CN110442797B - Internet hospital product configuration optimization method - Google Patents
Internet hospital product configuration optimization method Download PDFInfo
- Publication number
- CN110442797B CN110442797B CN201910764986.2A CN201910764986A CN110442797B CN 110442797 B CN110442797 B CN 110442797B CN 201910764986 A CN201910764986 A CN 201910764986A CN 110442797 B CN110442797 B CN 110442797B
- Authority
- CN
- China
- Prior art keywords
- data
- patient
- interest
- life cycle
- product
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/957—Browsing optimisation, e.g. caching or content distillation
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Business, Economics & Management (AREA)
- Biomedical Technology (AREA)
- Business, Economics & Management (AREA)
- Epidemiology (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Primary Health Care (AREA)
- Public Health (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The invention provides an internet hospital product configuration optimization method, which comprises the following steps: s1, acquiring patient data of the Internet hospital through a cloud network, and carrying out a sequential arrangement process of life cycle data M according to the full life cycle dimensional data of the patients; s2, arranging the life cycle data M in sequence, and recombining diagnosis and treatment categories of patients in each life cycle data set; s3, forming a recombined data vector N after the diagnosis and treatment categories are recombined, and mining diagnosis and treatment products in the recombined data vector; s4, delivering the interest degree of the diagnosis and treatment product according to the calling times and the interest degree of the patient according to the calling times of each vector element in the recombined data vector; and S5, after the interestingness is delivered, recombining each vector element in the data vector N with the life cycle data M established by the full life cycle dimensional data of the patient and the diagnosis and treatment categories to finally form an M multiplied by N matrix, and displaying the matrix through an intelligent terminal.
Description
Technical Field
The invention relates to the field of computer software, in particular to an internet hospital product configuration optimization method.
Background
Because the continuous development of internet technology and the speed increase of network bandwidth, the collection and mining of medical data are continuously systematized and normalized through the internet, but no mode for forming a medical hospital on the internet is formed in the prior art, and the data of the hospital are not networked, spread and operated, even if the concept of the internet hospital is formed, the displayed data are too complex, a systematized and simplified data display mode cannot be formed, the user uses the internet hospital to cause psychological conflict, the data cannot be popularized and used on a large scale, how to display and mine the data, and a fast and accurate data matrix is formed, so that technical personnel in the field need to solve corresponding technical problems urgently.
Disclosure of Invention
The invention aims to at least solve the technical problems in the prior art, and particularly creatively provides an internet hospital product configuration optimization method.
In order to achieve the above object, the present invention provides an internet hospital product configuration optimization method, which comprises the following steps:
s1, acquiring patient data of the Internet hospital through a cloud network, and carrying out a sequential arrangement process of life cycle data M according to the full life cycle dimensional data of the patients;
s2, arranging the life cycle data M in sequence, and recombining diagnosis and treatment categories of patients in each life cycle data set;
s3, forming a recombined data vector N after the diagnosis and treatment categories are recombined, and mining diagnosis and treatment products in the recombined data vector;
s4, delivering the interest degree of the diagnosis and treatment product according to the calling times and the interest degree of the patient according to the calling times of each vector element in the recombined data vector;
and S5, after the interestingness is delivered, recombining each vector element in the data vector N with the life cycle data M established by the full life cycle dimensional data of the patient and the diagnosis and treatment categories to finally form an M multiplied by N matrix, and displaying the matrix through an intelligent terminal.
Preferably, the S1 includes:
s1-1, data mining is carried out on patient data in the cloud network, and mining is carried out through common sense data, wherein the common science popularization data, the data before a doctor, the data in the doctor, the data after the doctor and the rehabilitation data of the patient are obtained;
s1-2, in the process of mining the health science popularization data of the patient, according to nutrition intake of the patient and attention degree of Chinese and western medicine knowledge acquired by a cloud network, data collection is carried out through intelligent equipment worn by the patient, and the step number, the heart rate and the exercise mileage are obtained;
s1-3, carrying out patient identity authentication after carrying out face recognition operation, fingerprint recognition operation and voice recognition operation on the patient;
s1-4, after identity authentication, scanning the two-dimensional code and the bar code of the patient intelligent equipment through an intelligent terminal, or collecting patient data through a Bluetooth pairing signal and NFC pairing information;
s1-5, dynamically displaying personal information and instantly displayed body and mind information data through a main interface, wherein the data displayed in a timing mode are statistical tables, graphs, bar charts or pie charts obtained through statistics, and the display content of the index early warning prompt comprises time, various indexes, comparison with personal normal index values and comparison with standard index values;
s1-6, acquiring personal input information through voice recognition or character recognition and uploading the personal input information to a cloud network;
s1-7, collecting data of the selected drugs, treatment means, diet condition, daily life condition and motion state before the patient visits the doctor, and storing the data in a cloud network;
s1-8, in the process of patient treatment, data collection is carried out on treatment means selected by a doctor, ingested medicines, medicine dosage, types selected by the medicines and the medicine use period, and the data are stored in a cloud network;
and S1-9, after the patient finishes the treatment, collecting data of the rehabilitation condition, the diet condition, the daily life condition and the motion state, and storing the data in a cloud network.
