CN113470773A - Cardiovascular disease evaluation management system based on big data - Google Patents
Cardiovascular disease evaluation management system based on big data Download PDFInfo
- Publication number
- CN113470773A CN113470773A CN202110628768.3A CN202110628768A CN113470773A CN 113470773 A CN113470773 A CN 113470773A CN 202110628768 A CN202110628768 A CN 202110628768A CN 113470773 A CN113470773 A CN 113470773A
- Authority
- CN
- China
- Prior art keywords
- score
- following table
- determined according
- probability
- age
- 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.)
- Pending
Links
Images
Classifications
-
- 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
- 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
-
- 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
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
-
- 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
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Abstract
The invention relates to a cardiovascular disease evaluation management system based on big data, which comprises a resident end, a community hospital and second-level hospital doctor end, a third-level hospital doctor end and a health administration department management end. The resident side comprises a basic data management system, a filing system, a daily monitoring system, a cardiovascular disease risk prediction system, a prompt system, an education system and an exchange system. The doctor end of community hospital and second grade hospital includes: (1) a notification system; (2) a diagnosis and treatment management system; (3) a referral and consultation system; (4) an alternating current system. The third grade hospital doctor end includes: (1) a receiving system; (2) a diagnosis and treatment management system; (3) a guidance system; (4) an alternating current system. The system provided by the invention can reduce manpower, material resources and financial resources for chronic disease management and optimize the utilization of sanitary resources.
Description
Technical Field
The invention relates to the technical field of medical treatment, in particular to a cardiovascular disease evaluation management system based on big data.
Background
Chronic cardiovascular metabolic diseases are major disease burden and challenge facing all countries in the world, and with the development of social economy, cardiovascular diseases such as stroke and ischemic heart disease are the first cause of death in China. The 2016 data show that 240 thousands of people in China die directly of chronic cardiovascular diseases, and meanwhile, the chronic cardiovascular diseases in China are increased by about 50% between 2010 and 2030. The 7 th national census shows that the population is about 2.6 hundred million over 60 years old in China and accounts for about 18.7 percent of the total population, China already enters an old society, and the expenditure of medical expenses is also a large economic burden of the whole country, especially chronic diseases which are continuous and lifelong, such as cardiovascular metabolic diseases, hypertension, coronary heart diseases, diabetes, cerebral apoplexy and the like. How to prevent and control a plurality of common chronic cardiovascular diseases under the condition of not being rich, reduce the national medical expense and relieve ' difficult and expensive ' seeing a doctor ' is an important content for preventing and controlling the chronic diseases at present. The compendium of the health China 2030 is also focused on the early prevention, diagnosis, treatment, management and control of chronic cardiovascular diseases. Nevertheless, the traditional management mode and technology for cardiovascular chronic diseases cannot meet the requirements of modern chronic disease prevention and control, and cannot effectively reduce the incidence of chronic diseases, improve the control rate, and effectively save resources such as manpower, material resources, financial resources and the like. The advancement of internet technology provides new ideas and methods for the prevention and control management of chronic diseases, and for this reason, the opinions on the development of promoting the internet + medical health encourage the recommendation of achieving the goals in the national health china 2030 programming by means of internet technology, wherein an important aspect is the long-term prevention and control management of chronic cardiovascular metabolic diseases.
However, the conventional chronic management mode (taking hypertension as an example) is: patients are uncomfortable to see a doctor (community, second-level and third-level hospitals), all levels of hospitals clearly diagnose hypertension, the community, the second-level and third-level hospitals receive treatment, regular follow-up prescription medicines, and poor disease control and diagnosis to the superior hospitals. The defects of the modes are that people who do not suffer from diseases lack the initiative of disease prevention (low hypertension awareness rate), patients lack the knowledge of disease and health management (low hypertension treatment rate), interaction motivation with medical care is low (low hypertension control rate), risk factors of upstream cardiovascular diseases cannot be effectively prevented and controlled, the morbidity of downstream cardiovascular complications (coronary heart disease, stroke, heart failure and the like) is caused, and the disability rate is increased. Meanwhile, the community heart passively deals with the existence of chronic disease management, such as examination, misreport and missed report of related data, and data distortion (waste of labor cost of medical treatment) is caused. The invention can effectively mobilize the active prevention and control enthusiasm of people at all ages, and the sick and non-sick people, and improve the prevention, control and management effects of the chronic cardiovascular diseases.
Disclosure of Invention
In order to achieve the above purpose, the invention provides the following technical scheme:
the invention provides a cardiovascular disease evaluation management system based on big data, which comprises a resident end, a community hospital and second-level hospital doctor end, a third-level hospital doctor end and a health administration department management end.
Preferably, the resident side comprises a basic data management system, a filing system, a daily monitoring system, a cardiovascular disease prediction system, a prompt system, an education system and a communication system.
Preferably, the real-name registration is carried out by adopting the modes of the WeChat applet, the public number, the mobile phone APP and the like, and basic data of the registered real-name registration is recorded into the basic data management system by the modes of the WeChat applet, the public number, the mobile phone APP and the like.
Preferably, the resident entered data includes, but is not limited to: name, sex, age, identification number, community of residence, telephone number, emergency contact telephone number, existence of existing confirmed cardiovascular disease, diagnosis conclusion of existing cardiovascular disease (picture can be taken and uploaded), and treatment scheme adopted at present (picture can be taken and uploaded).
Preferably, in the profiling system, the resident can inquire the community hospital doctors of the community where the resident is located, and select one doctor as the profiling doctor, and then complete the profiling of the chronic cardiovascular disease.
Preferably, in the daily monitoring system, the patient can input relevant detection parameters manually or through OCR recognition, and daily detection data uploading can be completed by adopting wearable health monitoring equipment.
Preferably, an integrated detection device is arranged in a community hospital to realize the entry of data related to cardiovascular diseases, and the integrated detection device integrates detection equipment including but not limited to a blood pressure detection device, a weight and height detection device, a blood fat detection device, a blood sugar detection device, a uric acid detection device, an electrocardiogram detection device and the like. When the device is used, the device can realize one-stop acquisition of data related to cardiovascular diseases with the help of medical care personnel, and can realize one-time uploading through the integrated detection device. The integrated detection device can greatly save the time for residents to check and wait at different detection places, and in addition, the system is favorable for residents (such as old people) unfamiliar with the operation of the smart phone to quickly and accurately report the detection data.
Preferably, the cardiovascular disease prediction system comprises the following two subsystems: (1) the unaffected resident subsystem is used for predicting the probability of suffering from the chronic cardiovascular disease of the unaffected residents; (2) and the sick resident subsystem is used for predicting the probability of suffering the downstream chronic cardiovascular diseases of the sick residents.
Preferably, the non-diseased resident subsystem comprises the following modules: the device comprises a 2-year coronary heart disease prediction module, a 10-year atrial fibrillation prediction module, a severe coronary heart disease (myocardial infarction or coronary heart disease death) prediction module within 10 years and an intermittent claudication probability prediction module within 4 years.
Preferably, the diseased resident subsystem includes the following modules: the module comprises a stroke prediction module for 5 years for patients with atrial fibrillation, a probability prediction module for patients with coronary heart disease or hypertension to have heart failure within 4 years, and a probability of coronary heart disease recurrence within 2 years for patients with coronary heart disease.
Preferably, any one of the modules of the resident subsystem without the disease and the resident subsystem with the disease comprises an information prompting sub-module, a data input sub-module and a calculating sub-module.
Preferably, the information prompting sub-module is configured to: prompting residents to use data required to be input by the module; and prompting the resident of the calculation result after the calculation is completed.
Preferably, the information input submodule is configured to: and the residents input data according to the prompt information of the information prompt submodule.
Preferably, the calculation submodule is configured to: and calculating to obtain a prediction result according to the data input by residents.
