the to-screen customer from the nasopharyngeal cancer screening request;

The second obtaining module 102 is configured to obtain, after the failure of acquiring the feature tag from the nasopharyngeal cancer screening request, the customer attribute data of the to-screen customer to be obtained from the nasopharyngeal cancer screening request;

The extracting module 103 is configured to extract, from a plurality of predetermined service servers, various feature data corresponding to the customer attribute data of the to-be-screened client;

The first analysis module 104 is configured to perform feature tag analysis on the extracted feature data according to a preset feature tag extraction rule to analyze the feature tag of the to-screen customer;

The second analysis module 105 is configured to perform quantity analysis on the predetermined significant feature label in the feature tag of the customer to be screened to determine the risk level of the nasopharyngeal cancer of the customer to be screened;

The warning output module 106 is configured to output early warning information corresponding to the determined risk level of nasopharyngeal cancer.

The screening server outputs corresponding preset warning information (ie, some precautions and health advice, etc.) based on the determined risk level of nasopharyngeal cancer. For example, the warning information corresponding to the low-risk level can be “****** customers, without significant features of nasopharyngeal cancer. It is recommended to conduct a nasopharyngeal cancer risk screening every year, do more indoor aerobic exercise, and participate less. Strenuous exercise outdoors, go to bed early and get up early; the warning information corresponding to the medium risk level can be “****** customers, with significant features of nasopharyngeal cancer, suggest further medical examinations, do more indoor aerobic exercise Less participation in outdoor strenuous exercise, early sleep and get up early; the warning information corresponding to the high-risk level can be “****** customers, you are a high-risk target for nasopharyngeal cancer, and must do further medical examination within 7 days” ;and many more.

As shown in FIG. 6, FIG. 6 is a program block diagram of a second embodiment of the nasopharyngeal cancer risk warning system of the present application.

In this embodiment, the nasopharyngeal cancer risk level includes a low risk level, a medium risk level, and a high risk level, and the second analysis module 105 includes:

The first analysis sub-module 1051 is configured to analyze whether there is a predetermined significant feature tag in the feature tag of the to-be-screened client;

The determining sub-module 1052 is configured to determine that the nasopharyngeal cancer risk level of the to-screen customer is a low risk level when there is no predetermined significant feature label in the feature tag of the to-screen customer;

The second analysis sub-module 1053 is configured to analyze whether the quantity of the predetermined significant feature tag included in the feature tag of the to-be-screened client has a predetermined number of significant feature tags, or Whether the predetermined number of significant feature tags accounts for a percentage of the total number of all predetermined significant feature tags is greater than a preset percentage;

The determining sub-module 1052 is further configured to: when the number of predetermined significant feature tags included is greater than a preset number, or the number of predetermined significant feature tags included in the total number of all the predetermined significant feature tags When the percentage is greater than the preset percentage, it is determined that the risk level of the nasopharyngeal cancer of the customer to be screened is a high risk level;

The determining sub-module 1052 is further configured to: when the number of the predetermined distinctive feature tags included is less than or equal to the preset number, or the total number of the predetermined significant feature tags included in the total of all the predetermined salient feature tags When the percentage of the quantity is less than or equal to the preset percentage, it is determined that the risk level of the nasopharyngeal cancer of the customer to be screened is the medium risk level.

In this embodiment, the determining the predetermined distinctive feature label includes the following steps:

1. Selecting a first preset number of customers, and obtaining customer attribute data of each selected customer;

2. Extracting various feature data corresponding to the customer attribute data of each customer from a plurality of predetermined service servers;

3. Perform feature tag analysis on the extracted feature data according to the preset feature tag extraction rule to analyze the feature tags of each client;

4. determining, according to a predetermined mapping relationship between the nasopharyngeal carcinoma and the customer attribute data, an abnormal customer having a nasopharyngeal cancer and a normal client not having a nasopharyngeal cancer among the first predetermined number of customers;

5. The feature tag corresponding to each normal customer and the feature tag corresponding to each abnormal customer are used as training samples of the preset salient feature analysis model, and the salient feature analysis model is trained by using each of the training samples to determine various features. The order of importance of the labels in the salient feature analysis model;

6. Performing a preset type analysis on various feature tags according to the order of importance in the significant feature analysis model, and analyzing the significant feature tags having significant influence on nasopharyngeal carcinoma.