Preferably, the S2 includes:
s2-1, forming life cycle data sets in the life cycle data M, wherein cloud data of patients before, during and after diagnosis are acquired by each life cycle data set;
and S2-2, according to the cloud data of the patient before, during and after the diagnosis, corresponding data acquisition is carried out in each life cycle data set according to the actual requirements of the patient, so that data sharing operation is carried out for different treatments at different stages.
Preferably, the S3 includes:
s3-1, according to the recombined data vector N, excavating diagnosis and treatment products in the recombined data vector;
s3-2, the diagnosis and treatment product is a product extracted from patient behavior data, a conventional medicine used in internal medicine for treatment and a diagnosis and treatment means; surgical techniques and anesthetic doses used in the surgical intervention at the clinic; whether a doctor visit is recorded in the psychiatric department or not;
and S3-3, pushing corresponding products according to the recorded data formed in different departments according to the patient behavior data.
Preferably, the S4 includes:
s4-1, recombining the calling times of each vector element in the data vector, and carrying out statistical classification through a cloud network;
s4-2, delivering the interest degree of the diagnosis and treatment product according to the calling times and the interest degree of the patient;
s4-3, receiving an initial user account configuration using one or more patient users; using one or more associated interest databases in the patient's points of interest; determining different stages of the patient in the full life cycle according to the received interest data, interested in those products and pushing the corresponding products in the recombined data vector;
s4-4, in the process of data transmission by using a cloud network, the interest data comprises at least one part of interest point content, interest department and interest point marketing data of a plurality of interest points in an interest set defined by the cloud network, wherein the interest points are determined by the interest data associated with at least part of patient users;
s4-5, identifying a product used by a coincident patient associated with the interest data using the one or more interest sets; determining a reminder format and the interest data by a reminder program using the one or more interest sets.
Preferably, the S4 delivering process further includes:
S4-A, obtaining an interest tag of a patient user, wherein the interest tag is used for identifying a selection plate block which is most interested in a product selected by the patient;
S4-B, obtaining products in the selected plate of the current patient; pushing the interest tag and the selection plate of interest to a patient; acquiring the association degree of the patient and the interest tag according to the selection condition of the interest tag in a cloud network;
S4-C, determining the association degree of the patient and the interest tag: determining whether the number of products selected by the patient during the full life cycle is greater than a recombined data vector N; if the number of the products selected by the user in the full life cycle is judged to be larger than the preset value, determining that the number of the products selected by the patient is invalid, and if the number of the products selected by the user in the full life cycle is judged to be smaller than the preset value, determining that the number of the products selected by the patient is valid;
the patient interest tag is S, and the extraction value for product screening is x ═ Σi＝IGi*μ(1+ei) Wherein I is a positive integer, GiFor the value of interest to be screened, μ is the parameter of interest, eiState parameters for real-time adjustment;
S4-D, if the frequency of the selection recommendation request of the patient exceeds a preset value, determining that the patient is not satisfied with the recommendation effect; the recommended content received by the patient within a period of time is longer in the product display area, and the real-time interactive response state is ensured, which represents satisfaction to the recommended product;
S4-E, initializing an interested product set, arranging interested products in the whole life cycle of different patients by using a decomposition mode, and distributing the same or different products in different recombined data vectors to investigate the interested products of the patients; then randomly selecting a product with higher use from the whole life cycle of all patients, pushing each recombined data vector to form the product satisfaction degree recommended by the patient, if the satisfaction degree of the patient on the recommended content is judged to be reduced, adjusting the corresponding product or putting the product off shelf, and obtaining the interest label according to the fact that the selection plate of the patient on the recommended product is higher; and pushing the interest tag for a new patient, and judging whether to load a product of the interest tag according to the selection condition of the new patient.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
the method comprises the steps of obtaining patient data of an internet hospital through a cloud network, pushing different used products after collecting and grabbing the patient data, finally forming an MXN product matrix, and displaying the product matrix through an intelligent terminal.
