CN115482916A - Product service recommendation method based on intelligent medical big data and intelligent medical system - Google Patents
Product service recommendation method based on intelligent medical big data and intelligent medical system Download PDFInfo
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Abstract
The invention discloses a product service recommendation method based on smart medical big data and a smart medical system, which comprise a medical data acquisition module, a medical data analysis module and a factor influence recommendation module, wherein the medical data acquisition module is used for acquiring medical information and patient information in a medical platform, the medical data analysis module is used for analyzing the acquired data information, the factor influence recommendation module is used for recommending medical services to patients according to different influence factors, the factor influence recommendation module is connected with the medical data analysis module through a network, the medical data acquisition module comprises a medical information input module, a behavior data acquisition module and a patient information acquisition module, and the medical data analysis module comprises a service quality analysis module, a service process information analysis module and a situation factor analysis module.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a product service recommendation method based on intelligent medical big data and an intelligent medical system.
Background
In the medical field, patients obtain high-quality on-line medical consultation services by paying, doctors obtain income and reputation by providing high-quality on-line services, and the rapid increase in demand and supply promotes the explosion of the on-line pay medical consultation service market. The online medical consultation service has the advantages of being free from time and space constraints, but has the disadvantages of low communication information transmission efficiency, difficulty in realizing medical examination and the like, and a patient faces to the selection of the online medical consultation service and the offline medical consultation service; meanwhile, in the process of selecting the online medical consultation service by the patient, the online medical consultation service is different from online purchase and offline consumption of the physical commodity, the online medical consultation service is online purchase and online consumption, the medical consultation service has the attribute of a trusting article, even if a consumer consumes the service, the quality of the service is difficult to judge, and the information asymmetry between the buyer and the seller is more serious. Therefore, it is necessary to design a product service recommendation method and an intelligent medical system based on intelligent medical big data for improving an intelligent recommendation result and optimizing diagnosis and treatment efficiency.
Disclosure of Invention
The invention aims to provide a product service recommendation method based on intelligent medical big data and an intelligent medical system, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: the product service recommendation method based on the smart medical big data comprises a medical data acquisition module, a medical data analysis module and a factor influence recommendation module, wherein the medical data acquisition module is used for acquiring medical information and patient information in a medical platform, the medical data analysis module is used for analyzing the acquired data information, the factor influence recommendation module is used for recommending medical services to patients according to different influence factors, the medical data analysis module is in network connection with the medical data acquisition module, and the factor influence recommendation module is in network connection with the medical data analysis module.
According to the technical scheme, the medical data acquisition module comprises a medical information input module, a behavior data acquisition module and a patient information acquisition module, the medical information input module is used for inputting medical information in the medical platform, the behavior data acquisition module is used for acquiring patient behavior information and doctor behavior information in the medical platform, and the patient information acquisition module is used for acquiring basic information registered by a patient when the patient visits the platform.
According to the technical scheme, the medical data analysis module comprises a service quality analysis module, a service process information analysis module and a situation factor analysis module, the service quality analysis module is used for analyzing the service quality in the medical platform, the service process information analysis module is used for analyzing the medical service process quality, and the situation factor analysis module is used for analyzing different situation factors.
According to the technical scheme, the factor influence recommendation module comprises a parameter selection module, a preliminary screening module and a classification recommendation module, wherein the parameter selection module is used for enabling a patient to independently select basic parameters, the preliminary screening module is used for preliminarily screening medical service information, and the classification recommendation module is used for carrying out classification recommendation on medical services.
According to the technical scheme, the product service recommendation method of the intelligent medical system comprises the following steps:
step S1: establishing an information database through an intelligent medical system, performing data base processing on data information required by a recommendation method, and storing records;
step S2: the system acquires behavior data information on each medical platform and basic personal information registered when a patient performs medical service on line through big data, and stores the behavior data information and the basic personal information into the database for further analysis and processing;
and step S3: further according to the acquired information, integrating and analyzing information parameters required to be considered in the process of selecting medical services by the patient;
and step S4: when a patient logs in a medical platform to perform on-line medical service, parameter selection is performed according to service contents to be searched, preliminary screening is performed after the selection is completed, and further selectable medical service contents are displayed for the patient according to the information analysis result.