Preferably, the information prompting sub-module adopts hardware such as a display screen, a loudspeaker or an intelligent bracelet to realize information prompting.
Preferably, the information input sub-module adopts hardware such as a keyboard and a touch screen to realize data input.
Preferably, when the resident uses the information prompting sub-module, the resident selects the disease prediction module concerned by the resident, inputs the parameters required for prediction of the module into the information input sub-module under the guidance of the information prompting sub-module, and outputs the obtained prediction result in the information prompting sub-module after the calculation of the calculation sub-module.
It is worth noting that the cardiovascular disease prediction system provided by the invention is used for exploring and predicting the health state of residents, and does not diagnose and treat cardiovascular diseases of the residents, in fact, a great number of residents do not suffer from the cardiovascular diseases when using the system, the system is beneficial to self preliminary evaluation of suffering probability, and the residents who do not suffer from the cardiovascular diseases can predict the chronic cardiovascular disease probability; the sick residents can predict the probability of the downstream cardiovascular diseases, communicate with medical care, receive corresponding further evaluation and medical advice (the stage may relate to diagnosis and treatment), receive corresponding chronic disease prevention and control knowledge, and carry out patient education.
Preferably, the reminder system comprises: the system comprises a sudden risk prompting module, a prediction result prompting module, a doctor seeing prompting module and a medicine taking prompting module.
The sudden risk prompting module: when detecting that residents in data input by the daily detection system are in a high risk or emergency medical treatment state, sending information to an emergency contact telephone number reserved in the system; at the same time, the documenting physician is informed in order to take emergency medical intervention.
A prediction result prompting module: the prediction result obtained by the cardiovascular disease prediction system is output, and the resident is informed of the prediction result, and meanwhile, the resident can choose to send the prediction result to the profiling doctor so that the profiling doctor can know the condition of the patient.
The diagnosis prompting module: the documenting doctor can remind residents to complete regular re-examinations through the consultation prompting module, and the residents can also manually enter and reserve expected re-diagnosis doctors and re-diagnosis time in the consultation prompting module.
The medicine taking prompt module: after the patient returns home with the medicine, the patient can be reminded to take the medicine according to the medical advice and the medicine taking scheme.
Preferably, the educational system includes a periodic push subsystem and an intelligent assistant subsystem.
The regular pushing subsystem is used for regularly pushing relevant knowledge for preventing and treating diseases according to cardiovascular diseases suffered by residents, helps residents to perform self-education management, arouses enthusiasm, revises life style and science popularization medical knowledge, and improves the re-diagnosis and compliance to medical measures.
The intelligent assistant subsystem is used for helping to solve the problems related to life styles such as resident eating habits and daily work and rest, and after the patient inputs related questions, the background automatically identifies keywords according to prestored information of the database and replies related popular science knowledge.
Preferably, the communication system is used for realizing communication between residents and doctors, and online communication can be carried out between the residents and the doctors, and voice communication can also be carried out through reserved telephones.
Preferably, the doctor end of the community hospital and the secondary hospital comprises: (1) a notification system; (2) a diagnosis and treatment management system; (3) a referral and consultation system; (4) an alternating current system.
Preferably, the notification system is adapted to: (1) informing the doctor that a new resident selects the resident as a filing doctor; (2) and receiving alarm information of the emergency risk prompting module, and informing a doctor to take emergency measures conveniently.
Preferably, the medical management system is configured to: the doctor adds the patient list and the patient case history, edits the patient case history data, sends the patient disease diagnosis suggestion, sets the patient medical reminding and medicine taking reminding, and gives the patient an electronic prescription for medicines.
Preferably, the referral, consultation system is used for: (1) initiating consultation and service instruction requests to doctors in the third-level hospital; (2) and sending a referral request to a doctor in the third-level hospital.
Preferably, the communication system is adapted to: (1) a doctor in a community hospital communicates with residents; (2) the doctors in the community hospitals and the doctors in the secondary hospitals communicate with the doctors in the tertiary hospitals. Similarly, the communication may be performed online, or voice communication may be performed through a reserved telephone.
Preferably, the doctor end of the third-class hospital comprises: (1) a receiving system; (2) a diagnosis and treatment management system; (3) a guidance system; (4) an alternating current system; (5) and (5) a referral system is carried out downwards.
As a preferred embodiment, in addition to the above, the receiving system is further configured to: receiving consultation and service guide requests initiated by doctors in community hospitals and secondary hospitals; receiving referral requests initiated by doctors in community hospitals and secondary hospitals.
Preferably, the medical management system is configured to: the doctor adds the patient list and the patient case history, edits the patient case history data, sends the patient disease diagnosis suggestion, sets the patient medical reminding and medicine taking reminding, and gives the patient an electronic prescription for medicines.
Preferably, the guidance system is for: (1) the consultation business service guide request is sent to the community hospital and the secondary hospital doctors according to the consultation initiated by the community hospital and the secondary hospital doctors, and the consultation result is sent to the community hospital and the secondary hospital doctors; (2) after receiving the treatment of the patient, the third-level hospital transfers the treatment to the second-level hospital or the community hospital or gives the management guidance suggestion of chronic diseases according to the state of the patient. (3) The advanced diagnosis and treatment means progress and the latest research results in related fields are sent to doctors of community hospitals and secondary hospitals, so that the doctors of the community hospitals and the secondary hospitals can improve the service capability.
Preferably, the communication system is adapted to: (1) the doctor in the third-level hospital communicates with residents; (2) the doctors in the third-level hospital communicate with the doctors in the community and the second-level hospital. Similarly, the communication may be performed online, or voice communication may be performed through a reserved telephone.
In a preferred embodiment, based on the above, the referral-down system is further configured to: the stable chronic disease patient is transferred to a second-level, community and rehabilitation medical institution, so that the patient can see a doctor nearby and the pressure of a third-level hospital is relieved.
Preferably, the health administration management end comprises a data statistics module and a supervision management module.
Preferably, the data statistics module is used for counting the conditions of cardiovascular diseases such as morbidity, treatment, rehabilitation and graded diagnosis and treatment, providing the conditions for reference for health administration departments, and providing decision bases for decision-making mechanisms to control and manage chronic cardiovascular metabolic diseases.
Compared with the prior art, the invention has the beneficial effects that:
(1) the early prediction evaluation, intervention, diagnosis and treatment of the disease are realized, the illness state of the patient is followed up in time, and a positive feedback closed loop circulation type chronic disease management mode is formed.
(2) The dynamic quantification of the disease risk is realized, the chronic cardiovascular metabolic disease mode is dynamically managed in a hierarchical manner according to the disease risk, and the hierarchical classification management is realized according to the condition of the patient, wherein the high-risk patient mainly carries out two-level and three-level intervention of a medical institution, the low-risk patient mainly carries out self-management of a life style and health education, the manpower, material resources and financial resources for chronic disease management are reduced, and the utilization of sanitary resources is optimized.
(3) The probability of chronic cardiovascular disease can be predicted by the sick residents; the sick residents can predict the probability of the downstream cardiovascular diseases, and the method is favorable for the patients to expect the possible disease risks and perform targeted prevention and control. On the basis, the patient can preliminarily evaluate the disease probability by himself, communicates with medical care by means of the platform, receives corresponding further evaluation and medical advice, and can periodically push the chronic disease prevention and control knowledge through the system to educate the patient. Improve the adhesiveness and the compliance of the chronic disease patients to cardiovascular disease management, and reduce the medical cost.
(4) The integrated detection device can greatly save the time for residents to check and wait at different detection places, and in addition, the system is favorable for residents (such as old people) unfamiliar with the operation of the smart phone to quickly and accurately report the detection data.
Description of the drawings:
fig. 1 is a schematic diagram of a cardiovascular disease assessment management system based on big data.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments.