In the nasopharyngeal cancer risk early warning system of the present application, the preset feature label extraction rule is: setting a corresponding label threshold for various characteristic data types of continuous values;

For example, the labeling threshold of the number of days in which the city PM2.5 (fine particles) exceeds the standard in the last three years may be 60 days. When the number of days in which the city PM2.5 has exceeded the standard in the last three years is greater than the corresponding label threshold “60 days” On behalf of the city, PM2.5 often exceeds the standard; the tag threshold corresponding to the number of days above the blue warning level of the city in the last three years may be 55 days. When the city is in the last three years, the number of days above the blue warning level is greater than the corresponding number. When the threshold is "55 days", it means that the bad weather in the city is too much; the number of days in the last year after sleeping at 23:00 in the last year may be 100 days. When the number of days in the last year is more than 23:00, the number of days is greater than the corresponding one. When the label threshold is "100 days", it means that it is preferred to sleep late; the labeling threshold of the number of take-outs of the barbecue in the most recent year may be 80 times. When the number of take-outs of the barbecue in the most recent year is greater than the corresponding label threshold "80 times" At the time, the representative prefers barbecue food; the label threshold corresponding to the number of medical treatments in the last year may be 30 times, when the number of medical treatments in the most recent year is greater than the corresponding label threshold "30 times" , The number of medical representatives and more.

Further, the present application further provides a computer readable storage medium storing a nasopharyngeal cancer risk warning system, the nasopharyngeal cancer risk warning system being executable by at least one processor to The at least one processor executes the nasopharyngeal cancer risk warning method in any of the above embodiments.

The above description is only a preferred embodiment of the present application, and is not intended to limit the scope of the patents of the present application, and the equivalent structural transformation, or direct/indirect use, of the present application and the contents of the drawings is used in the present invention. All other related technical fields are included in the patent protection scope of the present application.

Claims

An electronic device, comprising: a memory and a processor, wherein the memory stores a nasopharyngeal cancer risk warning system operable on the processor, the nasopharyngeal cancer risk warning system is The processor implements the following steps when executed:

After receiving a nasopharyngeal cancer screening request from a client to be screened, obtaining a characteristic label of the customer to be screened from the nasopharyngeal cancer screening request;

If the feature tag fails to be obtained from the nasopharyngeal cancer screening request, obtaining the customer attribute data of the to-screen customer from the nasopharyngeal cancer screening request;

Extracting, from a plurality of predetermined service servers, various feature data corresponding to the customer attribute data of the to-be-screened client;

Performing feature tag analysis on the extracted feature data according to a preset feature tag extraction rule to analyze the feature tag of the to-screen customer;

Performing quantitative analysis on the predetermined significant feature label in the feature tag of the customer to be screened to determine the risk level of the nasopharyngeal cancer of the customer to be screened;

The warning information corresponding to the determined risk level of nasopharyngeal cancer is output.
The electronic device according to claim 1, wherein the preset feature tag extraction rule is:

Setting a corresponding label threshold for various feature data types of consecutive values;

Setting a corresponding label range for various feature data types that are not continuous values;

According to the mapping relationship between various characteristic data types and tag thresholds of continuous values, the tag information corresponding to the feature data of various continuous values of each client is determined, and the mapping relationship between various feature data types and tag ranges according to non-continuous values is determined. The tag information corresponding to the feature data of various non-continuous values of each client is determined.
The electronic device according to claim 1, wherein said nasopharyngeal cancer risk level comprises a low risk level, a medium risk level, and a high risk level, said predetermined one of said feature tags of said to be screened customer A quantitative analysis of the characteristic signatures to determine the risk level of the nasopharyngeal cancer of the client to be screened includes:

Analyzing whether there is a predetermined significant feature tag in the feature tag of the to-be-screened client;

If there is no predetermined significant feature label, determining that the nasopharyngeal cancer risk level of the customer to be screened is a low risk level;

If there is a predetermined significant feature tag, the analysis contains whether the number of predetermined significant feature tags is greater than a preset number, or the analysis contains the total number of predetermined significant feature tags as a total of all predetermined significant feature tags. Whether the percentage of the quantity is greater than the preset percentage;

If the number of predetermined significant feature tags is greater than a preset number, or if the number of predetermined significant feature tags is greater than a predetermined percentage of the total number of all predetermined significant feature tags, then the Screen the client's nasopharyngeal cancer risk rating to a high risk level;