According to the above technical solution, the data base processing in step S1 specifically includes: and establishing a formatting algorithm object, sequentially reading the transmitted data, performing formatting analysis on the transmitted data, modifying and mending missing data and error data, setting a uniform formatting template, and further performing text processing on the illness state consultation text submitted by the patient, wherein the text processing comprises text word segmentation, noise word and symbol removal and word frequency value calculation.
According to the above technical solution, the step S3 further includes the steps of:
step S31: counting the price of the online consultation service of the doctor, wherein the online consultation service is divided into limited consultation service and unlimited consultation service, and the average value of the highest price and the lowest price is used as a reference amount; and analyzing the reputation of the doctor based on the patient evaluation accumulated by the doctor through long-term online and offline service for the patient, and recording the service quality as
In order to be the reputation of the doctor,
a service price;
step S32: the information quantity N of the doctor on-line consultation reply is counted according to the database records, the information richness of the consultation content is judged according to the information quantity and time for solving the specific problems of the patient in the specific information content, the information richness is used as the reference quantity of service timeliness, and the quality of the service process is recorded as
N is the total amount of information,
timeliness for service;
step S33: further considering situation factors, namely the competition degree of the service market and the disease risk degree of the patient, counting the number of doctors for managing various diseases, taking the number as the reference quantity of the competition degree of the service market, and judging the disease risk degree according to the disease information input when the patient consults.
According to the above technical solution, the step S3 further includes the steps of:
step S31: counting the price of the online consultation service of the doctor, wherein the online consultation service is divided into limited consultation service and unlimited consultation service, and the average value of the highest price and the lowest price is taken as a reference amount; and analyzing the reputation of the doctor based on the patient evaluation accumulated by the doctor through long-term online and offline service for the patient, and recording the service quality as
In order to be the reputation of the doctor,
as a service price;
step S32: the information quantity N of the doctor on-line consultation reply is counted according to the database records, the information richness of the consultation content is judged according to the information quantity and time for solving the specific problems of the patient in the specific information content, the information richness is used as the reference quantity of service timeliness, and the quality of the service process is recorded as
N is the total amount of information,
the service is time-sensitive;
step S33: further considering situation factors, namely the competition degree of the service market and the disease risk degree of the patient, counting the number of doctors for managing various diseases, taking the number as the reference quantity of the competition degree of the service market, and judging the disease risk degree according to the disease information input when the patient consults.
According to the above technical solution, the step S4 further comprises the steps of:
step S41: after logging in a medical platform, a patient firstly intelligently recommends optional service options according to personal information and whether the optional service options are required or not according to whether the optional service options are authorized or not in personal registration;
step S42: after authorization, the system accesses the registration information of the patient and calls a history record, and judges whether the patient is the first purchase of online service or repeated purchase, whether the residence of the patient is local or off-site, and whether the patient needs to see a doctor online or ask a doctor offline;
step S43: if offline inquiry is selected, the system can recommend a nearby hospital to the patient according to the distance between the residence of the patient and each hospital and provide online reservation service according to the illness state information;
step S44: and selecting on-line consultation, scanning the disease condition information, screening out doctors capable of processing the consultation according to the keywords, judging the service market competitiveness and the risk degree of the disease, and recommending the service to the patient according to different factors.
According to the above technical solution, the step S4 further comprises the steps of:
step S41: after logging in a medical platform, a patient firstly intelligently recommends optional service options according to personal information and whether the optional service options are required or not according to whether the optional service options are authorized or not in personal registration;
step S42: after authorization, the system accesses the registration information of the patient and calls a history record, and judges whether the patient is the first purchase of online service or repeated purchase, whether the residence of the patient is local or off-site, and whether the patient needs to see a doctor online or ask a doctor offline;
step S43: if off-line inquiry is selected, the system can recommend a nearby hospital to the patient according to the distance between the residence of the patient and each hospital and provide on-line reservation service according to the illness state information;
step S44: and selecting on-line consultation, scanning the disease condition information, screening out doctors capable of processing the consultation according to the keywords, judging the service market competitiveness and the risk degree of the disease, and recommending the service to the patient according to different factors.