Thus, the following detailed description of the embodiments of the invention is not intended to limit the scope of the invention as claimed, but is merely representative of some embodiments of the invention. 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.
It should be noted that the embodiments of the present invention and the features and technical solutions thereof may be combined with each other without conflict.
The invention provides a cardiovascular disease evaluation management system based on big data, which comprises a resident end, a community hospital and second-level hospital doctor end, a third-level hospital doctor end and a health administration department management end.
Preferably, the resident side comprises a basic data management system, a filing system, a daily monitoring system, a cardiovascular disease risk prediction system, a prompt system, an education system and a communication system.
Preferably, the real-name registration is carried out by adopting the modes of the WeChat applet, the public number, the mobile phone APP and the like, and basic data of the registered real-name registration is recorded into the basic data management system by the modes of the WeChat applet, the public number, the mobile phone APP and the like.
Preferably, the resident entered data includes, but is not limited to: name, sex, age, identification number, community of residence, telephone number, emergency contact telephone number, existence of existing confirmed cardiovascular disease, diagnosis conclusion of existing cardiovascular disease (picture can be taken and uploaded), and treatment scheme adopted at present (picture can be taken and uploaded).
Preferably, in the profiling system, the resident can inquire the community hospital doctors of the community where the resident is located, and select one doctor as the profiling doctor, and then complete the profiling of the chronic cardiovascular disease.
Preferably, in the daily monitoring system, the patient can input relevant detection parameters manually or through OCR recognition, and daily detection data uploading can be completed by adopting wearable health monitoring equipment.
Preferably, an integrated detection device is arranged in a community hospital to realize the entry of data related to cardiovascular diseases, and the integrated detection device integrates detection equipment including but not limited to a blood pressure detection device, a weight and height detection device, a blood fat detection device, a blood sugar detection device, a uric acid detection device, an electrocardiogram detection device and the like. When the device is used, the device can realize one-stop acquisition of data related to cardiovascular diseases with the help of medical care personnel, and can realize one-time uploading through the integrated detection device. The integrated detection device can greatly save the time for residents to check and wait at different detection places, and in addition, the system is favorable for residents (such as old people) unfamiliar with the operation of the smart phone to quickly and accurately report the detection data.
Preferably, the cardiovascular disease prediction system comprises the following two subsystems: (1) the unaffected resident subsystem is used for predicting the probability of suffering from the chronic cardiovascular disease of the unaffected residents; (2) and the sick resident subsystem is used for predicting the probability of suffering the downstream chronic cardiovascular diseases of the sick residents.
Preferably, the non-diseased resident subsystem comprises the following modules: the device comprises a 2-year coronary heart disease prediction module, a 10-year atrial fibrillation prediction module, a severe coronary heart disease (myocardial infarction or coronary heart disease death) prediction module within 10 years and an intermittent claudication probability prediction module within 4 years.
Preferably, the diseased resident subsystem includes the following modules: the module comprises a stroke prediction module for 5 years for patients with atrial fibrillation, a probability prediction module for patients with coronary heart disease or hypertension to have heart failure within 4 years, and a probability of coronary heart disease recurrence within 2 years for patients with coronary heart disease.
Preferably, any one of the modules of the resident subsystem without the disease and the resident subsystem with the disease comprises an information prompting sub-module, a data input sub-module and a calculating sub-module.
Preferably, the information prompting sub-module is configured to: prompting residents to use data required to be input by the module; and prompting the resident of the calculation result after the calculation is completed.
Preferably, the information input submodule is configured to: and the residents input data according to the prompt information of the information prompt submodule.
Preferably, the calculation submodule is configured to: and calculating to obtain a prediction result according to the data input by residents.
Preferably, the information prompting sub-module adopts hardware such as a display screen, a loudspeaker or an intelligent bracelet to realize information prompting.
Preferably, the information input sub-module adopts hardware such as a keyboard and a touch screen to realize data input.
Preferably, when the system is used, the resident selects the disease risk prediction module concerned by the resident, inputs parameters required for prediction of the module into the information input submodule under the guidance of the information prompting submodule, and outputs the obtained prediction result in the information prompting submodule after the calculation of the calculation submodule.
It is worth noting that the cardiovascular disease prediction system provided by the invention is used for exploring and predicting the health state of residents, and does not diagnose and treat cardiovascular diseases of the residents, in fact, a great number of residents do not suffer from the cardiovascular diseases when using the system, the system is beneficial to self preliminary evaluation of suffering probability, and the residents who do not suffer from the cardiovascular diseases can predict the chronic cardiovascular disease probability; the sick residents can predict the probability of the downstream cardiovascular diseases, communicate with medical care, receive corresponding further evaluation and medical advice (the stage may relate to diagnosis and treatment), receive corresponding chronic disease prevention and control knowledge, and carry out patient education.
Preferably, the parameters to be input by the 2-year coronary heart disease prediction module are as follows: age, total cholesterol, high density lipoprotein cholesterol, presence or absence of diabetes, smoking, blood pressure, and systolic blood pressure.
The concrete prediction method of the calculation submodule in the module comprises the following steps:
the first step is as follows: a score of 1 was determined according to the following table.
| Age (age) | Score of |
| 35-39 | 0 |
| 40-44 | 1 |
| 45-49 | 3 |
| 50-54 | 4 |
| 55-59 | 6 |
| 60-64 | 7 |
| 65-69 | 9 |
| 70-74 | 10 |
The second step is that: score 2 was determined according to the following table.
For example, if the total cholesterol is 4.14mmol/L and the HDL cholesterol is 1.29mmol/L, the score is 3.
The third step: a score of 3 was determined according to the following table.
| Diabetes mellitus | Score of |
| Is free of | 0 |
| Is provided with | 3 |
The fourth step: score 4 was determined according to the following table.
| Smoking | Score of |
| Is free of | 0 |
| Is provided with | 4 |
The fifth step: a score of 5 was determined according to the following table.
And a sixth step: the sum of the scores from the first step to the fifth step is accumulated, and the predicted probability of the 2-year first-onset coronary heart disease is looked up in the table below.
| Score of | Probability of 2 years | Score of | Probability of 2 years | Score of | Probability of 2 years |
| 0 | 0％ | 14 | 1％ | 28 | 17％ |
| 2 | 0％ | 16 | 2％ | 30 | 24％ |
| 4 | 0％ | 18 | 3％ | 32 | 32％ |
| 6 | 0％ | 20 | 4％ | 34 | 43％ |
| 8 | 0％ | 22 | 6％ | ||
| 10 | 1％ | 24 | 9％ | ||
| 12 | 1％ | 26 | 12％ |
For example: the resident is 45-49 years old, total cholesterol is 4.14mmol/L and high density lipoprotein cholesterol is 1.29mmol/L, and diabetes, smoking, and blood pressure contraction pressure are <110mmHg under untreated condition. Then, the resident has a score of 3 in the first step, a score of 6 in the second step, a score of 3 in the third step, a score of 4 in the fourth step, and a score of 0 in the fifth step. The total score of the resident is calculated to be 16 in the sixth step, and correspondingly, the probability of the first coronary heart disease in 2 years is 2%.
In a preferred embodiment, based on the above manner, the parameters to be input by the module for predicting atrial fibrillation occurring within 10 years are: age, systolic blood pressure, whether hypertension was treated, weight index, age at which significant heart noise occurred, age at which heart failure occurred.
The concrete prediction method of the calculation submodule in the module comprises the following steps:
the first step is as follows: a score of 1 was determined according to the following table.
The second step is that: score 2 was determined according to the following table.
| Systolic pressure (mmHg) | Score of |
| <160 | 0 |
| ≥160 | 1 |
The third step: a score of 3 was determined according to the following table.
| Whether hypertension is treated | Score of |
| Whether or not | 0 |
| Is that | 1 |
The fourth step: score 4 was determined according to the following table.
| Body mass index (Kg/m)2) | Score of |
| <30 | 0 |
| ≥30 | 1 |
The fifth step: a score of 5 was determined according to the following table.
For example: a score of 5 was assigned when heart overt noise occurred between the ages of 45-54.
And a sixth step: the score 6 was determined according to the following table.
The seventh step: the sum of the scores from the first step to the sixth step is added up and the probability of atrial fibrillation occurring in 10 years is looked up in the table below.
Preferably, the parameters required to be input by the module for predicting severe coronary heart disease (myocardial infarction or death of coronary heart disease) within 10 years are as follows: gender, age, total cholesterol, whether to smoke, HDL, blood pressure systolic pressure.
The concrete prediction method of the calculation submodule in the module comprises the following steps:
for males, the first step: a score of 1 was determined according to the following table.
The second step is that: score 2 was determined according to the following table.
For example: a male aged 40-49 who has total cholesterol (mmol/L) of 6.20-7.21mmol/L and smoked, and who has a score of 2 of 6+5 to 11 points; a male aged 50-59 who has total cholesterol (mmol/L) at 4.14-5.15mmol/L smoked would score 2+ 3-5 points.
The third step: a score of 3 was determined according to the following table.
The fourth step: score 4 was determined according to the following table.
| Blood pressure contraction pressure (mmHg) | Untreated | Treatment of |
| ＜120 | 0 | 0 |
| 120-129 | 0 | 1 |
| 130-139 | 1 | 2 |
| 140-159 | 1 | 2 |
| ≥160 | 2 | 3 |
The fifth step: the sum of the scores from the first step to the fourth step is added up, and the probability of suffering from severe coronary heart disease (myocardial infarction or coronary heart disease death) in men within 10 years is searched in the table below.
| Score of | ≤0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
| Probability (%) | ＜1 | 1 | 1 | 1 | 1 | 2 | 2 | 3 |
| Score of | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |
| Probability (%) | 4 | 5 | 6 | 8 | 10 | 12 | 16 | 20 |
| Score of | 16 | 17 | ||||||
| Probability (%) | ≥25 | ≥30 |
For women, the first step: a score of 1 was determined according to the following table.
The second step is that: score 2 was determined according to the following table;
for example: a woman of age 40-49 who has total cholesterol (mmol/L) of 6.20-7.21mmol/L and who smokes cigarettes, then has a score of 2 of 8+7 ═ 15; a woman of age 50-59 who has total cholesterol (mmol/L) at 4.14-5.15mmol/L smokes a score of 2+4 ═ 6.
The third step: score 3 was determined according to the following table;
the fourth step: score 4 was determined according to the following table.
| Blood pressure contraction pressure (mmHg) | Untreated | Treatment of |
| ＜120 | 0 | 0 |
| 120-129 | 1 | 3 |
| 130-139 | 2 | 4 |
| 140-159 | 3 | 5 |
| ≥160 | 4 | 6 |
The fifth step: the sum of the scores from the first step to the fourth step is added up and the probability of suffering from severe coronary heart disease (myocardial infarction or coronary heart disease death) within 10 years is looked up in the table below.
| Score of | ＜9 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |
| Probability (%) | ＜1 | 1 | 1 | 1 | 1 | 2 | 2 | 3 |
| Score of | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 |
| Probability (%) | 4 | 5 | 6 | 8 | 11 | 14 | 17 | 22 |
| Score of | 24 | ≥25 | ||||||
| Probability (%) | 27 | ≥30 |
In a preferred embodiment, based on the above manner, the module for predicting the probability of intermittent claudication within 4 years needs to input the following parameters: age, gender, total cholesterol, blood pressure, average daily smoking number, whether or not there is diabetes, whether or not there is coronary heart disease.
The concrete prediction method of the calculation submodule in the module comprises the following steps:
the first step is as follows: a score of 1 was determined according to the following table.
| Age (age) | 45-49 | 50-54 | 55-59 | 60-64 | 65-69 | 70-74 | 75-79 |
| Score of | 0 | +1 | +2 | +3 | +4 | +5 | +6 |
The second step is that: score 2 was determined according to the following table.
| Sex | Female with a view to preventing the formation of wrinkles | Male sex |
| Score of | 0 | 3 |
The third step: a score of 3 was determined according to the following table.
The fourth step: score 4 was determined according to the following table.
| Blood pressure | Is normal | Normal high value | Stage 1 hypertension | Stage 2 or above of hypertension |
| Score of | 0 | +1 | +2 | +4 |
The fifth step: a score of 5 was determined according to the following table.
And a sixth step: the score 6 was determined according to the following table.
| Diabetes mellitus | Is free of | Is provided with |
| Score of | 0 | +5 |
The seventh step: the score 7 was determined according to the following table.
| Coronary heart disease | Is free of | Is provided with |
| Score of | 0 | +5 |
Eighth step: the sum of the scores from the first step to the seventh step is added and the probability of intermittent lameness occurring within 4 years is looked up in the table below.
| Score of | ＜10 | 10-12 | 13-15 | 16-17 | 18 | 19 | 20 | 21 |
| Probability (%) | ＜1 | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
| Score of | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 |
| Probability (%) | 8 | 10 | 11 | 13 | 16 | 18 | 21 | 24 |
| Score of | 30 | |||||||
| Probability (%) | 28 |
As a preferred embodiment, on the basis of the above manner, further, the stroke probability prediction module for atrial fibrillation patients within 5 years needs to input the following parameters: age, sex, systolic blood pressure, whether it is diabetic, whether it is suffering from a past stroke or a transient ischemic attack.
The concrete prediction method of the calculation submodule in the module comprises the following steps:
the first step is as follows: a score of 1 was determined according to the following table.
| Age (age) | Score of |
| 55-59 | 0 |
| 60-62 | 1 |
| 63-66 | 2 |
| 67-71 | 3 |
| 72-74 | 4 |
| 75-77 | 5 |
| 78-81 | 6 |
| 82-85 | 7 |
| 86-90 | 8 |
| 91-93 | 9 |
| >93 | 10 |
The second step is that: score 2 was determined according to the following table.
| Sex | Score of |
| Male sex | 0 |
| Female with a view to preventing the formation of wrinkles | 6 |
The third step: a score of 3 was determined according to the following table.
| Systolic pressure (mmHg) | Score of |
| ＜120 | 0 |
| 120-139 | 1 |
| 140-159 | 2 |
| 160-179 | 3 |
| 179 | 4 |
The fourth step: score 4 was determined according to the following table.
| Diabetes mellitus | Score of |
| Is free of | 0 |
| Is provided with | 5 |
The fifth step: a score of 5 was determined according to the following table.
| Past stroke or transient ischemic attack | Score of |
| Is free of | 0 |
| Is provided with | 6 |
And a sixth step: the sum of the scores from the first step to the fifth step is added up, and the stroke probability of atrial fibrillation patients within 5 years is searched in the following table.
Preferably, the parameters required to be input by the probability prediction module for the occurrence of heart failure in 4 years of patients with coronary heart disease or hypertension are: age, sex, systolic blood pressure, heart rate, whether left ventricular hypertrophy is present, whether coronary heart disease is present, whether diabetes is present, and whether valvular disease is present.
The concrete prediction method of the calculation submodule in the module comprises the following steps:
for male patients, the first step: a score of 1 was determined according to the following table.
| Age (age) | 45-49 | 50-54 | 55-59 | 60-64 | 65-69 | 70-74 | 75-79 |
| Score of | 0 | +1 | +2 | +3 | +4 | +5 | +6 |
| Age (age) | 80-84 | 85-89 | 90-94 | ||||
| Score of | +7 | +8 | +9 |
The second step is that: score 2 was determined according to the following table.
| Systolic pressure (mmHg) | ＜120 | 120-139 | 140-169 | 170-189 | 190-219 | >219 |
| Score of | 0 | +1 | +2 | +3 | +4 | +5 |
The third step: a score of 3 was determined according to the following table.
| Heart rate (bpm) | ＜55 | 55-64 | 65-79 | 80-89 | 90-104 | >104 |
| Score of | 0 | +1 | +2 | +3 | +4 | +5 |
The fourth step: score 4 was determined according to the following table.
| Hypertrophy of left ventricle | Is free of | Is provided with |
| Score of | 0 | +4 |
The fifth step: a score of 5 was determined according to the following table.
| Coronary heart disease | Is free of | Is provided with |
| Score of | 0 | +8 |
And a sixth step: the score 6 was determined according to the following table.
| Diabetes mellitus | Is free of | Is provided with |
| Score of | 0 | +1 |
The seventh step: the score 7 was determined according to the following table.
| Valvular disease | Is free of | Is provided with |
| Score of | 0 | +5 |
Eighth step: the sum of the scores from the first step to the seventh step is added up, and the probability of heart failure occurring within 4 years in male patients with coronary heart disease or hypertension is looked up in the following table.
| Score of | Probability (%) | Score of | Probability (%) |
| 5 | 1 | 24 | 30 |
| 10 | 3 | 25 | 34 |
| 12 | 3 | 26 | 39 |
| 14 | 5 | 27 | 44 |
| 16 | 8 | 28 | 49 |
| 18 | 11 | 29 | 54 |
| 20 | 16 | 30 | 59 |
| 22 | 22 |
For female patients, the first step: a score of 1 was determined according to the following table.
| Age (age) | 45-49 | 50-54 | 55-59 | 60-64 | 65-69 | 70-74 | 75-79 |
| Score of | 0 | +1 | +2 | +3 | +4 | +5 | +6 |
| Age (age) | 80-84 | 85-89 | 90-94 | ||||
| Score of | +7 | +8 | +9 |
The second step is that: score 2 was determined according to the following table.
| Systolic pressure (mmHg) | <140 | 140-209 | >209 |
| Score of | 0 | +1 | +2 |
The third step: a score of 3 was determined according to the following table.
| Heart rate (bpm) | <60 | 60-79 | 80-104 | >104 |
| Score of | 0 | +1 | +2 | +3 |
The fourth step: score 4 was determined according to the following table.
| Hypertrophy of left ventricle | Is free of | Is provided with |
| Score of | 0 | +5 |
The fifth step: a score of 5 was determined according to the following table.
| Coronary heart disease | Is free of | Is provided with |
| Score of | 0 | +6 |
And a sixth step: the score 6 was determined according to the following table.
| Diabetes mellitus | Is free of | All (but no valvular disease) | Something (and valvular disease) |
| Score of | 0 | +6 | +2 |
The seventh step: the score 7 was determined according to the following table.
| Valvular disease | Is free of | Is provided with |
| Score of | 0 | +6 |
Eighth step: the score 8 was determined according to the following table.
| Body mass index (kg/m)2) | <21 | 21-25 | 26-29 | >29 |
| Score of | 0 | +1 | +2 | +3 |
Eighth step: the sum of the scores from the first step to the seventh step is added up and the probability of heart failure occurring within 4 years in female patients with coronary heart disease or hypertension is looked up in the following table.
| Score of | Probability (%) | Score of | Probability (%) |
| 5 | 1 | 24 | 30 |
| 10 | 3 | 25 | 34 |
| 12 | 3 | 26 | 39 |
| 14 | 5 | 27 | 44 |
| 16 | 8 | 28 | 49 |
| 18 | 11 | 29 | 54 |
| 20 | 16 | 30 | 59 |
| 22 | 22 |
Preferably, the input parameters of the module for predicting the recurrence probability of coronary heart disease of patients with coronary heart disease within 2 years are as follows: age, total cholesterol, high density lipoprotein cholesterol, whether it is diabetic, whether it is smoking, systolic blood pressure.
The concrete prediction method of the calculation submodule in the module comprises the following steps:
for male patients, the first step: a score of 1 was determined according to the following table.
| Age (age) | 35-39 | 40-44 | 45-49 | 50-54 | 55-59 | 60-64 | 65-69 | 70-74 |
| Score of | 0 | 1 | 3 | 4 | 6 | 7 | 9 | 10 |
The second step is that: score 2 was determined according to the following table.
The third step: a score of 3 was determined according to the following table.
| Diabetes mellitus | Is free of | Is provided with |
| Score of | 0 | +4 |
The fourth step: and accumulating the sum of the scores of the first step to the third step, and searching the probability of coronary heart disease recurrence of the male patient suffering from coronary heart disease within 2 years in the following table.
| Score of | Probability (%) | Score of | Probability (%) | Score of | Probability (%) |
| 0 | 3 | 14 | 9 | 28 | 25 |
| 2 | 4 | 16 | 11 | 30 | 29 |
| 4 | 4 | 18 | 13 | ||
| 6 | 5 | 20 | 14 | ||
| 8 | 6 | 22 | 17 | ||
| 10 | 7 | 24 | 19 | ||
| 12 | 8 | 26 | 22 |
For female patients, the first step: a score of 1 was determined according to the following table.
| Age (age) | 35-39 | 40-44 | 45-49 | 50-54 | 55-59 | 60-64 | 65-69 | 70-74 |
| Score of | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
The second step is that: score 2 was determined according to the following table.
The third step: a score of 3 was determined according to the following table.
| Diabetes mellitus | Is free of | Is provided with |
| Score of | 0 | +3 |
The fourth step: score 4 was determined according to the following table.
| Smoking | Is free of | Is provided with |
| Score of | 0 | +4 |
The fifth step: a score of 5 was determined according to the following table.
And a sixth step: and accumulating the sum of the scores of the first step to the fifth step, and searching the probability of coronary heart disease recurrence of the female patient with coronary heart disease in 2 years in the following table.
Preferably, the reminder system comprises: the system comprises a sudden risk prompting module, a prediction result prompting module, a doctor seeing prompting module and a medicine taking prompting module.
The sudden risk prompting module: when detecting that residents in data input by the daily detection system are in a high risk or emergency medical treatment state, sending information to an emergency contact telephone number reserved in the system; at the same time, the documenting physician is informed in order to take emergency medical intervention.
A prediction result prompting module: the prediction result obtained by the cardiovascular disease prediction system is output, and the resident is informed of the prediction result, and meanwhile, the resident can choose to send the prediction result to the profiling doctor so that the profiling doctor can know the condition of the patient.
The diagnosis prompting module: the documenting doctor can remind residents to complete regular re-examinations through the consultation prompting module, and the residents can also manually enter and reserve expected re-diagnosis doctors and re-diagnosis time in the consultation prompting module.
The medicine taking prompt module: after the patient returns home with the medicine, the patient is reminded to take the medicine according to the medicine taking method, so that the compliance of the patient is improved, and the treatment effect of the patient is promoted to be improved.
Preferably, the educational system includes a periodic push subsystem and an intelligent assistant subsystem.
The regular pushing subsystem is used for regularly pushing relevant knowledge for preventing and treating diseases according to cardiovascular diseases suffered by residents, helps residents to perform self-education management, arouses enthusiasm, revises life style and science popularization medical knowledge, and improves the re-diagnosis and compliance to medical measures.
The intelligent assistant subsystem is used for helping to solve the problems related to life styles such as resident eating habits and daily work and rest, and after the patient inputs related questions, the background automatically identifies keywords according to prestored information of the database and replies related popular science knowledge.
Preferably, the communication system is used for realizing communication between residents and doctors, and online communication can be carried out between the residents and the doctors, and voice communication can also be carried out through reserved telephones.
Preferably, the doctor end of the community hospital and the secondary hospital comprises: (1) a notification system; (2) a diagnosis and treatment management system; (3) a referral and consultation system; (4) an alternating current system.
Preferably, the notification system is adapted to: (1) informing the doctor that a new resident selects the resident as a filing doctor; (2) and receiving alarm information of the emergency risk prompting module, and informing a doctor to take emergency measures conveniently.
Preferably, the medical management system is configured to: the doctor adds the patient list and the patient case history, edits the patient case history data, sends the patient disease diagnosis suggestion, sets the patient medical reminding and medicine taking reminding, and gives the patient an electronic prescription for medicines.
Preferably, the referral, consultation system is used for: (1) initiating consultation and service instruction requests to doctors in the third-level hospital; (2) and sending a referral request to a doctor in the third-level hospital.
Preferably, the communication system is adapted to: (1) a doctor in a community hospital communicates with residents; (2) the doctors in the community hospitals and the doctors in the secondary hospitals communicate with the doctors in the tertiary hospitals. Similarly, the communication may be performed online, or voice communication may be performed through a reserved telephone.
Preferably, the doctor end of the third-class hospital comprises: (1) a receiving system; (2) a diagnosis and treatment management system; (3) a guidance system; (4) an alternating current system; (5) and (5) a referral system is carried out downwards.
As a preferred embodiment, in addition to the above, the receiving system is further configured to: receiving consultation and service guide requests initiated by doctors in community hospitals and secondary hospitals; receiving referral requests initiated by doctors in community hospitals and secondary hospitals.
Preferably, the medical management system is configured to: the doctor adds the patient list and the patient case history, edits the patient case history data, sends the patient disease diagnosis suggestion, sets the patient medical reminding and medicine taking reminding, and gives the patient an electronic prescription for medicines.
Preferably, the guidance system is for: (1) the consultation business service guide request is sent to the community hospital and the secondary hospital doctors according to the consultation initiated by the community hospital and the secondary hospital doctors, and the consultation result is sent to the community hospital and the secondary hospital doctors; (2) after receiving the treatment of the patient, the third-level hospital transfers the treatment to the second-level hospital or the community hospital or gives the management guidance suggestion of chronic diseases according to the state of the patient. And (3) leading-edge diagnosis and treatment means progress and latest research results in related fields are sent to doctors of community hospitals and secondary hospitals, so that the doctors of the community hospitals and the secondary hospitals can improve the service capacity.
Preferably, the communication system is adapted to: (1) the doctor in the third-level hospital communicates with residents; (2) the doctors in the third-level hospital communicate with the doctors in the community and the second-level hospital. Similarly, the communication may be performed online, or voice communication may be performed through a reserved telephone.
In a preferred embodiment, based on the above, the referral-down system is further configured to: the stable chronic disease patient is transferred to a second-level, community and rehabilitation medical institution, so that the patient can see a doctor nearby and the pressure of a third-level hospital is relieved.
Preferably, the health administration management end comprises a data statistics module and a supervision management module.
Preferably, the data statistics module is used for counting the conditions of cardiovascular diseases such as morbidity, treatment, rehabilitation and graded diagnosis and treatment, providing the conditions for reference for health administration departments, and providing decision bases for decision-making mechanisms to control and manage chronic cardiovascular metabolic diseases.
The above embodiments are only used for illustrating the invention and not for limiting the technical solutions described in the invention, and although the present invention has been described in detail in the present specification with reference to the above embodiments, the present invention is not limited to the above embodiments, and therefore, any modification or equivalent replacement of the present invention is made; all such modifications and variations are intended to be included herein within the scope of this disclosure and the appended claims.
Claims (18)
1. A big data based cardiovascular disease assessment management system, characterized by: comprises a resident end, a community hospital and secondary hospital doctor end, a tertiary hospital doctor end and a health administration department management end.
2. The big-data based cardiovascular disease assessment management system of claim 1, wherein: the resident end comprises a basic data management system, a filing system, a daily monitoring system, a cardiovascular disease prediction system, a prompt system, an education system and a communication system.
3. The big-data based cardiovascular disease assessment management system of claim 2, wherein: real-name registration is carried out by adopting the modes of a WeChat applet, a public number, a mobile phone APP and the like, and basic data are input into a basic data management system by the modes of the WeChat applet, the public number, the mobile phone APP and the like after registration; resident entered data includes, but is not limited to: name, sex, age, identification number, community of residence, telephone number, emergency contact telephone number, existence of existing confirmed cardiovascular disease, diagnosis conclusion of existing cardiovascular disease, and currently adopted treatment scheme.
4. The big-data based cardiovascular disease assessment management system according to claim 3, wherein: in the profiling system, a resident can inquire a community hospital doctor of a community in which the resident is located, and select one doctor as its profiling doctor.
5. The big-data based cardiovascular disease assessment management system according to claim 4, wherein: through the daily monitoring system, the patient can input manually and/or OCR discerns and types relevant detection parameter and/or adopt wearing formula health monitoring equipment to accomplish daily detection data upload, preferentially, arrange integrated detection device in the hospital of community in order to realize the input of cardiovascular disease relevant data, integrated detection device has integrateed including but not limited to detection device such as blood pressure test device, weight height detection device, blood lipid detection device, blood sugar detection device, uric acid detection device, heart electrograph detection device, realizes the one-stop collection of cardiovascular disease relevant data, once only uploads under medical personnel's help.
6. The big-data based cardiovascular disease assessment management system according to claim 5, wherein: the cardiovascular disease prediction system includes the following two subsystems: (1) the non-sick resident subsystem is used for predicting the probability of suffering from the chronic cardiovascular disease of non-sick residents; (2) and the sick resident subsystem is used for predicting the probability of suffering the downstream chronic cardiovascular diseases of the sick residents.
7. The big-data based cardiovascular disease assessment management system according to any of claims 1-6, wherein: the unaffected resident subsystem includes the following modules: the device comprises a 2-year coronary heart disease prediction module, a 10-year atrial fibrillation prediction module, a 10-year severe coronary heart disease prediction module and a 4-year intermittent claudication probability prediction module; any one module of the non-diseased resident subsystem comprises an information prompting submodule, a data input submodule and a calculating submodule.
8. The big-data based cardiovascular disease assessment management system according to any of claims 1-6, wherein: the sick resident subsystem comprises the following modules: the module comprises a stroke prediction module for 5 years for patients with atrial fibrillation, a probability prediction module for patients with coronary heart disease or hypertension to have heart failure within 4 years, and a probability of coronary heart disease recurrence within 2 years for patients with coronary heart disease; any one module of the sick resident subsystem comprises an information prompt submodule, a data input submodule and a calculation submodule.
9. The big-data based cardiovascular disease assessment management system according to any of claims 7-8, wherein: when the resident uses the intelligent prediction module, firstly, the concerned prediction module is selected, parameters required by the prediction of the module are input into the information input submodule under the guidance of the information prompting submodule, and the prediction result is output in the information prompting submodule after the calculation of the calculation submodule.
