CN110993043A - Medical health management system - Google Patents
Medical health management system Download PDFInfo
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- CN110993043A CN110993043A CN201911167243.3A CN201911167243A CN110993043A CN 110993043 A CN110993043 A CN 110993043A CN 201911167243 A CN201911167243 A CN 201911167243A CN 110993043 A CN110993043 A CN 110993043A
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- 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
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
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- G—PHYSICS
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- 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
- G16H40/60—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 for the operation of medical equipment or devices
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Abstract
The invention relates to the field of daily health management systems, in particular to a medical health management system which comprises an intelligent closestool, a server and a user terminal. The server comprises a database and an analysis module, and the intelligent closestool is used for collecting the detection data of the urine and the excrement of the user and sending the detection data to the database and the analysis module; the user terminal is used for sending a login request to the database by a user; the analysis module is used for analyzing the detection data according to the artificial intelligence prediction model and obtaining a detection result, and the detection result comprises a health assessment report and a risk prompt; the database is used for storing detection data and detection results, and is also used for receiving the login request, verifying whether the login request passes or not, and sending the detection data and the detection results to the user terminal after the user terminal passes the verification. The medical health management system provided by the invention has the advantages that the detection result is popular and easy to understand, the detection result viewing range is wide, and the data processing capacity of the system is improved.
Description
Technical Field
The invention relates to the field of daily intelligent health management systems, in particular to a medical health management system based on an intelligent closestool.
Background
The urine and the stool are necessary procedures every day, and the urine and the stool conditions can directly reflect the health conditions of various bodies, for example, the urine can reflect whether diseases of the urinary system, liver and gall diseases, diabetes and the like exist. Urine detection has a great role in early detection of these diseases and assessment of health status; the fecal occult blood can reflect the abnormal condition of the digestive tract, is an early warning for the abnormal condition of the digestive tract, and has great significance for early screening of malignant tumors (such as gastric cancer, colorectal cancer, polyp and adenoma) of the digestive tract. However, the examination of urine and fecal occult blood usually requires professional examination in hospitals, which consumes a lot of time and energy, and is inconvenient for young people who are busy in work and especially difficult for old people who are inconvenient in movement.
CN110269538A intelligent closestool lid and closestool with detection function are gathered to excrement, intelligent closestool lid includes: the excrement sampling module is used for collecting a liquid sample at a sampling position corresponding to an excrement detection item; the pipeline system is used for conveying the liquid sample to the detection module; the detection module is used for detecting the liquid sample to generate detection data; and the control module is in signal connection with the excrement sampling module, the pipeline system and the detection module and is used for acquiring detection data and analyzing the detection data to generate a detection result. The intelligent toilet lid with the excrement collecting and detecting functions can automatically complete excrement detection sample collection and detection, a user does not need to manually take a sample or manually send the sample into a detecting device, and complexity of user operation is reduced.
Patent No. CN103514359 discloses a urine test management system, which comprises: a urine testing device for capturing an image of a urine testing strip on which a user has performed a urine test, acquiring a urine test image, and transmitting the urine test image; and a urine testing management device for analyzing the urine testing image transmitted from the urine testing device, generating and displaying current analysis data, comparing the current analysis data with past analysis data, which is a result of urine testing performed by the user in the past, and generating and displaying health status history information in the form of a graph.
This intelligent closestool can analyze the testing result that generates the collection sample, but only contains the normal range data of test data contrast element among the testing result, obtains test element content anomaly or characteristic, and most use the user and do not have or only a small amount of medical knowledge, can't go out the healthy condition of oneself through this testing result analysis. In addition, the detection result of the intelligent closestool can only be checked on the intelligent closestool, the display range is limited, and a user cannot remotely acquire the detection result.
Disclosure of Invention
In order to solve the technical problems that the detection report is not convenient to understand and the range of the display of the detection result is limited, the invention aims to provide a medical health management system.
The technical scheme of the invention is as follows:
the medical health management system comprises an intelligent closestool, a server and a user terminal;
the server comprises a database and an analysis module, wherein:
the intelligent closestool is used for collecting the detection data of the urine and the excrement of the user and sending the detection data to the database and the analysis module;
the user terminal is used for sending a login request to the database by a user;
the analysis module is used for analyzing the detection data according to the artificial intelligence prediction model and obtaining a detection result, and the detection result comprises a health assessment report and a risk prompt;
the database is used for storing detection data and detection results, and is also used for receiving the login request, verifying whether the login request passes or not, and sending the detection data and the detection results to the user terminal after the user terminal passes the verification.