The product of the interest tag is loaded for the patient's selected condition.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic diagram of the operation of the present invention;
fig. 2 is a schematic view of an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As shown in fig. 1, the invention provides an internet hospital product configuration optimization method, which comprises the following steps:
s1, acquiring patient data of the Internet hospital through a cloud network, and carrying out a sequential arrangement process of life cycle data M according to the full life cycle dimensional data of the patients;
s2, arranging the life cycle data M in sequence, and recombining diagnosis and treatment categories of patients in each life cycle data set;
s3, forming a recombined data vector N after the diagnosis and treatment categories are recombined, and mining diagnosis and treatment products in the recombined data vector;
s4, delivering the interest degree of the diagnosis and treatment product according to the calling times and the interest degree of the patient according to the calling times of each vector element in the recombined data vector;
and S5, after the interestingness is delivered, recombining each vector element in the data vector N with the life cycle data M established by the full life cycle dimensional data of the patient and the diagnosis and treatment categories to finally form an M multiplied by N matrix, and displaying the matrix through an intelligent terminal.
Preferably, the S1 includes:
s1-1, data mining is carried out on patient data in the cloud network, and mining is carried out through common sense data, wherein the common science popularization data, the data before a doctor, the data in the doctor, the data after the doctor and the rehabilitation data of the patient are obtained;
s1-2, in the process of mining the health science popularization data of the patient, according to nutrition intake of the patient and attention degree of Chinese and western medicine knowledge acquired by a cloud network, data collection is carried out through intelligent equipment worn by the patient, and the step number, the heart rate and the exercise mileage are obtained;
s1-3, carrying out patient identity authentication after carrying out face recognition operation, fingerprint recognition operation and voice recognition operation on the patient;
s1-4, after identity authentication, scanning the two-dimensional code and the bar code of the patient intelligent equipment through an intelligent terminal, or collecting patient data through a Bluetooth pairing signal and NFC pairing information;
s1-5, dynamically displaying personal information and instantly displayed body and mind information data through a main interface, wherein the data displayed in a timing mode are statistical tables, graphs, bar charts or pie charts obtained through statistics, and the display content of the index early warning prompt comprises time, various indexes, comparison with personal normal index values and comparison with standard index values;
s1-6, acquiring personal input information through voice recognition or character recognition and uploading the personal input information to a cloud network;
s1-7, collecting data of the selected drugs, treatment means, diet condition, daily life condition and motion state before the patient visits the doctor, and storing the data in a cloud network;
s1-8, in the process of patient treatment, data collection is carried out on treatment means selected by a doctor, ingested medicines, medicine dosage, types selected by the medicines and the medicine use period, and the data are stored in a cloud network;
and S1-9, after the patient finishes the treatment, collecting data of the rehabilitation condition, the diet condition, the daily life condition and the motion state, and storing the data in a cloud network.
Preferably, the S2 includes:
s2-1, forming life cycle data sets in the life cycle data M, wherein cloud data of patients before, during and after diagnosis are acquired by each life cycle data set;
and S2-2, according to the cloud data of the patient before, during and after the diagnosis, corresponding data acquisition is carried out in each life cycle data set according to the actual requirements of the patient, so that data sharing operation is carried out for different treatments at different stages.
Preferably, the S3 includes:
s3-1, according to the recombined data vector N, excavating diagnosis and treatment products in the recombined data vector;
s3-2, the diagnosis and treatment product is a product extracted from patient behavior data, a conventional medicine used in internal medicine for treatment and a diagnosis and treatment means; surgical techniques and anesthetic doses used in the surgical intervention at the clinic; whether a doctor visit is recorded in the psychiatric department or not;
and S3-3, pushing corresponding products according to the recorded data formed in different departments according to the patient behavior data.