Compared with the prior art, the invention has the following beneficial effects: according to the invention, the medical data acquisition module, the medical data analysis module and the factor influence recommendation module are arranged to acquire the medical information and the patient information in the medical platform, so that the patient can recommend the consultation service suitable for the patient according to the competition degree and the risk degree of the service market of the patient disease by taking the service quality and the service process as reference under the condition that the quality of the paid consultation service is difficult to define, thereby avoiding the condition that the patient pays the expense but cannot obtain the good online medical consultation service, improving the consultation satisfaction of the patient, helping the doctor to improve the reputation and optimizing the working efficiency.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram of the system module composition of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention provides a technical solution: the product service recommendation method based on the intelligent medical big data comprises a medical data acquisition module, a medical data analysis module and a factor influence recommendation module, wherein the medical data acquisition module is used for acquiring medical information and patient information in a medical platform, the medical data analysis module is used for analyzing the acquired data information, the factor influence recommendation module is used for recommending medical services to patients according to different influence factors, the medical data analysis module is connected with the medical data acquisition module through a network, the factor influence recommendation module is connected with the medical data analysis module through a network, and through the arrangement of the medical data acquisition module, the medical data analysis module and the factor influence recommendation module, the medical information and the patient information in the medical platform are acquired.
The medical data acquisition module comprises a medical information input module, a behavior data acquisition module and a patient information acquisition module, wherein the medical information input module is used for inputting medical information in the medical platform, the behavior data acquisition module is used for acquiring patient behavior information and doctor behavior information in the medical platform, and the patient information acquisition module is used for acquiring basic information registered by a patient when the patient visits the platform.
The medical data analysis module comprises a service quality analysis module, a service process information analysis module and a situation factor analysis module, wherein the service quality analysis module is used for analyzing the service quality in the medical platform, the service quality comprises doctor reputation and service price, a patient can form the view of the doctor service quality based on the service experience of the patient and can transmit the view through the network platform, the reputation mainly comes from the evaluation of the patient, the price is a quality signal directly given by the doctor, the price is taken as the money amount provided by the doctor for the service of the patient, the quality and the cost of the service provided by the doctor can be reflected, for the patient, the price is a cost signal and a quality signal, the service process information analysis module is used for analyzing the medical service process quality, the service process quality comprises service timeliness and information amount, due to the trust attribute of medical consultation service, the patient can not accurately judge the service information quality, the patient consults for obtaining the information, the information amount corresponds to the service time length of the doctor, the high information amount can improve the understanding of the patient on the disease related condition, the situation analysis degree can reflect the attitude of the doctor, the satisfaction degree of the patient can be increased, the situation analysis module is used for obtaining information of different situation analysis and the situation factors of the disease risk of the situation analysis and the situation, the patient can be directly used for the decision-taking.
The factor influence recommendation module comprises a parameter selection module, a preliminary screening module and a classification recommendation module, wherein the parameter selection module is used for selecting basic parameters by a patient independently, the preliminary screening module is used for preliminarily screening medical service information, and the classification recommendation module is used for classifying and recommending medical services.
5. An intelligent medical system based product service recommendation method according to any one of claims 1-4, wherein: the product service recommendation method of the intelligent medical system comprises the following steps:
step S1: establishing an information database through an intelligent medical system, performing data base processing on data information required by a recommendation method, and storing records;
step S2: the system acquires behavior data information on each medical platform and basic personal information registered when a patient performs medical service on line through big data, and stores the behavior data information and the basic personal information into the database for further analysis and processing;
and step S3: further according to the obtained information, performing integrated analysis on information parameters which need to be considered in the process of selecting medical services by the patient;
and step S4: when a patient logs in a medical platform to perform on-line medical service, parameter selection is performed according to service contents to be searched, preliminary screening is performed after the selection is completed, and further selectable medical service contents are displayed for the patient according to the information analysis result.
The data base processing in the step S1 specifically includes: creating a formatting algorithm object, reading transmitted data in sequence, performing formatting analysis on the transmitted data, modifying and mending missing data and error data, and setting a uniform formatting template, firstly judging whether the data is missing or not, completing the missing data if the data is missing, judging whether the data is erroneous or not if the data is not missing, correcting, finally separating and dividing the data by separators to form a whole data segment, further performing text processing on a patient state consultation text submitted by a patient, wherein the text processing comprises text word segmentation, noise word and symbol removal and word frequency value calculation, firstly performing word segmentation on the text, performing word segmentation on the consultation text by using a word segmentation tool, and the word segmentation result comprises a subject word, a noise symbol and the like, then removing the noise word and the noise symbol, calculating the IDF value of the subject word, calculating word frequency and reverse word frequency by using a plurality of subject word banks in the database, and generating a whole subject word chain by using the separators to store the whole subject word chain in the database.