In the technical scheme of the invention, the artificial intelligence prediction module can analyze the detection data to obtain the detection result, rather than directly sending the detection data to the user, and after the detection data is analyzed by the artificial intelligence prediction module, a health assessment report and a risk prompt are obtained, so that the user can understand the detection result more easily. That is, the user can predict the health condition of the user directly according to the detection result, and does not need to compare whether each item of data of the user is in a normal range with the current detection report. The user directly logs in the database through the user terminal such as a mobile phone, a tablet, a computer and the like, the detection result is inquired, the user is not limited to check on the intelligent closestool, the user can inquire remotely, and the convenience of the user is improved. The database enlarges the storage space, can store more data information, improves the data processing capacity of the scheme system of the invention, and optimizes the performance of the system.
Further, the artificial intelligence prediction model is a neural network prediction model formed by training a plurality of groups of original detection data and original detection results, the original detection data are acquired detection data of the user at a time point A, and the original detection results are hospital physical examination results input by the user and after a preset time limit from the time point A.
Has the advantages that: under the continuous training of a large amount of original detection data and original detection results based on artificial intelligence, the artificial intelligence prediction model has a large amount of data bases, the closest detection results can be matched for the detection data of the current user, the original detection results in the artificial intelligence prediction model are hospital physical examination results of preset deadline time, the reliability is realized, and the historical health condition can embody the dynamic change condition of the health condition of the user.
The user terminal further comprises a user management module, wherein the user management module is used for registering login information of a user and sending the login information to the database, and the user management module is also used for sending information of the user magic database; the database is also used to store login information.
Has the advantages that: the user registration and the modification of the login information can be directly carried out at the user terminal, so that the user can conveniently manage the personal login information.
Further, the database also comprises a grouping module, wherein the grouping module is used for users to create a user group, the user group comprises 2 or more users, and the grouping module is also used for the users to set the viewing permission in the user group.
Has the advantages that: the user establishes the user group according to the requirement, the user opens the viewing authority of the user group member which allows viewing the personal detection result, and the member can directly view the detection result of the user through the verification of the database. Thus, the user may not be around the person who wants to care about, and may also know their physical health condition in time.
Further, the user terminal also comprises a user experience module for generating a temporary identity and acquiring detection data and a detection result.
Has the advantages that: the temporary identity can acquire detection data and detection results, registration is not needed, and the intelligent closestool is suitable for users who do not use the intelligent closestool for a long time.
Further, data transmission is carried out among the intelligent closestool, the database and the user terminal through wireless communication.
Has the advantages that: in the aspect of network installation and maintenance, the wireless communication cost is low, wiring is not needed, deployment is easy, and management is not needed to be performed by too much manpower.
Further, the intelligent closestool also comprises an association module, and the association module is used for generating a two-dimensional code containing the intelligent closestool information.
Has the advantages that: and scanning the two-dimensional code to obtain the information of the intelligent closestool.
Further, the user terminal further comprises a scanning module, and the scanning module is used for scanning the two-dimension code, identifying the intelligent closestool information and associating the intelligent closestool.
Has the advantages that: the two-dimensional code is scanned, the intelligent closestool information is automatically identified, manual input of the intelligent closestool information is replaced, operation steps of the associated intelligent closestool are simplified, and the intelligent closestool is simpler to use and easy to operate.
Further, the intelligent closestool is also used for collecting the detection data of the heart rate and the body fat of the user.
Has the advantages that: heart rate can be used to determine heart conditions, for example, healthy adults have a heart rate of 60-100 beats per minute, mostly 60-80 beats/minute, adults have a heart rate of more than 100 beats per minute (typically no more than 160 beats/minute) or infants have a heart rate of more than 150 beats/minute, and sinus tachycardia is known. It is commonly seen in normal people with exercise, excitement or fever, shock, anemia, hyperthyroidism, heart failure, etc.; the heart rate is 160-220 times/min, and the heart rate is often called paroxysmal tachycardia; those with a heart rate below 60 beats/minute (typically above 40 beats/minute) are called sinus bradycardia. Heart rate is below 40 beats/minute and atrioventricular block should be considered. The heart rate is too fast to be 160 beats/minute or less than 40 beats/minute, which is most common in patients with heart disease.
Body fat can represent the proportion of body fat occupancy. The method provides more detection aspects for the artificial intelligent prediction model, is beneficial to comparing the artificial intelligent prediction model to obtain a detection result with higher matching degree, and improves the accuracy of the detection result.