Preferably, the S4 includes:
s4-1, recombining the calling times of each vector element in the data vector, and carrying out statistical classification through a cloud network;
s4-2, delivering the interest degree of the diagnosis and treatment product according to the calling times and the interest degree of the patient;
s4-3, receiving an initial user account configuration using one or more patient users; using one or more associated interest databases in the patient's points of interest; determining different stages of the patient in the full life cycle according to the received interest data, interested in those products and pushing the corresponding products in the recombined data vector;
s4-4, in the process of data transmission by using a cloud network, the interest data comprises at least one part of interest point content, interest department and interest point marketing data of a plurality of interest points in an interest set defined by the cloud network, wherein the interest points are determined by the interest data associated with at least part of patient users;
s4-5, identifying a product used by a coincident patient associated with the interest data using the one or more interest sets; determining a reminder format and the interest data by a reminder program using the one or more interest sets.
Preferably, the S4 delivering process further includes:
S4-A, obtaining an interest tag of a patient user, wherein the interest tag is used for identifying a selection plate block which is most interested in a product selected by the patient;
S4-B, obtaining products in the selected plate of the current patient; the method comprises the following steps: selecting a medical department before diagnosis in the whole life cycle, observing the treatment process, and pushing the interest label and the interested selection plate to a patient; acquiring the association degree of the patient and the interest tag according to the selection condition of the interest tag in a cloud network;
S4-C, determining the association degree of the patient and the interest tag: determining whether the number of products selected by the patient during the full life cycle is greater than a recombined data vector N; if the number of the products selected by the user in the full life cycle is judged to be larger than the preset value, determining that the number of the products selected by the patient is invalid, and if the number of the products selected by the user in the full life cycle is judged to be smaller than the preset value, determining that the number of the products selected by the patient is valid;
the patient interest tag is S, and the extraction value for product screening is x ═ Σi＝IGi*μ(1+ei) Wherein I is a positive integer, GiFor the value of interest to be screened, μ is the parameter of interest, eiState parameters for real-time adjustment;
S4-D, if the frequency of the selection recommendation request of the patient exceeds a preset value, determining that the patient is not satisfied with the recommendation effect; the recommended content received by the patient within a period of time is longer in the product display area, and the real-time interactive response state is ensured, which represents satisfaction to the recommended product;
S4-E, initializing an interested product set, arranging interested products in the whole life cycle of different patients by using a decomposition mode, and distributing the same or different products in different recombined data vectors to investigate the interested products of the patients; then randomly selecting a product with higher use from the whole life cycle of all patients, pushing each recombined data vector to form the product satisfaction degree recommended by the patient, if the satisfaction degree of the patient on the recommended content is judged to be reduced, adjusting the corresponding product or putting the product off shelf, and obtaining the interest label according to the fact that the selection plate of the patient on the recommended product is higher; and pushing the interest tag for a new patient, and judging whether to load a product of the interest tag according to the selection condition of the new patient.
As shown in fig. 2, 1 divides internet hospital products from two dimensions. One dimension is the patient's full life cycle dimension, which can be divided into: health, science popularization, before, during, after, health maintenance, 5 stages. Another dimension is divided by hospital departments, e.g., emergency, surgical, medical, etc.
2 these two dimensions may constitute a two-dimensional product matrix. The internet hospitals provide products to the hospitals according to the product matrix.
First, the product catalog on the platform is organized according to a two-dimensional matrix of hospital departments (or departments) and patient health cycles. For example, product a is a "surgical" pre-office "product. The product B is a health product for internal medicine.
A product may cover multiple departments and phases of a patient's health cycle. There may also be multiple products within a patient health cycle stage of a department.
1. Each department product developer also develops a particular product according to this organizational form, and the developed products fit into a product matrix.
2. Each department shelves a product of the service that can be provided by himself through the product matrix. The product needs to be docked with the system in the hospital, the docking is successful and the racking can be completed after the test is passed. The docking operation may also be performed simultaneously with the development of the product in step 1.
3. The user can access the product in an internet hospital terminal app. The user can find products through departments and health cycle phases at the app.