Step S3 further comprises the steps of:
step S31: counting the price of the online consultation service of the doctor, wherein the online consultation service is divided into a limited consultation service and an unlimited consultation service, the limited consultation service answers the consultation service with a fixed number of questions at a payment, the unlimited consultation service answers the consultation service with all the questions within a fixed time at a payment, and the average value of the highest price and the lowest price is used as a reference quantity; the doctor reputation is analyzed by taking the patient evaluation accumulated by the doctor serving the patient online and offline for a long time as a standard, serious information asymmetry exists between doctors and patients, the patient is in an information weak position due to lack of professional knowledge and unknown doctor service quality level information, various information needs to be searched to reduce the information asymmetry between the doctors and the patients, the remote service attribute of online medical consultation provides convenience for the patient, meanwhile, the distance between the doctors and the patients further increases the service uncertainty of the patient, the doctor reputation serves as an external signal, reference information of service quality can be provided for the patient when purchasing a service decision, and the doctor reputation is established along with the time, more credible and reliable, and the service quality is recorded as service quality
In order for the doctor to be in reputation,
for service price, because of medical consultation clothesThe particularity of affairs is different from other commodities, that is, patients are difficult to judge the quality of service according to the knowledge degree of the patients, and the higher the price is to a great extent, the better the service is;
step S32: the method is characterized in that the online consultation reply information quantity N of doctors is recorded and counted according to a database, uncertainty and ambiguity exist in the disease diagnosis and treatment process of patients, the uncertainty comes from information loss, the information quantity is increased, the uncertainty is reduced, the ambiguity can come from insufficient understanding of disease related information, the service with high information richness can effectively reduce the ambiguity of the patients on the disease related information, the information richness of the information richness can be judged according to the information quantity and time for solving specific problems of the patients in the specific information content, the information richness is used as a service timeliness reference, a large amount of information and rapid problem feedback can improve the information richness of the information in service, the uncertainty and ambiguity of the patients on the disease related cognition are reduced, the expectation of the patients on the doctor service quality is improved, the services are promoted to be purchased by the patients, and the quality of the service process is recorded as consultation quality
N is the total amount of information,
the service is time-sensitive;
step S33: further consider the situation factor, namely service market competition degree and patient's disease risk degree, count the doctor's quantity of managing all kinds of diseases separately, regard this as the reference of service market competition degree, the quantity of doctor can explain the popularization of disease to a certain extent, the disease is more popular, supply and demand adjustment effect based on market, the more the doctor is counted, the disease relevant knowledge that the patient has is more, when the patient possesses more information, can pay close attention to the content of information itself more, more carefully, evaluate the information content comprehensively, judge its disease risk degree according to the state of illness information that the patient inputs when consulting, the risk degree of disease is concerned with the degree that the life health of patient receives the threat, receive the degree of threat difference, the patient also can be different to the degree of intervention of information processing, the influence degree that the information selected to the patient also has the difference.
Step S31, the specific analysis of the reputation of the chinese doctor is: obtaining historical scoring records and evaluation information of doctors, carrying out word vector processing on evaluation texts, carrying out correction processing on scoring characteristics, correcting original scores by adopting a method of combining the original scores with emotion analysis values of the evaluation texts, considering emotion deviation of the doctors in user evaluation, needing emotion analysis to calculate the deviation, carrying out emotion analysis processing on the evaluation texts to obtain emotion polarity values, and adopting a Text Blob emotion analysis tool, wherein the variation range of the emotion polarity is [ -1,1]-1 represents a negative and 1 a positive, the sentiment polarity value obtained by the evaluation is compared to the original score 1-5]Combining, specifically calculating as P = rho R + (1-rho) S, wherein R is the original score of a doctor, S is the evaluation emotion polarity value, P is the final score after the emotion analysis value is combined with the original score, rho is the parameter value for adjusting the weight between the original score and the emotion polarity value, counting the corrected final score, and averaging the average value
As a reference amount of the reputation of the doctor, namely the higher the score is, the higher the reputation is;
the information richness in step S32 is specifically: obtaining a complete record of one consultation according to the history record, and obtaining the number M of sentences which are generated in one consultation time and specifically solve the problems of the patients and the number M of all sentences by taking a section of sentences as a standard, wherein the number M of sentences is used as a parameter of information richness in the consultation process and is specifically calculated as
The higher the information richness is, the higher the problem solving efficiency of the doctor is, the higher the doctor reputation is, the condition that the patient time is intentionally delayed for obtaining the consultation expense can be avoided, the patient satisfaction is improved, and the average value of the richness of all historical consultation content information is taken
Is a recommended reference amount.
Step S4 further comprises the steps of:
step S41: after a patient logs in a medical platform, intelligently recommending selectable service options according to personal information according to whether the selected option is authorized during personal registration, selecting whether the option is needed, and autonomously selecting whether the option is authorized to be recommended according to personal wishes of the patient;
step S42: after authorization, the system accesses the registration information of the patient and calls a history record, and judges whether the patient is the first purchase of online service or repeated purchase, whether the residence of the patient is local or off-site, and whether the patient needs to see a doctor online or ask a doctor offline;
step S43: if off-line inquiry is selected, the system can recommend a nearby hospital to the patient according to the distance between the residence of the patient and each hospital and provide on-line reservation service according to the illness state information;
step S44: and selecting online consultation, starting to scan illness state information, screening doctors capable of processing the consultation according to the keywords, judging the service market competitiveness and risk degree of the disease, dividing the disease into high-intervention and low-intervention diseases according to the risk degree, providing and storing the diseases into a database according to the judgment standard, and recommending the service to the patient according to different factors.
Step S44 specifically includes: and (3) integrating the recommendation factors, and recommending the doctor with the highest comprehensive index to the patient for selection of consultation services, wherein the specific comprehensive recommendation index is as follows:
alpha, beta, gamma and delta are respectively weight parameters of doctor reputation, service price, service timeliness and information quantity of consultation service, and alpha + beta + gamma + delta =1 is influenced by the competition degree and risk degree of the service market of patient consultation state, and finally n-top-ranked paid consultation service is screened out for the patient for self-selection and purchase, namely under the conditions of the competition degree and risk degree of the patient disease service market, the patient is more inclined to adopt high-range quality to carry out quality judgment, namely, clues of doctor reputation and service timeliness relative to the service price and information quantityThe weight proportion will be higher.
It should be noted that, in this document, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The utility model provides an intelligent medical system, includes medical data acquisition module, medical data analysis module and factor influence recommendation module, its characterized in that: the medical data acquisition module is used for acquiring medical information and patient information in a medical platform, the medical data analysis module is used for analyzing the acquired data information, the factor influence recommendation module is used for recommending medical services to patients according to different influence factors, the medical data analysis module is connected with the medical data acquisition module through a network, and the factor influence recommendation module is connected with the medical data analysis module through a network.
2. The intelligent medical system of claim 1, wherein: the medical data acquisition module comprises a medical information input module, a behavior data acquisition module and a patient information acquisition module, wherein the medical information input module is used for inputting medical information in the medical platform, the behavior data acquisition module is used for acquiring patient behavior information and doctor behavior information in the medical platform, and the patient information acquisition module is used for acquiring basic information registered by a patient when the patient visits the platform.
3. The intelligent medical system of claim 2, wherein: the medical data analysis module comprises a service quality analysis module, a service process information analysis module and a situation factor analysis module, wherein the service quality analysis module is used for analyzing the service quality in the medical platform, the service process information analysis module is used for analyzing the medical service process quality, and the situation factor analysis module is used for analyzing different situation factors.
4. The intelligent medical system of claim 3, wherein: the factor influence recommendation module comprises a parameter selection module, a preliminary screening module and a classification recommendation module, wherein the parameter selection module is used for enabling a patient to autonomously select basic parameters, the preliminary screening module is used for preliminarily screening medical service information, and the classification recommendation module is used for classifying and recommending medical services.
5. The intelligent medical system based product service recommendation method according to any one of claims 1-4, wherein: the product service recommendation method of the intelligent medical system comprises the following steps:
step S1: establishing an information database through an intelligent medical system, performing data base processing on data information required by a recommendation method, and storing records;
step S2: the system acquires behavior data information on each medical platform and basic personal information registered when a patient performs medical service on line through big data, and stores the behavior data information and the basic personal information into the database for further analysis and processing;
and step S3: further according to the obtained information, performing integrated analysis on information parameters which need to be considered in the process of selecting medical services by the patient;
and step S4: when a patient logs in a medical platform to perform on-line medical service, parameter selection is performed according to service contents to be searched, preliminary screening is performed after the selection is completed, and further selectable medical service contents are displayed for the patient according to the information analysis result.