Furthermore, the user terminal also comprises a voice input module, the voice detection module is used for generating a body condition inquiry table, and is also used for collecting voice information answered by the user according to the body condition inquiry table, recognizing the voice information to obtain detection data, and sending the detection data to the database and the analysis module.
Has the advantages that: the voice information of the user describing the body condition of the user comprises fatigue degree, sleep condition, mood state, diet condition and the like, detection data of the analysis module is supplemented, detection data in the artificial intelligence prediction model can be supplemented, detection is corrected, and the obtained detection result is more accurate. For example, if a user drinks food violently recently, undigested food residues can remain in excrement, so that the content of glucose in the detection data can appear, but the artificial intelligent prediction model cannot predict the error detection result of diabetes by supplementing the detection data of the self body condition input by the voice of the user.
Drawings
Fig. 1 is a flowchart of a medical health management system according to a first embodiment of the medical health management system.
Detailed Description
The following is further detailed by way of specific embodiments:
example one
As shown in fig. 1, the medical health management system in the embodiment includes an intelligent toilet, a server and a user terminal; the server comprises a database and an analysis module;
the intelligent closestool is used for collecting detection data of urine and excrement of a user and sending the detection data to the database and the analysis module; specifically, the intelligent toilet bowl is a device equipped with a urine detection sensor and a feces detection sensor, and is further equipped with a body fat sensor and a heart rate sensor, wherein the body fat sensor collects body fat of a user as a part of detection data, and the heart rate sensor collects a heart rate of the user as a part of the detection data. Specifically, the body fat rate is the fat content in the human body, the heart rate is the number of beats per minute of the heart, the body fat needs to be capable of feeding back the body fat content of the user, and if the fat content is more than 20% of the body weight, the user is overnourished and lack of exercise; the body fat rate is too low, indicating that the user is malnourished and needs to be supplemented with nutrients.
In the embodiment, the intelligent closestool disclosed in the Chinese patent publication No. CN110295658A file is selected by me to realize the acquisition of detection data, and in addition, a WIFI module can be arranged to upload the detection data to a server (or a database) through a router. In other embodiments, the test data may be manually input by the user after the test strip tests the urine and fecal dilutions. The user terminal is used for sending a login request to the database by a user. Specifically, the user terminal is a smart phone which can access a specific website through a browser, and accesses a server (or a database) through WIFI or a wireless network. In other embodiments, the application may be a smart device such as a tablet computer, or a smart phone/tablet computer loaded with a specific APP.
In the use, if need bind intelligent closestool and smart mobile phone, can produce the two-dimensional code through intelligent closestool, the mode that the smart mobile phone scanned specifically is, intelligent closestool still includes the correlation module, and user terminal still includes the scanning module, and the correlation module generates the two-dimensional code that contains intelligent closestool information. The scanning module scans the two-dimensional code, identifies the information of the intelligent closestool and associates the intelligent closestool.
The analysis module is used for analyzing the detection data according to the artificial intelligence prediction model and obtaining a detection result, wherein the detection result comprises a health assessment report and a risk prompt, and in the embodiment, the risk prompt is the probability of a certain disease. The artificial intelligence prediction model is a neural network prediction model formed by training a plurality of groups of original detection data and original detection results, the original detection data are acquired detection data of a user at a time point A, and the original detection results are hospital physical examination results input by the user and within a preset time limit from the time point A.
The analysis module is part of the server and essentially a processor of the server. The essence of the artificial intelligence predictive model is that a certain amount of training has been performed before the user purchases it. The content of the training is such that the model records a mapping relationship (or a prediction relationship) between the original detection data (detection data in normal use) and the original detection result (detection result in normal use). Both the raw test data and the raw test results can be obtained by prior data acquisition or by simulation using hospital-provided data. After the model is normally used, the detection result of the user can be output according to the detection data of the user, the user can know the health condition of the user more conveniently, and therefore the user can seek medical advice or change living habits in time. After the user timely gets a doctor, the user can also feed back the detection result of the hospital to the server through the user terminal, and the artificial intelligence prediction model in the server compares and learns the detection result output by the user and the detection result given by the hospital, so that the accuracy of the subsequent detection result is improved. In addition, in the actual use process of the user, the server compares the detection result of the previous prediction with the detection data when the prediction date is reached, so that self-model correction is realized, and the model is more suitable for the user.