4. Each department of the hospital provides services for users through products, and patient information can be counted.
5. Patient information is shared among various departments.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (2)
1. An Internet hospital product configuration optimization method is characterized by comprising the following steps:
s1, acquiring patient data of the Internet hospital through a cloud network, and carrying out a sequential arrangement process of life cycle data M according to the full life cycle dimensional data of the patients;
s1-1, data mining is carried out on patient data in the cloud network, and mining is carried out through common sense data, wherein the common science popularization data, the data before a doctor, the data in the doctor, the data after the doctor and the rehabilitation data of the patient are obtained;
s1-2, in the process of mining the health science popularization data of the patient, according to nutrition intake of the patient and attention degree of Chinese and western medicine knowledge acquired by a cloud network, data collection is carried out through intelligent equipment worn by the patient, and the step number, the heart rate and the exercise mileage are obtained;
s1-3, carrying out patient identity authentication after carrying out face recognition operation, fingerprint recognition operation and voice recognition operation on the patient;
s1-4, after identity authentication, scanning the two-dimensional code and the bar code of the patient intelligent equipment through an intelligent terminal, or collecting patient data through a Bluetooth pairing signal and NFC pairing information;
s1-5, dynamically displaying personal information and instantly displayed body and mind information data through a main interface, wherein the data displayed in a timing mode are statistical tables, graphs, bar charts or pie charts obtained through statistics, and the display content of the index early warning prompt comprises time, various indexes, comparison with personal normal index values and comparison with standard index values;
s1-6, acquiring personal input information through voice recognition or character recognition and uploading the personal input information to a cloud network;
s1-7, collecting data of the selected drugs, treatment means, diet condition, daily life condition and motion state before the patient visits the doctor, and storing the data in a cloud network;
s1-8, in the process of patient treatment, data collection is carried out on treatment means selected by a doctor, ingested medicines, medicine dosage, types selected by the medicines and the medicine use period, and the data are stored in a cloud network;
s1-9, after the patient finishes the treatment, collecting data of the rehabilitation condition, the diet condition, the daily life condition and the motion state, and storing the data in a cloud network;
s2, arranging the life cycle data M in sequence, and recombining diagnosis and treatment categories of patients in each life cycle data set;
s2-1, forming life cycle data sets in the life cycle data M, wherein cloud data of patients before, during and after diagnosis are acquired by each life cycle data set;
s2-2, according to the cloud data of the patient before, during and after the diagnosis, corresponding data acquisition is carried out in each life cycle data set according to the actual requirements of the patient, so that data sharing operation is carried out for different treatments at different stages;
s3, forming a recombined data vector N after the diagnosis and treatment categories are recombined, and mining diagnosis and treatment products in the recombined data vector;
s3-1, according to the recombined data vector N, excavating diagnosis and treatment products in the recombined data vector;
s3-2, the diagnosis and treatment product is a product extracted from patient behavior data, a conventional medicine used in internal medicine for treatment and a diagnosis and treatment means; surgical techniques and anesthetic doses used in the surgical intervention at the clinic; whether a doctor visit is recorded in the psychiatric department or not;
s3-3, pushing corresponding products according to the recorded data formed in different departments according to the patient behavior data;
s4, delivering the interest degree of the diagnosis and treatment product according to the calling times and the interest degree of the patient according to the calling times of each vector element in the recombined data vector;
s4-1, recombining the calling times of each vector element in the data vector, and carrying out statistical classification through a cloud network;
s4-2, delivering the interest degree of the diagnosis and treatment product according to the calling times and the interest degree of the patient;
s4-3, receiving an initial user account configuration using one or more patient users; using one or more associated interest databases in the patient's points of interest; determining different stages of the patient in the full life cycle according to the received interest data, interested in those products and pushing the corresponding products in the recombined data vector;
s4-4, in the process of data transmission by using a cloud network, the interest data comprises at least one part of interest point content, interest department and interest point marketing data of a plurality of interest points in an interest set defined by the cloud network, wherein the interest points are determined by the interest data associated with at least part of patient users;
s4-5, identifying a product used by a coincident patient associated with the interest data using the one or more interest sets; determining a reminder format and the interest data by a reminder using the one or more interest sets;
and S5, after the interestingness is delivered, recombining each vector element in the data vector N with the life cycle data M established by the full life cycle dimensional data of the patient and the diagnosis and treatment categories to finally form an M multiplied by N matrix, and displaying the matrix through an intelligent terminal.