6. The intelligent medical system-based product service recommendation method of claim 5, wherein: the data basic processing in step S1 specifically includes: and establishing a formatting algorithm object, reading the transmitted data in sequence, performing formatting analysis on the transmitted data, modifying and mending missing data and error data, setting a uniform formatting template, and further performing text processing on the illness state consultation text submitted by the patient, wherein the text processing comprises text word segmentation, noise word and symbol removal and word frequency value calculation.
7. The intelligent medical system-based product service recommendation method of claim 6, wherein: the step S3 further comprises the steps of:
step S31: counting the price of the online consultation service of the doctor, wherein the online consultation service is divided into limited consultation service and unlimited consultation service, and the average value of the highest price and the lowest price is taken as a reference amount; and analyzing the reputation of the doctor based on the patient evaluation accumulated by the doctor through long-term online and offline service for the patient, and recording the service quality as
In order for the doctor to be in reputation,
a service price;
step S32: the information quantity N of the doctor on-line consultation reply is counted according to the database records, the information richness of the consultation content is judged according to the information quantity and time for solving the specific problems of the patient in the specific information content, the information richness is used as the reference quantity of service timeliness, and the quality of the service process is recorded as
N is the total amount of information,
the service is time-sensitive;
step S33: further considering situation factors, namely the competition degree of the service market and the disease risk degree of the patient, counting the number of doctors for managing various diseases, taking the number as the reference quantity of the competition degree of the service market, and judging the disease risk degree according to the disease information input when the patient consults.
8. The intelligent medical system-based product service recommendation method of claim 7, wherein: the specific analysis of the reputation of the Chinese medicine in step S31 is as follows: acquiring historical scoring records and evaluation information of doctors, performing word vector processing on the evaluation texts, correcting scoring characteristics, correcting original scores by adopting a method of combining the original scores with emotion analysis values of the evaluation texts, specifically calculating the original scores as P = rho R + (1-rho) S, counting the corrected final scores, and averaging the average values
As a reference for the reputation of the doctor;
the information richness in step S32 is specifically: obtaining a complete record of one consultation according to the history record, obtaining the number M of sentences which are generated in one consultation time and used for solving the problem of the patient and the number M of all sentences by taking a section of sentences as a standard, taking the number M of the sentences as parameters of information richness in the consultation process, and specifically calculatingIs composed of
And taking the average value of the richness of all historical consultation content information
Is a recommended reference amount.
9. The intelligent medical system-based product service recommendation method of claim 8, wherein: the step S4 further includes the steps of:
step S41: after logging in a medical platform, a patient firstly intelligently recommends optional service options according to personal information and whether the optional service options are required or not according to whether the optional service options are authorized or not in personal registration;
step S42: after authorization, the system accesses the registration information of the patient and calls a history record, and judges whether the patient is the first purchase of online service or repeated purchase, whether the residence of the patient is local or off-site, and whether the patient needs to see a doctor online or ask a doctor offline;
step S43: if off-line inquiry is selected, the system can recommend a nearby hospital to the patient according to the distance between the residence of the patient and each hospital and provide on-line reservation service according to the illness state information;
step S44: and selecting on-line consultation, scanning the disease condition information, screening out doctors capable of processing the consultation according to the keywords, judging the service market competitiveness and the risk degree of the disease, and recommending the service to the patient according to different factors.
10. The intelligent medical system-based product service recommendation method of claim 9, wherein: the step S44 specifically includes: and (3) integrating the recommendation factors, and recommending the doctor with the highest comprehensive index to the patient for selection of consultation services, wherein the specific comprehensive recommendation index is as follows:
finally screening the patientsAnd (4) paying consulting services of the top n in the ranking for self-choosing to buy.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116825304A (en) * | 2023-06-25 | 2023-09-29 | 湖南大学 | Online medical method and system based on deep interconnection |
| CN117497203A (en) * | 2023-11-15 | 2024-02-02 | 江苏鼎驰电子科技有限公司 | Medical data interconnection and intercommunication method and system based on artificial intelligence and intelligent medical treatment |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116825304A (en) * | 2023-06-25 | 2023-09-29 | 湖南大学 | Online medical method and system based on deep interconnection |
| CN116825304B (en) * | 2023-06-25 | 2024-02-23 | 湖南大学 | Online medical method and system based on deep interconnection |
| CN117497203A (en) * | 2023-11-15 | 2024-02-02 | 江苏鼎驰电子科技有限公司 | Medical data interconnection and intercommunication method and system based on artificial intelligence and intelligent medical treatment |
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