For example, the user uses the intelligent closestool 1 day in the month, the intelligent closestool acquires the current detection data of the user, the server gives a detection result according to the artificial intelligence prediction model, and the core content in the detection result is that the user can have diarrhea due to the change of flora 2 days later. Then the user uses the intelligent closestool again 2 days later, the intelligent closestool acquires the detection data of the user again, verifies the deviation amount with the previous detection result, and outputs a new detection result. By the mode, the user can continuously carry out optimization training on the artificial intelligence prediction model in the actual use process, so that the artificial intelligence prediction model can adapt to the individual difference of the user and give a more accurate detection result.
Of course, the input of the detection data and the detection result can be input by voice. The user terminal further comprises a voice module, the voice module is used for outputting the preset body condition inquiry table to the user in a voice mode, collecting voice information answered by the user according to the body condition inquiry table, recognizing the recognized voice information into detection data and sending the detection data to the server (or the database and the analysis module).
The database is used for storing detection data and detection results, and is also used for receiving the login request, verifying whether the login request passes or not, and sending the detection data and the detection results to the user terminal after the user terminal passes the verification. The user terminal also comprises a user management module, the user management module is used for registering login information of a user and sending the login information to the database, and the user management module is also used for modifying the login information; the database is also used to store login information. The database also comprises a grouping module, wherein the grouping module is used for creating a user group by the user, the user group comprises 2 or more users, and the grouping module is also used for setting the viewing permission in the user group by the user. In this embodiment, the user X and 1 or more users Y together create a user group, and the user X sets a viewing permission for viewing the detection result of the user X. The database is also essentially part of the server and mainly serves as a data store. In this embodiment, the database mainly stores the detection data and the detection result, and certainly, the database also needs to store the personal information of the user, so as to facilitate the user to log in. The user of the same intelligent closestool may have 3 or more people, and the user is required to input a login request through the intelligent mobile phone so as to verify the identity of the user. The functions of registration, login, grouping, and the like are mature in the prior art, and are not described herein again.
In practical use, except that the family of the user can use the intelligent closestool for a long time and needs to perform identity verification, when the guest of the user uses the intelligent closestool at home, the embodiment also provides a function of temporary identity generation. The user main terminal also comprises a user experience module which is used for generating temporary identity and acquiring detection data and detection results. In this embodiment, the user obtains the temporary identity through the user experience module, the user side sends a login request including the temporary identity to the database, and when the database verifies that the login request is the temporary identity, the user side passes the verification and sends detection data and a detection result to the user side. The detection data and the detection result corresponding to the temporary identity are output only once, and the artificial intelligent prediction model cannot be continuously optimized, but can be conveniently used by guests.
When the intelligent mobile phone is used specifically, a user logs in a specific website by using the intelligent mobile phone and registers login information of the user. When the intelligent closestool is used for detection, the intelligent mobile phone scans the two-dimensional code generated on the intelligent closestool, and the binding between the user and the intelligent closestool is completed. The intelligent closestool collects the detection data of gas, body fat and heart rate discharged by urine, excrement and intestinal tracts, and sends the detection data to the database and the analysis module through WIIF. The database stores the detection data, and the artificial intelligence prediction module in the analysis module generates a detection result according to the detection data of the user and stores the detection result into the database. The user logs in a specific website on the smart phone, and the database sends the detection data and the detection result to the smart phone of the user after verifying the login information of the user.
Example two
Compared with the first embodiment, the difference is only that: the intelligent toilet also includes a sensor for collecting the gas composition emitted by the bowel and using the gas composition as part of the sensed data. In particular, the flatus discharged from the intestinal tract is generally known as the discharged flatus, and the flatus discharged from the human body can reflect the health condition of the gastrointestinal tract of the human body to a certain extent. The intelligent closestool also comprises a facial detection module for collecting the facial expression of the user when the intestinal gas is discharged, and the state of the facial expression is used for correcting the final detection result after analysis and processing. In particular, the facial detection module employs a camera to capture facial expressions of the user. And then the intelligent closestool corrects the detected gas component information of the wind according to the facial expression of the user. The main reason is that when the user discharges the gas in the intestinal tract, a certain physiological experience can be fed back through the facial expression. If the process of gas emission is painful, the intestinal tract is prevented from having certain hidden danger; if the venting of gas is a comfortable and relaxing process, it may be the result of something that the user has recently consumed, but the parenteral has a potential risk. The method comprises the steps of collecting facial expressions of a user while exhausting air, and then correcting results of gas components (a specific correction scheme is that if the expressions of the user are reserved and comfortable, the gas components exhausted by the user cannot be collected at this time, and the intestinal tract of the user is considered to be healthy), so that the whole detection data can be obtained more accurately.