2. The internet hospital product configuration optimization method of claim 1, wherein said S4 delivery process further comprises:
S4-A, obtaining an interest tag of a patient user, wherein the interest tag is used for identifying a selection plate block which is most interested in a product selected by the patient;
S4-B, obtaining products in the selected plate of the current patient; pushing the interest tag and the selection plate of interest to a patient; acquiring the association degree of the patient and the interest tag according to the selection condition of the interest tag in a cloud network;
S4-C, determining the association degree of the patient and the interest tag: determining whether the number of products selected by the patient during the full life cycle is greater than a recombined data vector N; if the number of the products selected by the user in the full life cycle is judged to be larger than the preset value, determining that the number of the products selected by the patient is invalid, and if the number of the products selected by the user in the full life cycle is judged to be smaller than the preset value, determining that the number of the products selected by the patient is valid;
the patient interest tag is S, and the extraction value for product screening is x ═ Σi＝IGi*μ(1+ei) Wherein I is a positive integer, GiFor the value of interest to be screened, μ is the parameter of interest, eiState parameters for real-time adjustment;
S4-D, if the frequency of the selection recommendation request of the patient exceeds a preset value, determining that the patient is not satisfied with the recommendation effect; the recommended content received by the patient within a period of time is longer in the product display area, and the real-time interactive response state is ensured, which represents satisfaction to the recommended product;
S4-E, initializing an interested product set, arranging interested products in the whole life cycle of different patients by using a decomposition mode, and distributing the same or different products in different recombined data vectors to investigate the interested products of the patients; then randomly selecting a product with higher use from the whole life cycle of all patients, pushing each recombined data vector to form the product satisfaction degree recommended by the patient, if the satisfaction degree of the patient on the recommended content is judged to be reduced, adjusting the corresponding product or putting the product off shelf, and obtaining the interest label according to the fact that the selection plate of the patient on the recommended product is higher; and pushing the interest tag for a new patient, and judging whether to load a product of the interest tag according to the selection condition of the new patient.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910764986.2A CN110442797B (en) | 2019-08-19 | 2019-08-19 | Internet hospital product configuration optimization method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910764986.2A CN110442797B (en) | 2019-08-19 | 2019-08-19 | Internet hospital product configuration optimization method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110442797A CN110442797A (en) | 2019-11-12 |
| CN110442797B true CN110442797B (en) | 2022-02-08 |
Family
ID=68436439
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910764986.2A Active CN110442797B (en) | 2019-08-19 | 2019-08-19 | Internet hospital product configuration optimization method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110442797B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110851503B (en) * | 2019-11-19 | 2022-07-05 | 天津开心生活科技有限公司 | Medical mode conversion identification method and device, electronic equipment and storage medium |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20020005888A (en) * | 2000-07-10 | 2002-01-18 | 황인택 | Method and system for providing remote health care service |
| CN105468605A (en) * | 2014-08-25 | 2016-04-06 | 济南中林信息科技有限公司 | Entity information map generation method and device |
| CN105528526A (en) * | 2016-01-15 | 2016-04-27 | 胡广芹 | Traditional Chinese medicine inheritance and big data mining-based dynamic health management system for life cycle |
| CN106919611A (en) * | 2015-12-25 | 2017-07-04 | 北京国双科技有限公司 | Product information method for pushing and device |
| CN108804419A (en) * | 2018-05-22 | 2018-11-13 | 湖南大学 | Medicine is sold accurate recommended technology under a kind of line of knowledge based collection of illustrative plates |
| CN108921670A (en) * | 2018-07-04 | 2018-11-30 | 重庆大学 | A kind of potential interest of fusion user, the Drug trading recommended method of space-time data and classification popularity |
| CN109493244A (en) * | 2018-11-07 | 2019-03-19 | 平安医疗健康管理股份有限公司 | Method and Related product based on prediction model configuration demand for insurance |
| CN110008279A (en) * | 2019-03-27 | 2019-07-12 | 北京工商大学 | One kind is based on " relationship-weight " the orderly visual analysis method of matrix and application |