10. The cardiovascular disease prediction system of any of claims 1-6, wherein: the parameters required to be input by the 2-year coronary heart disease prediction module are as follows: age, total cholesterol, high density lipoprotein cholesterol, presence or absence of diabetes, smoking, blood pressure, and systolic blood pressure;
the concrete prediction method of the calculation submodule in the module comprises the following steps:
the first step is as follows: a score of 1 was determined according to the following table;
The second step is that: score 2 was determined according to the following table;
the third step: score 3 was determined according to the following table;
The fourth step: score 4 was determined according to the following table;
The fifth step: score 5 was determined according to the following table;
and a sixth step: accumulating the fraction sums from the first step to the fifth step, and searching the predicted probability of the first-onset coronary heart disease in 2 years in the table below;
The parameters required to be input by the atrial fibrillation prediction module within 10 years are as follows: age, systolic blood pressure, whether hypertension is treated, body mass index, age at which significant heart noise occurs, age at which heart failure occurs;
the concrete prediction method of the calculation submodule in the module comprises the following steps:
the first step is as follows: a score of 1 was determined according to the following table;
The second step is that: score 2 was determined according to the following table;
The third step: score 3 was determined according to the following table;
The fourth step: score 4 was determined according to the following table;
The fifth step: score 5 was determined according to the following table;
and a sixth step: a score of 6 was determined according to the following table;
the seventh step: accumulating the fraction sum of the first step to the sixth step, and searching the probability of atrial fibrillation occurring in 10 years in the following table;
The parameters required to be input by the module for predicting the serious coronary heart disease (myocardial infarction or death of coronary heart disease) within 10 years are as follows: gender, age, total cholesterol, whether to smoke, HDL, blood pressure systolic;
the concrete prediction method of the calculation submodule in the module comprises the following steps:
for males, the first step: a score of 1 was determined according to the following table;
the second step is that: score 2 was determined according to the following table;
the third step: score 3 was determined according to the following table;
The fourth step: score 4 was determined according to the following table;
The fifth step: accumulating the sum of the scores from the first step to the fourth step, and searching the probability of suffering from severe coronary heart disease (myocardial infarction or death of coronary heart disease) in the male within 10 years in the following table;
For women, the first step: a score of 1 was determined according to the following table;
the second step is that: score 2 was determined according to the following table;
the third step: score 3 was determined according to the following table;
the fourth step: score 4 was determined according to the following table;
The fifth step: accumulating the sum of the scores from the first step to the fourth step, and searching the probability of suffering from severe coronary heart disease (myocardial infarction or death of coronary heart disease) of the female within 10 years in the following table;
The parameters required to be input by the intermittent claudication probability prediction module within 4 years are as follows: age, gender, total cholesterol, blood pressure, average daily smoking number, whether or not there is diabetes, whether or not there is coronary heart disease;
the concrete prediction method of the calculation submodule in the module comprises the following steps:
the first step is as follows: a score of 1 was determined according to the following table;
The second step is that: score 2 was determined according to the following table;
The third step: score 3 was determined according to the following table;
the fourth step: score 4 was determined according to the following table;
The fifth step: score 5 was determined according to the following table;
and a sixth step: a score of 6 was determined according to the following table;
The seventh step: score 7 was determined according to the following table;
Eighth step: accumulating the sum of the scores from the first step to the seventh step, and searching the probability of intermittent claudication within 4 years in the following table;
Parameters needing to be input by a stroke probability prediction module in 5 years of atrial fibrillation patients are as follows: age, sex, systolic blood pressure, whether it is diabetic, whether it has a past stroke or a transient ischemic attack;
the concrete prediction method of the calculation submodule in the module comprises the following steps:
the first step is as follows: a score of 1 was determined according to the following table;
The second step is that: score 2 was determined according to the following table;
The third step: score 3 was determined according to the following table;
The fourth step: score 4 was determined according to the following table;
The fifth step: score 5 was determined according to the following table;
And a sixth step: accumulating the sum of the scores of the first step to the fifth step, and searching the stroke probability of the atrial fibrillation patient within 5 years in the following table;
the probability prediction module for the patients with coronary heart disease and hypertension to have heart failure within 4 years needs to input the following parameters: age, sex, systolic blood pressure, heart rate, whether left ventricular hypertrophy is present, whether coronary heart disease is present, whether diabetes is present, whether valvular disease is present;
the concrete prediction method of the calculation submodule in the module comprises the following steps:
for male patients, the first step: a score of 1 was determined according to the following table;
The second step is that: score 2 was determined according to the following table;
The third step: score 3 was determined according to the following table;
The fourth step: score 4 was determined according to the following table;
The fifth step: score 5 was determined according to the following table;
And a sixth step: a score of 6 was determined according to the following table;
The seventh step: score 7 was determined according to the following table;
Eighth step: accumulating the sum of the scores from the first step to the seventh step, and searching the probability of heart failure of male patients with coronary heart disease and hypertension within 4 years in the lower table;
for female patients, the first step: a score of 1 was determined according to the following table;
The second step is that: score 2 was determined according to the following table;
The third step: score 3 was determined according to the following table;
The fourth step: score 4 was determined according to the following table;
The fifth step: score 5 was determined according to the following table;
And a sixth step: a score of 6 was determined according to the following table;
The seventh step: score 7 was determined according to the following table;
Eighth step: the score 8 was determined according to the following table;
Eighth step: accumulating the sum of the scores from the first step to the seventh step, and searching the probability of heart failure of female patients with coronary heart disease and hypertension within 4 years in the lower table;
The probability prediction module for recurrence of coronary heart disease of patients with coronary heart disease in 2 years needs to input the following parameters: age, total cholesterol, high density lipoprotein cholesterol, whether it is diabetic, whether it is smoking, systolic blood pressure;
the concrete prediction method of the calculation submodule in the module comprises the following steps:
for male patients, the first step: a score of 1 was determined according to the following table;
The second step is that: score 2 was determined according to the following table;
the third step: score 3 was determined according to the following table;
The fourth step: accumulating the sum of the scores of the first step to the third step, and searching the probability of recurrence of coronary heart disease in 2 years for the male patient suffering from coronary heart disease in the following table;
For female patients, the first step: a score of 1 was determined according to the following table;
The second step is that: score 2 was determined according to the following table;
the third step: score 3 was determined according to the following table;
The fourth step: score 4 was determined according to the following table;
The fifth step: score 5 was determined according to the following table;
and a sixth step: and accumulating the sum of the scores of the first step to the fifth step, and searching the probability of coronary heart disease recurrence of the female patient with coronary heart disease in 2 years in the following table.
11. The big-data based cardiovascular disease assessment management system of claim 10, wherein: the prompt system includes: the system comprises a sudden risk prompting module, a prediction result prompting module, a diagnosis prompting module and a medicine taking prompting module.
12. The big-data based cardiovascular disease assessment management system of claim 11, wherein:
the burst risk prompting module is used for: when detecting that residents in data input by the daily detection system are in a high risk or emergency medical treatment state, sending information to an emergency contact telephone number reserved in the system; at the same time, informing the filing doctor to take emergency medical intervention measures;
the prediction result prompting module is used for: the prediction result obtained by the cardiovascular disease prediction system is output, and the resident is informed of the prediction result, and meanwhile, the resident can select to send the prediction result to the profiling doctor so that the profiling doctor can know the condition of the patient;
the diagnosis prompting module: the documenting doctor can remind residents of completing regular re-examination through the attendance prompting module, and in addition, residents can also manually enter and reserve expected re-diagnosis doctors and re-diagnosis time in the attendance prompting module;
the medicine taking prompt module is used for: after the patient returns home with the medicine, the patient is reminded to take the medicine according to the medicine taking method.
13. The big-data based cardiovascular disease assessment management system of claim 12, wherein: the education system comprises a regular pushing subsystem and an intelligent assistant subsystem;
the regular pushing subsystem is used for regularly pushing relevant knowledge for preventing and treating diseases according to cardiovascular diseases suffered by residents;
the intelligent assistant subsystem is used for helping to solve the problems related to life styles such as resident eating habits and daily work and rest, and after the patient inputs related questions, the background automatically identifies keywords according to prestored information of the database and replies related popular science knowledge.
14. The big-data based cardiovascular disease assessment management system of claim 1, wherein: the doctor end of community hospital and second grade hospital includes: (1) a notification system; (2) a diagnosis and treatment management system; (3) a referral and consultation system; (4) an alternating current system.
15. The big-data based cardiovascular disease assessment management system of claim 14, wherein: the notification system is to: (1) informing the doctor that a new resident selects the resident as a filing doctor; (2) receiving alarm information of the sudden risk prompting module and informing a doctor to take emergency measures conveniently;
the diagnosis and treatment management system is used for: adding a patient list and patient medical records by a doctor, editing patient medical record data, sending the patient medical record data to a patient disease diagnosis suggestion, setting patient hospitalizing reminding and medicine taking reminding, and making a medicine electronic prescription for the patient;
the referral and consultation system is used for: (1) initiating consultation and service instruction requests to doctors in the third-level hospital; (2) a referral request is sent to a doctor in a third-level hospital;
the communication system is used for: (1) a doctor in a community hospital communicates with residents; (2) the community hospital doctor, the secondary hospital doctor and the tertiary hospital doctor communicate with each other; similarly, the communication can be performed online, and the voice communication can also be performed through a reserved telephone.
16. The big-data based cardiovascular disease assessment management system of claim 15, wherein: preferably, the doctor end of the third-class hospital comprises: (1) a receiving system; (2) a diagnosis and treatment management system; (3) a guidance system; (4) an alternating current system; (5) and (5) a referral system is carried out downwards.
17. The big-data based cardiovascular disease assessment management system of claim 16, wherein: the receiving system is used for: receiving consultation and service guide requests initiated by doctors of community hospitals and secondary hospitals; receiving referral requests initiated by doctors in community hospitals and secondary hospitals; meanwhile, the stable chronic disease patient is transferred to a second-level, community and rehabilitation medical institution;
the diagnosis and treatment management system is used for: adding a patient list and patient medical records by a doctor, editing patient medical record data, sending the patient medical record data to a patient disease diagnosis suggestion, setting patient hospitalizing reminding and medicine taking reminding, and making a medicine electronic prescription for the patient;
the guidance system is for: (1) the consultation initiated by doctors in the community hospital and the secondary hospital carries out service instruction requests, and the consultation results are sent to the doctors in the community hospital and the secondary hospital; (2) after receiving a patient, the third-level hospital transfers the patient to a second-level hospital or a community hospital or gives a chronic disease management guidance suggestion according to the state of the patient; (3) the method comprises the steps of sending advanced diagnosis and treatment means progress and latest research results in related fields to doctors of community hospitals and secondary hospitals so as to facilitate the doctors of the community hospitals and the secondary hospitals to improve service capability;
the communication system is used for: (1) the doctor in the third-level hospital communicates with residents; (2) the doctors in the third-level hospital communicate with the doctors in the community hospital and the doctors in the second-level hospital; similarly, the communication can be carried out online, and the voice communication can also be carried out through a reserved telephone;
the referral-down system is used for: the stable chronic disease patient is transferred to a second-level, community and rehabilitation medical institution.
18. The big-data based cardiovascular disease assessment management system of claim 1, wherein: the management end of the health administration department comprises a data statistics module and a supervision management module, wherein the data statistics module is used for counting the conditions of the cardiovascular diseases such as morbidity, treatment, rehabilitation, graded diagnosis and treatment and the like and providing the conditions for the health administration department for reference.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110628768.3A CN113470773A (en) | 2021-06-01 | 2021-06-01 | Cardiovascular disease evaluation management system based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110628768.3A CN113470773A (en) | 2021-06-01 | 2021-06-01 | Cardiovascular disease evaluation management system based on big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113470773A true CN113470773A (en) | 2021-10-01 |
Family
ID=77872383
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110628768.3A Pending CN113470773A (en) | 2021-06-01 | 2021-06-01 | Cardiovascular disease evaluation management system based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113470773A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117292829A (en) * | 2023-10-13 | 2023-12-26 | 黄恺 | Graded diagnosis and treatment information system for coronary heart disease |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110208434A1 (en) * | 2010-02-12 | 2011-08-25 | Hersh Steven H | Method and software for cardiovascular assessment and risk detection |
| CN105335804A (en) * | 2014-08-06 | 2016-02-17 | 北京计算机技术及应用研究所 | Community health service system |
| CN105930674A (en) * | 2016-03-04 | 2016-09-07 | 江苏鹿得医疗电子股份有限公司 | Medical health data collection based intelligent health forecasting and decision-making system |
| CN107025375A (en) * | 2017-03-14 | 2017-08-08 | 芜湖卫健康物联网医疗科技有限公司 | A kind of cloud classification diagnosis and treatment data handling system and its data processing method |
| CN107169270A (en) * | 2017-04-24 | 2017-09-15 | 江苏省苏北人民医院 | A kind of chronic cardiovascular diseases remote hierarchical follow-up accurate management system |
-
2021
- 2021-06-01 CN CN202110628768.3A patent/CN113470773A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110208434A1 (en) * | 2010-02-12 | 2011-08-25 | Hersh Steven H | Method and software for cardiovascular assessment and risk detection |
| CN105335804A (en) * | 2014-08-06 | 2016-02-17 | 北京计算机技术及应用研究所 | Community health service system |
| CN105930674A (en) * | 2016-03-04 | 2016-09-07 | 江苏鹿得医疗电子股份有限公司 | Medical health data collection based intelligent health forecasting and decision-making system |
| CN107025375A (en) * | 2017-03-14 | 2017-08-08 | 芜湖卫健康物联网医疗科技有限公司 | A kind of cloud classification diagnosis and treatment data handling system and its data processing method |
| CN107169270A (en) * | 2017-04-24 | 2017-09-15 | 江苏省苏北人民医院 | A kind of chronic cardiovascular diseases remote hierarchical follow-up accurate management system |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117292829A (en) * | 2023-10-13 | 2023-12-26 | 黄恺 | Graded diagnosis and treatment information system for coronary heart disease |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Celler et al. | Using information technology to improve the management of chronic disease | |
| Carey et al. | Prevalence and correlates of sexual activity and HIV-related risk behavior among psychiatric outpatients. | |
| CN105956371A (en) | Remote video interrogation system | |
| Panda et al. | Big data in health care: A mobile based solution | |
| US20130131574A1 (en) | Dialysis treatment monitoring | |
| WO2014025503A1 (en) | System and method of health care delivery | |
| CN104376409A (en) | Triage data processing method and system based on network hospital | |
| Rice | Economic Costs of Drug Abuse Dorothy P. Rice, Sander Kelman, and Leonard S. Miller | |
| Ferdows et al. | Assessment of racial disparities in mortality rates among older adults living in US rural vs urban counties from 1968 to 2016 | |
| CN109887566B (en) | Intelligent management method and system for electronic health files | |
| Squires et al. | The financial impact of a nurse-led telemedicine service for inflammatory bowel disease in a large district general hospital | |
| CN106919804A (en) | Medicine based on clinical data recommends method, recommendation apparatus and server | |
| Cipolletta et al. | Illness trajectories in patients suffering from leg ulcers: A qualitative study | |
| CN108597588A (en) | The full life course health collection of illustrative plates creation method of full crowd, system and storage medium | |
| CN113470773A (en) | Cardiovascular disease evaluation management system based on big data | |
| CN105847331A (en) | Chronic disease recovery remote communication support system based on cyber physical system (CPS) | |
| CN105765571A (en) | Method and arrangement for finding second opinion for disease using modern database, search and matching technology integrated with social media | |
| JP2009031900A (en) | Medical checkup data processor | |
| CN107066816B (en) | Medical treatment guidance method and device based on clinical data and server | |
| Simning et al. | Patients discharged from higher‐quality skilled nursing facilities spend more days at home | |
| CN113360847B (en) | Cardiovascular disease prediction system and cardiovascular disease management system comprising same | |
| Rivera-Hernandez et al. | Changes in migration and mortality among patients with kidney failure in Puerto Rico after Hurricane Maria | |
| CN115410688A (en) | Memorial-based insomnia cognitive behavior treatment scheme generation method and device | |
| CN105975779A (en) | Blood sugar management method based on Internet of Things | |
| CN116434899A (en) | Health record information platform establishment method based on multi-source data |
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 |