If the number of predetermined significant feature tags is less than or equal to the preset number, or the percentage of the predetermined significant feature tags included in the total number of all the predetermined significant feature tags is less than or equal to the preset percentage, Then, the risk level of the nasopharyngeal cancer of the customer to be screened is determined to be a medium risk level.
The electronic device according to claim 3, wherein the preset feature tag extraction rule is:

Setting a corresponding label threshold for various feature data types of consecutive values;

Setting a corresponding label range for various feature data types that are not continuous values;

According to the mapping relationship between various characteristic data types and tag thresholds of continuous values, the tag information corresponding to the feature data of various continuous values of each client is determined, and the mapping relationship between various feature data types and tag ranges according to non-continuous values is determined. The tag information corresponding to the feature data of various non-continuous values of each client is determined.
The electronic device of claim 1, wherein the determining the predetermined distinctive feature tag comprises:

Selecting a first preset number of customers, and obtaining customer attribute data of each selected customer;

Extracting various feature data corresponding to customer attribute data of each customer from a plurality of predetermined service servers;

According to the preset feature tag extraction rule, feature tag analysis is performed on the extracted feature data to analyze the feature tags of each client;

Determining an abnormal customer having nasopharyngeal cancer and a normal client not having nasopharyngeal cancer among the first predetermined number of customers according to a predetermined mapping relationship between the nasopharyngeal cancer and the customer attribute data;

Using the feature tag corresponding to each normal customer and the feature tag corresponding to each abnormal customer as the training sample of the preset salient feature analysis model, the salient feature analysis model is trained by using each of the training samples to determine that various feature tags are in the Ranking of importance in the salient feature analysis model;

A pre-set type analysis was performed on various feature tags according to the order of importance in the salient feature analysis model, and a significant feature tag having a significant influence on nasopharyngeal carcinoma was analyzed.
The electronic device according to claim 5, wherein the preset feature tag extraction rule is:

Setting a corresponding label threshold for various feature data types of consecutive values;

Setting a corresponding label range for various feature data types that are not continuous values;

According to the mapping relationship between various characteristic data types and tag thresholds of continuous values, the tag information corresponding to the feature data of various continuous values of each client is determined, and the mapping relationship between various feature data types and tag ranges according to non-continuous values is determined. The tag information corresponding to the feature data of various non-continuous values of each client is determined.
The electronic device according to claim 5, wherein the salient feature analysis model is a gradient promotion decision tree model, and the training steps of the salient feature analysis model include:

The salient feature analysis model is trained by using each of the training samples, and multiple iterative decision trees are constructed, and the top N decision trees with the highest accuracy rate are selected as the final training result of the model;

An order of importance of all feature tags in the salient feature analysis model is output according to the selected decision tree.
The electronic device according to claim 7, wherein the preset feature tag extraction rule is:

Setting a corresponding label threshold for various feature data types of consecutive values;

Setting a corresponding label range for various feature data types that are not continuous values;

According to the mapping relationship between various characteristic data types and tag thresholds of continuous values, the tag information corresponding to the feature data of various continuous values of each client is determined, and the mapping relationship between various feature data types and tag ranges according to non-continuous values is determined. The tag information corresponding to the feature data of various non-continuous values of each client is determined.
A method for warning a risk of nasopharyngeal cancer, characterized in that the method comprises the steps of:

After receiving a nasopharyngeal cancer screening request from a client to be screened, obtaining a characteristic label of the customer to be screened from the nasopharyngeal cancer screening request;

If the feature tag fails to be obtained from the nasopharyngeal cancer screening request, obtaining the customer attribute data of the to-screen customer from the nasopharyngeal cancer screening request;

Extracting, from a plurality of predetermined service servers, various feature data corresponding to the customer attribute data of the to-be-screened client;

Performing feature tag analysis on the extracted feature data according to a preset feature tag extraction rule to analyze the feature tag of the to-screen customer;

Performing quantitative analysis on the predetermined significant feature label in the feature tag of the customer to be screened to determine the risk level of the nasopharyngeal cancer of the customer to be screened;

The warning information corresponding to the determined risk level of nasopharyngeal cancer is output.
The nasopharyngeal cancer risk warning method according to claim 9, wherein the preset feature label extraction rule is:

Setting a corresponding label threshold for various feature data types of consecutive values;

Setting a corresponding label range for various feature data types that are not continuous values;

According to the mapping relationship between various characteristic data types and tag thresholds of continuous values, the tag information corresponding to the feature data of various continuous values of each client is determined, and the mapping relationship between various feature data types and tag ranges according to non-continuous values is determined. The tag information corresponding to the feature data of various non-continuous values of each client is determined.
The nasopharyngeal cancer risk warning method according to claim 9, wherein the nasopharyngeal cancer risk level comprises a low risk level, a medium risk level and a high risk level, wherein the characteristic tag of the customer to be screened is The quantitative analysis of the predetermined distinctive feature tags for determining the risk level of the nasopharyngeal cancer of the client to be screened includes:

Analyzing whether there is a predetermined significant feature tag in the feature tag of the to-be-screened client;

If there is no predetermined significant feature label, determining that the nasopharyngeal cancer risk level of the customer to be screened is a low risk level;

If there is a predetermined significant feature tag, the analysis contains whether the number of predetermined significant feature tags is greater than a preset number, or the analysis contains the total number of predetermined significant feature tags as a total of all predetermined significant feature tags. Whether the percentage of the quantity is greater than the preset percentage;

If the number of predetermined significant feature tags is greater than a preset number, or if the number of predetermined significant feature tags is greater than a predetermined percentage of the total number of all predetermined significant feature tags, then the Screen the client's nasopharyngeal cancer risk rating to a high risk level;

If the number of predetermined significant feature tags is less than or equal to the preset number, or the percentage of the predetermined significant feature tags included in the total number of all the predetermined significant feature tags is less than or equal to the preset percentage, Then, the risk level of the nasopharyngeal cancer of the customer to be screened is determined to be a medium risk level.
The nasopharyngeal cancer risk warning method according to claim 11, wherein the preset feature label extraction rule is:

Setting a corresponding label threshold for various feature data types of consecutive values;

Setting a corresponding label range for various feature data types that are not continuous values;

According to the mapping relationship between various characteristic data types and tag thresholds of continuous values, the tag information corresponding to the feature data of various continuous values of each client is determined, and the mapping relationship between various feature data types and tag ranges according to non-continuous values is determined. The tag information corresponding to the feature data of various non-continuous values of each client is determined.
The nasopharyngeal cancer risk warning method according to claim 9, wherein the determining step of the predetermined significant feature label comprises:

Selecting a first preset number of customers, and obtaining customer attribute data of each selected customer;

Extracting various feature data corresponding to customer attribute data of each customer from a plurality of predetermined service servers;

According to the preset feature tag extraction rule, feature tag analysis is performed on the extracted feature data to analyze the feature tags of each client;

Determining an abnormal customer having nasopharyngeal cancer and a normal client not having nasopharyngeal cancer among the first predetermined number of customers according to a predetermined mapping relationship between the nasopharyngeal cancer and the customer attribute data;

Using the feature tag corresponding to each normal customer and the feature tag corresponding to each abnormal customer as the training sample of the preset salient feature analysis model, the salient feature analysis model is trained by using each of the training samples to determine that various feature tags are in the Ranking of importance in the salient feature analysis model;

A pre-set type analysis was performed on various feature tags according to the order of importance in the salient feature analysis model, and a significant feature tag having a significant influence on nasopharyngeal carcinoma was analyzed.
The nasopharyngeal cancer risk warning method according to claim 13, wherein the preset feature label extraction rule is:

Setting a corresponding label threshold for various feature data types of consecutive values;

Setting a corresponding label range for various feature data types that are not continuous values;

According to the mapping relationship between various characteristic data types and tag thresholds of continuous values, the tag information corresponding to the feature data of various continuous values of each client is determined, and the mapping relationship between various feature data types and tag ranges according to non-continuous values is determined. The tag information corresponding to the feature data of various non-continuous values of each client is determined.
The nasopharyngeal cancer risk warning method according to claim 13, wherein the salient feature analysis model is a gradient elevation decision tree model, and the training steps of the salient feature analysis model include:

The salient feature analysis model is trained by using each of the training samples, and multiple iterative decision trees are constructed, and the top N decision trees with the highest accuracy rate are selected as the final training result of the model;

An order of importance of all feature tags in the salient feature analysis model is output according to the selected decision tree.
The nasopharyngeal cancer risk warning method according to claim 15, wherein the preset feature label extraction rule is:

Setting a corresponding label threshold for various feature data types of consecutive values;

Setting a corresponding label range for various feature data types that are not continuous values;

According to the mapping relationship between various characteristic data types and tag thresholds of continuous values, the tag information corresponding to the feature data of various continuous values of each client is determined, and the mapping relationship between various feature data types and tag ranges according to non-continuous values is determined. The tag information corresponding to the feature data of various non-continuous values of each client is determined.
A computer readable storage medium storing a nasopharyngeal cancer risk warning system, the nasopharyngeal cancer risk warning system being executable by at least one processor to cause the at least one The processor performs the following steps:

After receiving a nasopharyngeal cancer screening request from a client to be screened, obtaining a characteristic label of the customer to be screened from the nasopharyngeal cancer screening request;

If the feature tag fails to be obtained from the nasopharyngeal cancer screening request, obtaining the customer attribute data of the to-screen customer from the nasopharyngeal cancer screening request;

Extracting, from a plurality of predetermined service servers, various feature data corresponding to the customer attribute data of the to-be-screened client;

Performing feature tag analysis on the extracted feature data according to a preset feature tag extraction rule to analyze the feature tag of the to-screen customer;

Performing quantitative analysis on the predetermined significant feature label in the feature tag of the customer to be screened to determine the risk level of the nasopharyngeal cancer of the customer to be screened;

The warning information corresponding to the determined risk level of nasopharyngeal cancer is output.
The computer readable storage medium of claim 17, wherein the nasopharyngeal cancer risk level comprises a low risk level, a medium risk level, and a high risk level, wherein the feature tag of the customer to be screened The quantitative analysis of the pre-determined distinctive feature tags to determine the risk level of the nasopharyngeal cancer of the client to be screened includes:

Analyzing whether there is a predetermined significant feature tag in the feature tag of the to-be-screened client;

If there is no predetermined significant feature label, determining that the nasopharyngeal cancer risk level of the customer to be screened is a low risk level;

If there is a predetermined significant feature tag, the analysis contains whether the number of predetermined significant feature tags is greater than a preset number, or the analysis contains the total number of predetermined significant feature tags as a total of all predetermined significant feature tags. Whether the percentage of the quantity is greater than the preset percentage;

If the number of predetermined significant feature tags is greater than a preset number, or if the number of predetermined significant feature tags is greater than a predetermined percentage of the total number of all predetermined significant feature tags, then the Screen the client's nasopharyngeal cancer risk rating to a high risk level;

If the number of predetermined significant feature tags is less than or equal to the preset number, or the percentage of the predetermined significant feature tags included in the total number of all the predetermined significant feature tags is less than or equal to the preset percentage, Then, the risk level of the nasopharyngeal cancer of the customer to be screened is determined to be a medium risk level.
The computer readable storage medium of claim 17, wherein the determining the predetermined distinctive feature tag comprises:

Selecting a first preset number of customers, and obtaining customer attribute data of each selected customer;

Extracting various feature data corresponding to customer attribute data of each customer from a plurality of predetermined service servers;

According to the preset feature tag extraction rule, feature tag analysis is performed on the extracted feature data to analyze the feature tags of each client;

Determining an abnormal customer having nasopharyngeal cancer and a normal client not having nasopharyngeal cancer among the first predetermined number of customers according to a predetermined mapping relationship between the nasopharyngeal cancer and the customer attribute data;

Using the feature tag corresponding to each normal customer and the feature tag corresponding to each abnormal customer as the training sample of the preset salient feature analysis model, the salient feature analysis model is trained by using each of the training samples to determine that various feature tags are in the Ranking of importance in the salient feature analysis model;

A pre-set type analysis was performed on various feature tags according to the order of importance in the salient feature analysis model, and a significant feature tag having a significant influence on nasopharyngeal carcinoma was analyzed.
The computer readable storage medium of claim 17, wherein the predetermined feature tag extraction rule is:

Setting a corresponding label threshold for various feature data types of consecutive values;

Setting a corresponding label range for various feature data types that are not continuous values;

According to the mapping relationship between various characteristic data types and tag thresholds of continuous values, the tag information corresponding to the feature data of various continuous values of each client is determined, and the mapping relationship between various feature data types and tag ranges according to non-continuous values is determined. The tag information corresponding to the feature data of various non-continuous values of each client is determined.