The foregoing is merely an example of the present invention and common general knowledge of known specific structures and features of the embodiments is not described herein in any greater detail. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (10)
1. Medical health management system, including intelligent closestool, server and user terminal, its characterized in that:
the server comprises a database and an analysis module, wherein:
the intelligent closestool is used for collecting the detection data of the urine and the excrement of the user and sending the detection data to the database and the analysis module;
the user terminal is used for sending a login request to the database by a user;
the analysis module is used for analyzing the detection data according to the artificial intelligence prediction model and obtaining a detection result, and the detection result comprises a health assessment report and a risk prompt;
the database is used for storing detection data and detection results, and is also used for receiving the login request, verifying whether the login request passes or not, and sending the detection data and the detection results to the user terminal after the user terminal passes the verification.
2. The medical health management system of claim 1, wherein: the artificial intelligence prediction model is a neural network prediction model formed by training a plurality of groups of original detection data and original detection results, the original detection data are acquired detection data of a user at a time point A, and the original detection results are hospital physical examination results input by the user and after a preset time limit from the time point A.
3. The medical health management system of claim 1, wherein: the user terminal also comprises a user management module, the user management module is used for registering login information of a user and sending the login information to the database, and the user management module is also used for modifying the login information; the database is also used to store login information.
4. The medical health management system of claim 1, wherein: the database also comprises a grouping module, wherein the grouping module is used for users to create user groups, the user groups comprise more than 2 users, and the grouping module is also used for the users to set viewing permission in the user groups.
5. The medical health management system of claim 3, wherein: the user main terminal also comprises a user experience module which is used for generating temporary identity and acquiring detection data and detection results.
6. The medical health management system of claim 1, wherein: and the intelligent closestool, the database and the user terminal are in data transmission through wireless communication.
7. The medical health management system of claim 1, wherein: the intelligent closestool also comprises an association module, and the association module is used for generating the two-dimensional code containing the intelligent closestool information.
8. The medical health management system of claim 5, wherein: the user terminal further comprises a scanning module, and the scanning module is used for scanning the two-dimensional code, identifying the information of the intelligent closestool and associating the intelligent closestool.
9. The medical health management system of claim 1, wherein: the intelligent closestool is also used for collecting the detection data of the heart rate and the body fat of the user.
10. The medical health management system of claim 1, wherein: the user terminal also comprises a voice input module, the voice detection module is used for outputting the body condition inquiry table to the user in a voice mode, collecting and collecting voice information answered by the user according to the body condition inquiry table, recognizing the recognized voice information as detection data and sending the detection data to the database and the analysis module.
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| CN201911167243.3A CN110993043A (en) | 2019-11-25 | 2019-11-25 | Medical health management system |
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| CN201911167243.3A CN110993043A (en) | 2019-11-25 | 2019-11-25 | Medical health management system |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111739620A (en) * | 2020-06-16 | 2020-10-02 | 武汉光谷联合医学检验所股份有限公司 | Detection information management method, device, equipment and storage medium |
| CN113113026A (en) * | 2021-04-15 | 2021-07-13 | 重庆德方信息技术有限公司 | Voiceprint identity authentication system and intelligent detection closestool based on home user level |
| CN113240326A (en) * | 2021-06-02 | 2021-08-10 | 黄淮学院 | Production management detection system for civil engineering based on big data |
| CN117147544A (en) * | 2023-08-30 | 2023-12-01 | 深圳市国邦生物技术有限公司 | Urine detection and analysis system of intelligent closestool |
-
2019
- 2019-11-25 CN CN201911167243.3A patent/CN110993043A/en not_active Withdrawn
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111739620A (en) * | 2020-06-16 | 2020-10-02 | 武汉光谷联合医学检验所股份有限公司 | Detection information management method, device, equipment and storage medium |
| CN113113026A (en) * | 2021-04-15 | 2021-07-13 | 重庆德方信息技术有限公司 | Voiceprint identity authentication system and intelligent detection closestool based on home user level |
| CN113240326A (en) * | 2021-06-02 | 2021-08-10 | 黄淮学院 | Production management detection system for civil engineering based on big data |
| CN113240326B (en) * | 2021-06-02 | 2022-10-18 | 黄淮学院 | Production management detection system for civil engineering based on big data |
| CN117147544A (en) * | 2023-08-30 | 2023-12-01 | 深圳市国邦生物技术有限公司 | Urine detection and analysis system of intelligent closestool |
| CN117147544B (en) * | 2023-08-30 | 2024-07-12 | 深圳市国邦生物技术有限公司 | Urine detection and analysis system of intelligent closestool |
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Application publication date: 20200410 |