| CN110136794A (en) * | 2019-05-22 | 2019-08-16 | 湖北可汗广电文化有限公司 | A kind of mother and baby's Life cycle health medical treatment service system based on artificial intelligence |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6539281B2 (en) * | 2001-04-23 | 2003-03-25 | Accenture Global Services Gmbh | Online medicine cabinet |
-
2019
- 2019-08-19 CN CN201910764986.2A patent/CN110442797B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20020005888A (en) * | 2000-07-10 | 2002-01-18 | 황인택 | Method and system for providing remote health care service |
| CN105468605A (en) * | 2014-08-25 | 2016-04-06 | 济南中林信息科技有限公司 | Entity information map generation method and device |
| CN106919611A (en) * | 2015-12-25 | 2017-07-04 | 北京国双科技有限公司 | Product information method for pushing and device |
| CN105528526A (en) * | 2016-01-15 | 2016-04-27 | 胡广芹 | Traditional Chinese medicine inheritance and big data mining-based dynamic health management system for life cycle |
| CN108804419A (en) * | 2018-05-22 | 2018-11-13 | 湖南大学 | Medicine is sold accurate recommended technology under a kind of line of knowledge based collection of illustrative plates |
| CN108921670A (en) * | 2018-07-04 | 2018-11-30 | 重庆大学 | A kind of potential interest of fusion user, the Drug trading recommended method of space-time data and classification popularity |
| CN109493244A (en) * | 2018-11-07 | 2019-03-19 | 平安医疗健康管理股份有限公司 | Method and Related product based on prediction model configuration demand for insurance |
| CN110008279A (en) * | 2019-03-27 | 2019-07-12 | 北京工商大学 | One kind is based on " relationship-weight " the orderly visual analysis method of matrix and application |
| CN110136794A (en) * | 2019-05-22 | 2019-08-16 | 湖北可汗广电文化有限公司 | A kind of mother and baby's Life cycle health medical treatment service system based on artificial intelligence |
Non-Patent Citations (4)
| Title |
|---|
| Matrix-Type and Pattern-Based Simple LCA for Eco-Innovative Design of Products;Jaha Lewis Chen etc.;《2003 EcoDesign 3rd International Symposium on Environmentally Conscious Design and Inverse Manufacturing》;20040816;第467-471页 * |
| MC模式下基于顾客需求的产品配置优化分析;梁樑 等;《管理科学学报》;20030630;第53-56页 * |
| Time-aware Location Sequence Recommendation for Cold-start Mobile Users;Ting Shen etc.;《26TH ACM SIGSPATIAL INTERNATIONAL CONFERENCE ON ADVANCES IN GEOGRAPHIC INFORMATION SYSTEMS》;20181231;第1-3页 * |
| 电子病历系统中处方用药推荐模块的设计与实现;陶敏;《中国优秀硕士学位论文全文数据库（信息科技辑）》;20151215;第I138-70页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110442797A (en) | 2019-11-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106529182A (en) | Cloud hospital | |
| CN109636623A (en) | Medical data method for detecting abnormality, device, equipment and storage medium | |
| RU2009105047A (en) | MOBILE DEVICE, METHOD AND SYSTEM FOR PROCESSING FACTORS INFLUENCING THE BLOOD SUGAR LEVEL | |
| KR20170013276A (en) | Method, device and system for improving health condition | |
| CN106326657B (en) | The recommendation method and system of dosing regimens | |
| US20210134441A1 (en) | Selection and performance of hosted and distributed imaging analysis services | |
| KR100727770B1 (en) | System and method for providing health foods prescription information via integration healthcare | |
| JP2019071146A (en) | Display device for displaying clinical examination value, server, display method, and program | |
| KR20210136483A (en) | Health functional food effect collection method and system using the same | |
| JP5245954B2 (en) | Prescription order issuing program, prescription order issuing device and method | |
| JP2023018074A (en) | Disease diagnosis/medical treatment/prevention system | |
| KR20210052122A (en) | System and method for providing user-customized food information service | |
| JP5494018B2 (en) | Input support program, input support device, input support system, and input support method | |
| CN110442797B (en) | Internet hospital product configuration optimization method | |
| JP2016018224A (en) | Service system for using health condition prediction diagnosis | |
| CN110544533A (en) | System for health monitoring according to medical advice | |
| CN109033275B (en) | Medicine area demand prediction method based on association map and neural network | |
| CN112307028B (en) | Cross-data information knowledge modal differential content recommendation method oriented to essential computation | |
| CN110580952A (en) | Large-scale unmanned hospital system using artificial intelligence | |
| KR20200006836A (en) | Prescription system by consultation process based on mobile app, and consultation server | |
| JP2019053457A (en) | Candidate menu presentation system, management device and candidate menu presentation method | |
| JP7198959B1 (en) | Information processing method, computer program and information processing device | |
| CN111993442B (en) | High-intelligence general medical practice robot based on big data | |
| CN112768030A (en) | Safe medication management system and method thereof | |
| CN110517764B (en) | Telemedicine system and method for providing continuous coronary heart disease care |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |