WO2023207796A1 - Evidence-based practice and decision-making assistance method for intervention measures around whole person health - Google Patents
Evidence-based practice and decision-making assistance method for intervention measures around whole person health Download PDFInfo
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
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- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
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Definitions
- the invention relates to evidence-based intervention measures and decision-making assistance methods focusing on whole-person health, and belongs to the technical fields of big data and medical and health resource allocation.
- the medical treatment record submission system is launched, the medical treatment information submitted by the client is checked for legality, the efficacy coefficient is calculated, and past medical treatment records are generated, including pre-medical condition information or post-medical condition information, and adverse reactions Symptom indicators, visiting doctors, adverse reaction coefficients, and efficacy coefficients; and are stored in the medical treatment record database; (3) The patient inputs information related to the current condition and requests recommended doctor services through the client before medical treatment, and submits the request to the backend system; (4) After the backend system receives a request to recommend doctor services, the similar record retrieval system retrieves the previous medical treatment information in the medical treatment record database, calculates the condition parameters and condition similarity, and finds out the medical treatment in the database that is similar to the patient's current condition.
- the intervention measure database includes a plurality of different types of intervention measure sub-databases, and each intervention measure sub-database collects several corresponding types of intervention measures and characteristic information of each of the intervention measures;
- the intervention path includes several nodes. Each node contains characteristic information required for the required intervention measures. Optional options are determined based on the user's individual health data. Node, combine optional nodes to form at least one intervention plan;
- Intervention plans are provided to the user.
- the user selects the final intervention plan; when the intervention plan is determined, based on the characteristic information of the intervention measures at each node in the intervention plan and the user's individual characteristic data, Match interventions suitable for users in the intervention database and provide them to users.
- the Western medicine intervention database includes several intervention measures in the field of Western medicine
- the complementary and alternative therapy intervention database includes several interventions outside the fields of traditional Chinese medicine and Western medicine.
- Establish an evidence-based model based on neural networks obtain individual health data of people suffering from various diseases as training samples, conduct training through neural networks, and generate evidence-based models that predict the diseases and disease states suffered by users.
- the disease states indicate severity of illness;
- the intervention path corresponding to the disease in this state is queried in the intervention path classification table.
- the specific steps of determining optional nodes according to the user's individual health data and composing the optional nodes into at least one intervention program are:
- a scoring value range is set corresponding to each decision-making layer, and a preferred scoring value is set corresponding to each node;
- Starting from the starting node it continues to advance through the nodes connected in the upper layer until it is connected to the node in the uppermost decision-making layer, forming at least one intervention plan.
- the correction and confirmation of the intervention plan is carried out cyclically by preventive medicine doctors and clinical medicine doctors alternately or in combination.
- corresponding prognosis predictions and preferred intervention implementation suggestions for the intervention plan are also generated to provide users with selected interventions.
- the basis of the plan is that clinical doctors include traditional Chinese medicine doctors, general practitioners, and specialists.
- the user after implementing an intervention measure, the user evaluates the effect of the intervention measure, and stores the evaluation results as the user's individual characteristics for real-time training and matching of intervention measures.
- the invention is an evidence-based and decision-making assistance method for intervention measures that focuses on the health of the whole person. It establishes an intervention measure database through real-world data for big data evidence-based analysis, and becomes a common guideline for selecting intervention measures such as Chinese and Western medicine, traditional and complementary medicine, etc., so that organic combination. Taking individual users as a whole system, many intervention paths and intervention measures are gradually stratified and refined, and suitable and effective intervention measures are selected to match and combine with users to form the prevention, monitoring and control of users' overall health problems. Diagnose and provide intervention plans to assist patients and doctors in medical and health institutions in making decisions.
- the intervention measure database includes a plurality of different types of intervention measure sub-databases, and each intervention measure sub-database collects several corresponding types of intervention measures and characteristic information of each of the intervention measures;
- the intervention path includes several nodes. Each node contains the type of intervention measures required and the characteristic information that the intervention measures should have. According to the user Determine optional nodes based on individual health data, and combine the optional nodes into at least one intervention plan;
- this embodiment does not bundle other paid services and products, does not specify or privately imply the brand, trade name, manufacturer and other product information of the intervention measure, and only provides a common name. Realize the separation of decision-making aids and intervention measures, separation of diagnosis and treatment, and block the factors that induce demand to ensure service quality and reflect the objective needs and value preferences of users.
- This embodiment uses a real-world intervention database to conduct big data evidence-based analysis, as a common guideline for selecting Western medicine, traditional and complementary medicine, and other interventions. Taking the individual user as a whole system, many intervention paths and intervention measures are gradually stratified and refined, and suitable and effective intervention measures are selected to match the user to form the prevention, monitoring, diagnosis, and management of the user's overall health problems. Provide intervention plans to assist patients and doctors in medical and health institutions in making decisions.
- the Chinese medicine intervention database includes several intervention measures in the field of Chinese medicine, such as Chinese patent medicines, Chinese medicine pieces and preparations, health teas, medicated diets, spices, massage, acupuncture, moxibustion, cupping, acupoint massage, Qigong, etc.;
- the Western medicine intervention database includes several intervention measures in the field of Western medicine, such as over-the-counter drugs, prescription drugs, injections, surgeries, high-level care, etc.;
- the intervention path includes a daily health care intervention path, a self-intervention path (for minor and chronic diseases suitable for medication, diet therapy, physiotherapy, etc.), and a medical and health system intervention path (for injections, Diseases with therapeutic intervention such as surgery, hospitalization, etc.); nodes in the daily health care intervention path such as traditional Chinese medicine, lifestyle behavior adjustment, sports and fitness, environmental occupation, etc.; nodes in the self-intervention path such as drug intervention, complementary and alternative therapies and non-drug interventions;
- the drug intervention refers to drugs or devices that can be intervened by oneself without the help of professionals, including traditional Chinese medicine, Western medicine, and ethnic medicine.
- the disease states indicate The severity of the disease can be set as mild (suitable for medication, diet, physiotherapy, natural therapy, etc.), moderate (requiring injections, minimally invasive care, etc.), severe (requiring surgery, high-level care, etc.); cycle
- the results predicted by the syndrome model are, for example: disease: pancreatitis, disease status: severe;
- the influenza with the level status of moderate disease is divided into the medical and health system intervention path.
- the heart disease with the level status of mild disease is divided into the daily health care intervention path and the self-intervention path at the same time, and the level status is classified into the self-intervention path.
- Moderate and severe heart diseases are simultaneously divided into daily health care intervention paths, self-intervention paths and medical and health system intervention paths;
- the specific steps of determining optional nodes based on the user's individual health data and composing the optional nodes into at least one intervention plan are:
- a corresponding decision tree is pre-constructed for each intervention path.
- the decision tree includes n layers of decision-making layers from bottom to top (layers N1 ⁇ N4 in the figure).
- Each decision-making layer contains Several nodes (node a1, node b1, etc. in the figure), and each lower node is connected to at least one upper node, and each node is configured with the required intervention measures;
- the N1 layer is set to 80 ⁇ 95 points
- the N2 layer is set to 60 ⁇ 79 points
- the N3 layer is set to 30 ⁇ 59 points
- the N4 layer is set to 0 ⁇ 29 points
- the preferred score value corresponding to each node for example, the preferred score value range of d1 is 0 ⁇ 7, the preferred score value range of d2 is 8 ⁇ 10, etc.
- the starting node Starting from the starting node, it continues to progress through the nodes connected to the upper layer until it is connected to the node in the uppermost decision-making layer to form at least one intervention plan; for example, starting from the N3 layer, the starting node is determined to be c3 , then through upward progression, the intervention plans formed are c3 ⁇ b1 ⁇ a1 and c3 ⁇ b2 ⁇ a1.
- the specific steps of matching intervention measures suitable for the user in the intervention measures database according to the characteristic information required for the intervention measures at each node in the intervention plan and the user's individual characteristic data are:
- the characteristic information of the intervention can be refined, for example, to medicines
- the user's individual characteristic data includes the user's life data, physiological sign data, permanent address and environmental data, medical history and medication data, health and medical consumption data, and user preferences.
- the user after the user implements an intervention measure, he or she evaluates the implementation effect of the intervention measure, and stores the evaluation results as the user's individual characteristics for real-time training and matching of the intervention measures.
- interventions we provide a basis for individuals to choose the best intervention based on health data.
- the target evaluation subjects are people and groups of people, that is, obtaining evidence-based evidence such as effectiveness and suitability or relevant causal inferences from many intervention measures, focusing on the effect outcome (efficacy) and factors affecting differences in population situations, which is also more conducive to clinical evaluation. preventive interventions (especially traditional Chinese medicine).
- this embodiment establishes a continuous relationship between people and electronic information decision-making assistance systems and regional population health information systems, and then extends it to natural systems (monitoring intervention equipment) and social service systems, making individual and regional group health and intervention measures , policies form a closed relationship, which is conducive to individual intervention, regional population intervention, integration and coordination of policy measures, and multi-party joint decision-making to maximize individual-based group benefits.
- it proposes intervention paths and specific measures for users based on health data, classifies the intervention paths, and further provides optimized matching intervention option information and prognosis predictions to form an intervention plan to provide implementation suggestions for users. For non-individualized Intervention matching evaluation information or research results are shared through the decision-making assistance system network platform.
Abstract
The present invention relates to an evidence-based practice and decision-making assistance method for intervention measures around whole person health, comprising the following steps: establishing an intervention measure database, wherein the intervention measure database comprises different types of intervention measure sub-databases; obtaining individual feature data and individual health data of a user, and determining an intervention path of the user by means of the individual health data; refining the intervention path and forming an intervention scheme, wherein the intervention path comprises a plurality of nodes, determining optional nodes according to the individual health data of the user, and forming at least one intervention scheme by means of the optional nodes; providing the intervention scheme to the user, and when there are multiple intervention schemes, selecting a final intervention scheme by the user; and after the intervention scheme is determined, matching an intervention measure suitable for the user from the intervention measure database according to feature information required by an intervention measure of each node in the intervention scheme and according to the individual feature data of the user, and providing the intervention measure to the user.
Description
本发明涉及围绕全人健康的干预措施循证与决策辅助方法，属于大数据、医疗卫生资源分配技术领域。The invention relates to evidence-based intervention measures and decision-making assistance methods focusing on whole-person health, and belongs to the technical fields of big data and medical and health resource allocation.
在目前的医学服务系统中，干预措施与医疗保健提供者之间利益冲突是医学伦理的凸出问题，不同程度抑制低成本干预的竞争力与可及性。由于医疗信息不对称，用户几乎没有干预实施的决策权，迄今这种家长式医疗模式已经持续数千年。用户通常根据医生诊断后给出的解决方案选择医疗干预措施，而医生给出的药方和医疗方案具有强烈的主观性，医疗干预措施不当匹配导致大医院的拥挤和过度诊疗、过度健康消费，社会总体健康医疗费用高。医疗干预占据主导地位，预防干预薄弱，两者不连续处于两极隔裂状态。并且由于中西医及补充和替代干预措施不同的医学理论体系，用户难以正确选择并合理利用干预资源，需要借助干预措施数据库（真实世界数据、健康医疗大数据）获取实际产生的有效性、适合性等的最佳证据，提供用户、医疗系统医生干预决策之用。In the current medical service system, the conflict of interest between interventions and health care providers is a prominent issue in medical ethics, which inhibits the competitiveness and accessibility of low-cost interventions to varying degrees. Due to the asymmetry of medical information, users have almost no decision-making power to intervene in implementation. This paternalistic medical model has lasted for thousands of years. Users usually choose medical interventions based on the solutions given by doctors after diagnosis. However, the prescriptions and medical plans given by doctors are highly subjective. Improper matching of medical intervention measures leads to overcrowding in large hospitals, overdiagnosis and treatment, and excessive health consumption. Society Overall health care costs are high. Medical intervention dominates, preventive intervention is weak, and the two are discontinuous and separated. Moreover, due to the different medical theoretical systems of Chinese and Western medicine and complementary and alternative interventions, it is difficult for users to correctly select and rationally utilize intervention resources. They need to use intervention databases (real-world data, health and medical big data) to obtain actual effectiveness and suitability. Provide the best evidence for users and medical system doctors to intervene in decision-making.
公开号为“CN103559637A”的发明专利公开了一种为就诊患者推荐医生的方法，包括如下步骤：（1）患者通过客户端提交就诊信息，包括就诊前病情信息或者就诊后病情信息、不良反应症状指标、就诊医生；（2）就诊记录提交系统启动，对客户端提交的就诊信息进行就诊信息合法性检查，疗效系数计算，生成以往就诊记录，包括就诊前病情信息或者就诊后病情信息、不良反应症状指标、就诊医生、不良反应系数、疗效系数；并存储在就诊记录数据库；（3）患者通过客户端在就诊前输入当前病情相关信息及请求推荐医生服务，并将该请求提交到后台系统；（4）后台系统收到推荐医生服务的请求后，由相似记录检索系统检索就诊记录数据库中以往就诊信息，进行病情参量计算、病情相似度计算，找出数据库中与该患者当前病情相似的就诊记录，并将检索结果暂存起来供医生评估系统使用；（5）医生评估系统在检索结果中统计每个医生对这类患者进行诊治的有效率和不良率，包括诊治有效率最高的几位医生，并将这些信息生成医生有效率及不良反应率列表传输到客户端；（6）客户端向患者推荐诊治有效率最高的几位医生，并且如果患者选择的医生的诊治不良率较高，则提醒患者选择其他医生就诊。上述现有技术公开了如何根据患者的诊疗数据合理的推荐医生，但是还是没有解决医疗卫生资源匹配的问题，如何以患者为中心，提高患者在医疗干预措施选择方面的参与性，是目前急需解决的问题。The invention patent with the publication number "CN103559637A" discloses a method of recommending doctors for patients, which includes the following steps: (1) The patient submits medical information through the client, including pre-medical condition information or post-medical condition information, and adverse reaction symptoms. Indicators, visiting doctors; (2) The medical treatment record submission system is launched, the medical treatment information submitted by the client is checked for legality, the efficacy coefficient is calculated, and past medical treatment records are generated, including pre-medical condition information or post-medical condition information, and adverse reactions Symptom indicators, visiting doctors, adverse reaction coefficients, and efficacy coefficients; and are stored in the medical treatment record database; (3) The patient inputs information related to the current condition and requests recommended doctor services through the client before medical treatment, and submits the request to the backend system; (4) After the backend system receives a request to recommend doctor services, the similar record retrieval system retrieves the previous medical treatment information in the medical treatment record database, calculates the condition parameters and condition similarity, and finds out the medical treatment in the database that is similar to the patient's current condition. Record and temporarily store the search results for use by the doctor evaluation system; (5) The doctor evaluation system counts the effectiveness and failure rates of each doctor in the diagnosis and treatment of this type of patients in the search results, including the ones with the highest diagnosis and treatment efficiency Doctors, and generate a list of doctors' effectiveness and adverse reaction rates from this information and transmit it to the client; (6) The client recommends the doctors with the highest diagnosis and treatment efficiency to the patient, and if the doctor selected by the patient has a higher rate of adverse reactions, The patient is reminded to choose another doctor for treatment. The above-mentioned prior art discloses how to reasonably recommend doctors based on the patient's diagnosis and treatment data, but it still does not solve the problem of matching medical and health resources. How to put the patient as the center and improve the patient's participation in the selection of medical intervention measures is an urgent problem at present. The problem.
为了解决上述现有技术中存在的问题，本发明提出了围绕全人健康的干预措施循证与决策辅助方法，建立干预措施数据库，根据用户的健康数据确定干预路径，并以用户个体作为一个整体的系统，进行诸多干预路径和干预措施渐变分层、细化，选择适合性、有效性强的干预措施与用户进行匹配，形成用户整体健康问题的预防、监测、诊断、提供干预方案辅助用户及医疗卫生系统医生进行决策。In order to solve the problems existing in the above-mentioned prior art, the present invention proposes an evidence-based and decision-making assistance method for intervention measures focusing on the health of the whole person, establishes an intervention measure database, determines the intervention path according to the user's health data, and takes the individual user as a whole The system gradually stratifies and refines many intervention paths and intervention measures, selects suitable and effective intervention measures to match users, forms the prevention, monitoring, and diagnosis of users' overall health problems, and provides intervention plans to assist users and Healthcare system physicians make decisions.
本发明的技术方案如下：The technical solution of the present invention is as follows:
一方面，本发明提供一种围绕全人健康的干预措施循证与决策辅助方法，包括以下步骤：On the one hand, the present invention provides an evidence-based and decision-making assistance method for intervention measures focusing on whole-person health, which includes the following steps:
建立干预措施数据库，所述干预措施数据库中包括多种不同类型的干预措施子数据库，各所述干预措施子数据库中收集有若干对应类型的干预措施以及各所述干预措施的特征信息；Establishing an intervention measure database, the intervention measure database includes a plurality of different types of intervention measure sub-databases, and each intervention measure sub-database collects several corresponding types of intervention measures and characteristic information of each of the intervention measures;
获取用户的个体特征数据以及个体健康数据，通过个体健康数据确定该用户的干预路径；Obtain the user's individual characteristic data and individual health data, and determine the user's intervention path through the individual health data;
将用户个体作为整体系统，细化干预路径并形成干预方案，所述干预路径包括若干节点，每一节点包含有所需的干预措施所要具备的特征信息，根据用户的个体健康数据确定可选的节点，将可选的节点组成至少一种干预方案；Treat the individual user as an overall system, refine the intervention path and form an intervention plan. The intervention path includes several nodes. Each node contains characteristic information required for the required intervention measures. Optional options are determined based on the user's individual health data. Node, combine optional nodes to form at least one intervention plan;
提供干预方案给用户，当存在多种干预方案时，由用户选择最终干预方案；当干预方案确定后，根据干预方案中的每一节点的干预措施所要具备的特征信息以及用户的个体特征数据，到干预措施数据库中匹配适合用户的干预措施，并提供给用户。Intervention plans are provided to the user. When there are multiple intervention plans, the user selects the final intervention plan; when the intervention plan is determined, based on the characteristic information of the intervention measures at each node in the intervention plan and the user's individual characteristic data, Match interventions suitable for users in the intervention database and provide them to users.
作为优选实施方式，所述干预措施子数据库包括中医药干预数据库、西医药干预数据库、补充和替代疗法数据库，其中，西医指的是现代医学；As a preferred embodiment, the intervention sub-database includes a Chinese medicine intervention database, a Western medicine intervention database, and a complementary and alternative therapy database, where Western medicine refers to modern medicine;
所述中医药干预数据库中包括中医药领域中的若干项干预措施；The traditional Chinese medicine intervention database includes several intervention measures in the field of traditional Chinese medicine;
所述西医药干预数据库中包括西医药领域中的若干项干预措施；The Western medicine intervention database includes several intervention measures in the field of Western medicine;
所述补充和替代疗法干预数据库中包括在中医药领域和西医药领域之外的若干项干预措施。The complementary and alternative therapy intervention database includes several interventions outside the fields of traditional Chinese medicine and Western medicine.
作为优选实施方式，所述干预路径包括日常保健干预路径、自我干预路径、医疗卫生系统干预路径；As a preferred embodiment, the intervention path includes a daily health care intervention path, a self-intervention path, and a medical and health system intervention path;
所述通过个体健康数据确定该用户的干预路径的步骤具体为：The steps of determining the user's intervention path through individual health data are specifically:
建立基于神经网络的循证模型，获取各种疾病的患病人群的个体健康数据作为训练样本，通过神经网络进行训练，生成预测用户罹患的疾病以及疾病状态的循证模型，所述疾病状态表明患病的严重程度；Establish an evidence-based model based on neural networks, obtain individual health data of people suffering from various diseases as training samples, conduct training through neural networks, and generate evidence-based models that predict the diseases and disease states suffered by users. The disease states indicate severity of illness;
建立干预路径分类表，根据对应疾病的类型以及疾病状态分别将各状态的疾病划分至日常保健干预路径、自我干预路径或医疗卫生系统干预路径中；Establish an intervention path classification table, and classify the diseases in each state into daily health care intervention paths, self-intervention paths or medical and health system intervention paths according to the corresponding disease types and disease states;
将用户的个体健康数据输入循证模型，循证模型预测当前用户罹患的疾病以及疾病状态；Input the user's individual health data into the evidence-based model, and the evidence-based model predicts the current disease and disease status of the user;
根据预测得到的当前用户罹患的疾病以及疾病状态在所述干预路径分类表中查询该状态的疾病对应的干预路径。According to the predicted disease and disease state suffered by the current user, the intervention path corresponding to the disease in this state is queried in the intervention path classification table.
作为优选实施方式，所述根据用户的个体健康数据确定可选的节点，将可选的节点组成至少一种干预方案的具体步骤为：As a preferred embodiment, the specific steps of determining optional nodes according to the user's individual health data and composing the optional nodes into at least one intervention program are:
对应每一种干预路径预先构建对应的决策树，所述决策树中包括从下至上的n层决策层，每一决策层中包含若干节点，且每一下层节点至少与一上层节点连接，每一节点均配置有所需的干预措施；A corresponding decision tree is pre-constructed for each intervention path. The decision tree includes n layers of decision-making layers from bottom to top. Each decision-making layer contains several nodes, and each lower-layer node is connected to at least one upper-layer node. Each lower-layer node is connected to at least one upper-layer node. Each node is configured with the required interventions;
对应每一决策层设置一评分值范围，且对应每一节点设置优选评分值；A scoring value range is set corresponding to each decision-making layer, and a preferred scoring value is set corresponding to each node;
根据所述用户的个体健康数据对该用户进行健康评分，获取该用户的健康评分值；判断该用户的健康评分值属于哪一层决策层的评分值范围，并以该决策层作为起始层，从所述起始层中选取优选评分值最接近用户的健康评分值的节点作为起始节点；Perform a health score on the user based on the user's individual health data to obtain the user's health score; determine which decision-making layer's score range the user's health score belongs to, and use this decision-making layer as the starting layer , select the node whose preferred score value is closest to the user's health score value from the starting layer as the starting node;
以所述起始节点开始，不断通过上一层连接的节点进行递进，直至连接至最上层决策层中的节点，形成至少一种干预方案。Starting from the starting node, it continues to advance through the nodes connected in the upper layer until it is connected to the node in the uppermost decision-making layer, forming at least one intervention plan.
作为优选实施方式，所述根据所述用户的个体健康数据对该用户进行健康评分，获取该用户的健康评分值的方法具体为：As a preferred embodiment, the user is given a health score based on the user's individual health data, and the method for obtaining the user's health score is specifically:
收集若干患病或健康人群的个体健康数据作为样本，分别对各样本进行人为评分，作为标签值；将所有样本划分为样本集、验证集和测试集；Collect individual health data of several sick or healthy people as samples, manually score each sample as a label value; divide all samples into sample sets, verification sets and test sets;
将样本集输入至神经网络进行迭代训练，得到健康评分模型；通过验证集调整健康评分模型中的特征参数；通过测试集对健康评分模型的评分准确率进行测试；得到训练好的健康评分模型；Input the sample set into the neural network for iterative training to obtain a health score model; adjust the characteristic parameters in the health score model through the verification set; test the scoring accuracy of the health score model through the test set; obtain the trained health score model;
将用户的个体健康数据输入至训练好的健康评分模型中，得出该用户的健康评分值。Input the user's individual health data into the trained health score model to obtain the user's health score value.
作为优选实施方式，所述根据干预方案中的每一节点的干预措施所要具备的特征信息以及用户的个体特征数据，到干预措施数据库中匹配适合用户的干预措施的具体步骤为：As a preferred embodiment, the specific steps of matching intervention measures suitable for the user in the intervention measure database according to the characteristic information required by the intervention measures at each node in the intervention plan and the user's individual characteristic data are:
获取每一所需的干预措施及该干预措施具备的特征信息，到干预措施数据库中搜寻具有对应特征信息的干预措施作为候选资源；Obtain each required intervention and the characteristic information of the intervention, and search for intervention measures with corresponding characteristic information in the intervention database as candidate resources;
获取用户的个体特征，根据用户的个体特征与候选资源的特征信息的匹配度，筛选出适合用户的推荐干预措施。Obtain the user's individual characteristics, and select recommended interventions suitable for the user based on the matching degree between the user's individual characteristics and the characteristic information of the candidate resources.
作为优选实施方式，在将干预方案发送给用户之前，根据干预路径的类型匹配相对应的医生，由医生进行干预方案的补正和确认。As a preferred embodiment, before sending the intervention plan to the user, a corresponding doctor is matched according to the type of intervention path, and the doctor corrects and confirms the intervention plan.
作为优选实施方式，所述干预方案的补正和确认由预防医学医生、临床医学医生交替或组合的方式循环进行，同时还生成干预方案相应的预后预测及优选的干预实施建议事项提供用户作为选择干预方案的依据，其中临床医学医生包括中医医生、全科医生、专科医生。As a preferred embodiment, the correction and confirmation of the intervention plan is carried out cyclically by preventive medicine doctors and clinical medicine doctors alternately or in combination. At the same time, corresponding prognosis predictions and preferred intervention implementation suggestions for the intervention plan are also generated to provide users with selected interventions. The basis of the plan is that clinical doctors include traditional Chinese medicine doctors, general practitioners, and specialists.
作为优选实施方式，用户在实施一项干预措施后，对干预措施效果进行评价，将评价结果作为用户的个体特征进行存储，用于干预措施的实时训练和匹配。As a preferred embodiment, after implementing an intervention measure, the user evaluates the effect of the intervention measure, and stores the evaluation results as the user's individual characteristics for real-time training and matching of intervention measures.
另一方面，本发明还提供一种电子设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时实现如本发明任一实施例所述的围绕全人健康的干预措施循证与决策辅助方法。On the other hand, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, any implementation of the present invention is implemented. Evidence-based and decision-making aids for interventions focusing on whole-person health as described in the example.
本发明具有如下有益效果：The invention has the following beneficial effects:
本发明一种围绕全人健康的干预措施循证与决策辅助方法，根据用户的健康数据确定干预路径，并基于干预路径匹配干预措施，实现决策辅助与干预措施分离、诊断与治疗分离，为用户提供客观、有效的干预方案及决策建议。The present invention is an evidence-based and decision-making assistance method for intervention measures focusing on the health of the whole person. It determines the intervention path according to the user's health data, and matches the intervention measures based on the intervention path, thereby realizing the separation of decision-making assistance and intervention measures, and the separation of diagnosis and treatment, providing users with Provide objective and effective intervention plans and decision-making suggestions.
本发明一种围绕全人健康的干预措施循证与决策辅助方法，通过真实世界的数据建立干预措施数据库进行大数据循证，成为选择中西医学、传统和补充医学等实施干预的共同遵循，使之有机结合。以用户个体作为一个整体的系统，进行诸多干预路径、干预措施渐变分层及细化，选择适合性、有效性显著的干预措施与用户进行匹配、组合，形成用户整体健康问题的预防、监测、诊断、提供干预方案辅助患者及医疗卫生机构医生进行决策。The invention is an evidence-based and decision-making assistance method for intervention measures that focuses on the health of the whole person. It establishes an intervention measure database through real-world data for big data evidence-based analysis, and becomes a common guideline for selecting intervention measures such as Chinese and Western medicine, traditional and complementary medicine, etc., so that organic combination. Taking individual users as a whole system, many intervention paths and intervention measures are gradually stratified and refined, and suitable and effective intervention measures are selected to match and combine with users to form the prevention, monitoring and control of users' overall health problems. Diagnose and provide intervention plans to assist patients and doctors in medical and health institutions in making decisions.
本发明一种围绕全人健康的干预措施循证与决策辅助方法，将用户对干预措施的实施效果评价作为用户的个体特征进行存储，在健康数据的基础上为个体选择匹配最佳干预措施提供依据。The invention is an evidence-based and decision-making assistance method for intervention measures focusing on whole-person health. The user's evaluation of the implementation effect of the intervention measures is stored as the user's individual characteristics, and the best intervention measures are selected and matched for the individual based on the health data. in accordance with.
图1为本发明实施例的流程图；Figure 1 is a flow chart of an embodiment of the present invention;
图2为本发明实施例中决策树的示例图。Figure 2 is an example diagram of a decision tree in an embodiment of the present invention.
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts fall within the scope of protection of the present invention.
应当理解，文中所使用的步骤编号仅是为了方便描述，不对作为对步骤执行先后顺序的限定。It should be understood that the step numbers used in the text are only for convenience of description and are not intended to limit the execution order of the steps.
应当理解，在本发明说明书中所使用的术语仅仅是出于描述特定实施例的目的而并不意在限制本发明。如在本发明说明书和所附权利要求书中所使用的那样，除非上下文清楚地指明其它情况，否则单数形式的“一”、“一个”及“该”意在包括复数形式。It should be understood that the terminology used in the description of the present invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly dictates otherwise.
术语“包括”和“包含”指示所描述特征、整体、步骤、操作、元素和/或组件的存在，但并不排除一个或多个其它特征、整体、步骤、操作、元素、组件和/或其集合的存在或添加。The terms "comprises" and "comprising" indicate the presence of described features, integers, steps, operations, elements and/or components but do not exclude the presence of one or more other features, integers, steps, operations, elements, components and/or The existence or addition to its collection.
术语“和/或”是指相关联列出的项中的一个或多个的任何组合以及所有可能组合，并且包括这些组合。The term "and/or" refers to any and all possible combinations of one or more of the associated listed items and includes such combinations.
实施例一：Example 1:
参见图1，一种围绕全人健康的干预措施循证与决策辅助方法，包括以下步骤：Referring to Figure 1, an evidence-based and decision-making aid approach to interventions focusing on whole-person health includes the following steps:
建立干预措施数据库，所述干预措施数据库中包括多种不同类型的干预措施子数据库，各所述干预措施子数据库中收集有若干对应类型的干预措施以及各所述干预措施的特征信息；Establishing an intervention measure database, the intervention measure database includes a plurality of different types of intervention measure sub-databases, and each intervention measure sub-database collects several corresponding types of intervention measures and characteristic information of each of the intervention measures;
获取用户的个体特征数据以及个体健康数据，通过个体健康数据确定该用户的干预路径；Obtain the user's individual characteristic data and individual health data, and determine the user's intervention path through the individual health data;
将用户个体作为一个整体的系统，进行细化干预路径并形成干预方案，所述干预路径包括若干节点，每一节点包含有所需的干预措施的类型以及干预措施所要具备的特征信息，根据用户的个体健康数据确定可选的节点，将可选的节点组成至少一种干预方案；Treat individual users as a whole system to refine the intervention path and form an intervention plan. The intervention path includes several nodes. Each node contains the type of intervention measures required and the characteristic information that the intervention measures should have. According to the user Determine optional nodes based on individual health data, and combine the optional nodes into at least one intervention plan;
提供干预方案给用户，当存在多种干预方案时，由用户选择最终干预方案；当干预方案确定后，根据干预方案中的每一节点的干预措施所要具备的特征信息以及用户的个体特征数据，到干预措施数据库中匹配适合用户的干预措施，并提供给用户。Intervention plans are provided to the user. When there are multiple intervention plans, the user selects the final intervention plan; when the intervention plan is determined, based on the characteristic information of the intervention measures at each node in the intervention plan and the user's individual characteristic data, Match interventions suitable for users in the intervention database and provide them to users.
本实施例在提供干预方案时，并不捆绑其他有偿服务和产品，不指定或私下暗示干预措施的品牌、商号、厂家等产品信息，只提供通用名称。实现决策辅助与干预措施分离、诊断与治疗分离，阻隔诱导需求因素以确保服务品质，体现用户的客观需求和价值偏好。When providing an intervention plan, this embodiment does not bundle other paid services and products, does not specify or privately imply the brand, trade name, manufacturer and other product information of the intervention measure, and only provides a common name. Realize the separation of decision-making aids and intervention measures, separation of diagnosis and treatment, and block the factors that induce demand to ensure service quality and reflect the objective needs and value preferences of users.
本实施例通过真实世界的干预措施数据库进行大数据循证，作为选择西医学、传统和补充医学等实施干预的共同遵循。以用户个体作为一个整体的系统，进行诸多干预路径、干预措施渐变分层及细化，选择适合性、有效性显著的干预措施与用户进行匹配，形成用户整体健康问题的预防、监测、诊断、提供干预方案辅助患者及医疗卫生机构医生进行决策。This embodiment uses a real-world intervention database to conduct big data evidence-based analysis, as a common guideline for selecting Western medicine, traditional and complementary medicine, and other interventions. Taking the individual user as a whole system, many intervention paths and intervention measures are gradually stratified and refined, and suitable and effective intervention measures are selected to match the user to form the prevention, monitoring, diagnosis, and management of the user's overall health problems. Provide intervention plans to assist patients and doctors in medical and health institutions in making decisions.
作为本实施例的优选实施方式，所述干预措施子数据库包括中医药干预数据库、西医药干预数据库、补充和替代疗法数据库；As a preferred implementation of this embodiment, the intervention sub-database includes a Chinese medicine intervention database, a Western medicine intervention database, and a complementary and alternative therapy database;
所述中医药干预数据库中包括中医药领域中的若干项干预措施，例如中成药、中药饮片及制剂、保健茶饮、药膳、香料、推拿、针灸、艾灸、火罐、穴位按摩、气功等；The Chinese medicine intervention database includes several intervention measures in the field of Chinese medicine, such as Chinese patent medicines, Chinese medicine pieces and preparations, health teas, medicated diets, spices, massage, acupuncture, moxibustion, cupping, acupoint massage, Qigong, etc.;
所述西医药干预数据库中包括西医药领域中的若干项干预措施，例如非处方药、处方药、注射、手术、高级别护理等；The Western medicine intervention database includes several intervention measures in the field of Western medicine, such as over-the-counter drugs, prescription drugs, injections, surgeries, high-level care, etc.;
所述补充和替代疗法干预数据库中包括在中医药领域和西医药领域之外的若干项干预措施，例如健康教育、精神-心理疗法、音乐疗法、保健食品、维生素疗法、温泉疗法等。The complementary and alternative therapy intervention database includes several interventions outside the fields of traditional Chinese medicine and Western medicine, such as health education, psycho-psychotherapy, music therapy, health foods, vitamin therapy, spa therapy, etc.
作为本实施例的优选实施方式，所述干预路径包括日常保健干预路径、自我干预路径（针对适合药疗、食疗、理疗等干预的小病慢病）、医疗卫生系统干预路径（针对需要注射、手术、住院等治疗干预的疾病）；日常保健干预路径中的节点例如中医养生、生活行为调整、运动健身、环境职业等；自我干预路径的节点例如药物干预、补充和替代疗法非药物干预措施；所述药物干预是指不借助专业人员协助可自行干预的药品或器械，包括中药、西药、民族药品，其中中医药干预遵循便利性原则结合个体健康数据的干预逻辑为中成药及制剂、中药饮片方剂；医疗卫生系统干预路径的节点例如注射、手术、住院等；除上述三种干预路径外，还包括急诊急救干预路径，当用户的健康问题属于需要急诊急救的，事先提供给用户周围医疗机构的信息，并告知用户立刻前往医疗机构进行急诊急救。As a preferred implementation of this embodiment, the intervention path includes a daily health care intervention path, a self-intervention path (for minor and chronic diseases suitable for medication, diet therapy, physiotherapy, etc.), and a medical and health system intervention path (for injections, Diseases with therapeutic intervention such as surgery, hospitalization, etc.); nodes in the daily health care intervention path such as traditional Chinese medicine, lifestyle behavior adjustment, sports and fitness, environmental occupation, etc.; nodes in the self-intervention path such as drug intervention, complementary and alternative therapies and non-drug interventions; The drug intervention refers to drugs or devices that can be intervened by oneself without the help of professionals, including traditional Chinese medicine, Western medicine, and ethnic medicine. Among them, traditional Chinese medicine intervention follows the principle of convenience and combines individual health data with intervention logic of Chinese patent medicines and preparations, and Chinese medicine preparations. Prescriptions; nodes of the medical and health system intervention paths such as injections, surgeries, hospitalizations, etc.; in addition to the above three intervention paths, it also includes emergency first aid intervention paths. When the user's health problem requires emergency first aid, it will be provided to the user's surrounding medical institutions in advance information and inform users to go to a medical institution for emergency first aid immediately.
所述通过个体健康数据确定该用户的干预路径的步骤具体为：The steps of determining the user's intervention path through individual health data are specifically:
建立基于神经网络的循证模型，获取各种疾病的患病人群的个体健康数据作为训练样本，通过神经网络进行训练，生成预测用户罹患的疾病以及疾病状态的循证模型，所述疾病状态表明患病的严重程度，可以设定为轻症（适合药疗、食疗、理疗、自然疗法等）、中症（需注射、微创护理等）、重症（需手术、高级别护理等）；循证模型预测的结果例如：疾病：胰腺炎，疾病状态：重症；Establish an evidence-based model based on neural networks, obtain individual health data of people suffering from various diseases as training samples, conduct training through neural networks, and generate evidence-based models that predict the diseases and disease states suffered by users. The disease states indicate The severity of the disease can be set as mild (suitable for medication, diet, physiotherapy, natural therapy, etc.), moderate (requiring injections, minimally invasive care, etc.), severe (requiring surgery, high-level care, etc.); cycle The results predicted by the syndrome model are, for example: disease: pancreatitis, disease status: severe;
建立干预路径分类表，根据对应疾病的类型以及疾病状态分别将各状态的疾病划分至日常保健干预路径、自我干预路径或医疗卫生系统干预路径中；例如将级别状态为轻症的流行性感冒划分至自我干预路径，将级别状态为中症的流行性感冒划分至医疗卫生系统干预路径中，又例如将级别状态为轻症的心脏病同时划分至日常保健干预路径和自我干预路径，将级别状态为中症和重症的心脏病同时划分至日常保健干预路径、自我干预路径和医疗卫生系统干预路径中；Establish an intervention path classification table, and classify each state of the disease into a daily health care intervention path, a self-intervention path, or a medical and health system intervention path according to the corresponding disease type and disease status; for example, classify influenza with a mild status To the self-intervention path, the influenza with the level status of moderate disease is divided into the medical and health system intervention path. For example, the heart disease with the level status of mild disease is divided into the daily health care intervention path and the self-intervention path at the same time, and the level status is classified into the self-intervention path. Moderate and severe heart diseases are simultaneously divided into daily health care intervention paths, self-intervention paths and medical and health system intervention paths;
将用户的个体健康数据输入循证模型，循证模型预测当前用户罹患的疾病以及疾病状态；Input the user's individual health data into the evidence-based model, and the evidence-based model predicts the current disease and disease status of the user;
根据预测得到的当前用户罹患的疾病以及疾病状态在所述干预路径分类表中查询该状态的疾病对应的干预路径；对于仅对应一条干预路径的健康问题，仅针对对应类型的干预路径生成干预方案，对于对应多条干预路径的健康问题，针对多条干预路径同时生成干预方案。According to the predicted disease and disease status of the current user, query the intervention path corresponding to the disease in the intervention path classification table; for health problems that only correspond to one intervention path, an intervention plan is generated only for the corresponding type of intervention path. , for health problems corresponding to multiple intervention paths, intervention plans are generated simultaneously for multiple intervention paths.
作为本实施例优选实施方式，所述根据用户的个体健康数据确定可选的节点，将可选的节点组成至少一种干预方案的具体步骤为：As a preferred implementation of this embodiment, the specific steps of determining optional nodes based on the user's individual health data and composing the optional nodes into at least one intervention plan are:
如图2所示，对应每一种干预路径预先构建对应的决策树，所述决策树中包括从下至上的n层决策层（如图中的N1~N4层），每一决策层中包含若干节点（如图中的a1节点、b1节点等等），且每一下层节点至少与一上层节点连接，每一节点均配置有所需的干预措施；As shown in Figure 2, a corresponding decision tree is pre-constructed for each intervention path. The decision tree includes n layers of decision-making layers from bottom to top (layers N1~N4 in the figure). Each decision-making layer contains Several nodes (node a1, node b1, etc. in the figure), and each lower node is connected to at least one upper node, and each node is configured with the required intervention measures;
对应每一决策层设置一评分值范围（如N1层设置为80~95分，N2层设置为60~79分，N3层设置为30~59分，N4层设置为0~29分），且对应每一节点设置优选评分值（如d1优选评分值范围为0~7，d2优选评分值范围为8~10等等）；Set a score value range corresponding to each decision-making layer (for example, the N1 layer is set to 80~95 points, the N2 layer is set to 60~79 points, the N3 layer is set to 30~59 points, and the N4 layer is set to 0~29 points), and Set the preferred score value corresponding to each node (for example, the preferred score value range of d1 is 0~7, the preferred score value range of d2 is 8~10, etc.);
根据所述用户的个体健康数据对该用户进行健康评分，获取该用户的健康评分值；判断该用户的健康评分值属于哪一层决策层的评分值范围，并以该决策层作为起始层，以图2为例，若用户的健康评分为45分，则以N3层为起始层，若用户的健康评分为75分，则从N2层为起始层；从所述起始层中选取优选评分值最接近用户的健康评分值的节点作为起始节点；Perform a health score on the user based on the user's individual health data to obtain the user's health score; determine which decision-making layer's score range the user's health score belongs to, and use this decision-making layer as the starting layer , taking Figure 2 as an example, if the user's health score is 45 points, the N3 layer is the starting layer; if the user's health score is 75 points, the N2 layer is the starting layer; from the starting layer Select the node with the preferred score value closest to the user's health score value as the starting node;
以所述起始节点开始，不断通过上一层连接的节点进行递进，直至连接至最上层决策层中的节点，形成至少一种干预方案；例如从N3层开始，起始节点确定为c3，则通过向上递进，形成的干预方案为c3→b1→a1以及c3→b2→a1两种。Starting from the starting node, it continues to progress through the nodes connected to the upper layer until it is connected to the node in the uppermost decision-making layer to form at least one intervention plan; for example, starting from the N3 layer, the starting node is determined to be c3 , then through upward progression, the intervention plans formed are c3→b1→a1 and c3→b2→a1.
作为本实施例的优选实施方式，所述根据所述用户的个体健康数据对该用户进行健康评分，获取该用户的健康评分值的方法具体为：As a preferred implementation of this embodiment, the user is given a health score based on the user's individual health data, and the method for obtaining the user's health score is specifically:
收集若干患病或健康人群的个体健康数据作为样本，分别对各样本进行人为评分，作为标签值；将所有样本划分为样本集、验证集和测试集；Collect individual health data of several sick or healthy people as samples, manually score each sample as a label value; divide all samples into sample sets, verification sets and test sets;
将样本集输入至神经网络进行迭代训练，得到健康评分模型；通过验证集调整健康评分模型中的特征参数；通过测试集对健康评分模型的评分准确率进行测试；得到训练好的健康评分模型；Input the sample set into the neural network for iterative training to obtain a health score model; adjust the characteristic parameters in the health score model through the verification set; test the scoring accuracy of the health score model through the test set; obtain the trained health score model;
将用户的个体健康数据输入至训练好的健康评分模型中，得出该用户的健康评分值。Input the user's individual health data into the trained health score model to obtain the user's health score value.
作为本实施例的优选实施方式，所述根据干预方案中的每一节点的干预措施所要具备的特征信息以及用户的个体特征数据，到干预措施数据库中匹配适合用户的干预措施的具体步骤为：As a preferred implementation of this embodiment, the specific steps of matching intervention measures suitable for the user in the intervention measures database according to the characteristic information required for the intervention measures at each node in the intervention plan and the user's individual characteristic data are:
获取每一所需的干预措施及该干预措施具备的特征信息，到干预措施数据库中搜寻具有对应特征信息的干预措施作为候选资源；可对干预措施的特征信息进行细化，例如细化至医药商品通常采用的最小包装、最小计量计价单位及型号等，直到不宜分隔为止，例如粒、包、瓶等；Obtain each required intervention and the characteristic information of the intervention, and search for interventions with corresponding characteristic information in the intervention database as candidate resources; the characteristic information of the intervention can be refined, for example, to medicines The minimum packaging, minimum measurement and pricing unit, model, etc. that the commodity usually adopts, until it is no longer suitable to separate, such as granules, bags, bottles, etc.;
获取用户的个体特征，根据用户的个体特征与候选资源的特征信息的匹配度，筛选出适合用户的推荐干预措施。Obtain the user's individual characteristics, and select recommended interventions suitable for the user based on the matching degree between the user's individual characteristics and the characteristic information of the candidate resources.
所述用户的个体特征数据包括用户的生活数据、生理体征数据、常住地址和环境数据、病史药历数据，健康医疗消费数据以及用户偏好。The user's individual characteristic data includes the user's life data, physiological sign data, permanent address and environmental data, medical history and medication data, health and medical consumption data, and user preferences.
作为本实施例的优选实施方式，在将干预方案发生给用户之前，根据干预路径的类型匹配相对应的医生，由医生进行干预方案的补正和确认。所述干预方案的补正和确认由预防医学医生、临床医学医生交替或组合的方式循环进行，同时还生成干预方案相应的预后预测及优选的干预实施建议事项提供用户作为选择干预方案的依据，其中临床医学医生包括中医医生、全科医生、专科医生。As a preferred implementation of this embodiment, before the intervention plan is presented to the user, a corresponding doctor is matched according to the type of intervention path, and the doctor corrects and confirms the intervention plan. The correction and confirmation of the intervention plan are carried out cyclically by preventive medicine doctors and clinical medicine doctors alternately or in combination. At the same time, the corresponding prognosis prediction and preferred intervention implementation suggestions of the intervention plan are also generated to provide users with the basis for selecting the intervention plan, among which Clinical medicine doctors include traditional Chinese medicine doctors, general practitioners, and specialists.
作为本实施例的优选实施方式，用户在实施一项干预措施后，对干预措施实施效果进行评价，将评价结果作为用户的个体特征进行存储，用于干预措施的实时训练和匹配。通过对干预措施进行评价，在健康数据的基础上为个体选择匹配最佳干预措施提供依据。针对的评价主体是人和人群，即从诸多干预措施获得有效性、适合性等循证证据或相关性因果推断，注重效果结局（疗效）及人群情境差异性影响因素，也更有利于对临床预防干预（特别是中医药）做出评价。As a preferred implementation of this embodiment, after the user implements an intervention measure, he or she evaluates the implementation effect of the intervention measure, and stores the evaluation results as the user's individual characteristics for real-time training and matching of the intervention measures. By evaluating interventions, we provide a basis for individuals to choose the best intervention based on health data. The target evaluation subjects are people and groups of people, that is, obtaining evidence-based evidence such as effectiveness and suitability or relevant causal inferences from many intervention measures, focusing on the effect outcome (efficacy) and factors affecting differences in population situations, which is also more conducive to clinical evaluation. preventive interventions (especially traditional Chinese medicine).
本实施例一方面建立了人与电子信息决策辅助系统、区域人群健康信息系统的持续关联关系，进而再拓展到自然系统（监测干预装备）和社会服务系统，使得个体及区域群体健康、干预措施、政策形成闭合关联，有利于个体干预、区域人群干预、政策措施整合协同，多方共同决策实现基于个体的群体效益最大化。另一方面，根据健康数据为用户提出干预路径及具体措施，对干预路径进行分类，再进一步提供优化匹配干预措施可选项信息及预后预测，形成干预方案为用户提供实施建议，而对于非个体化干预措施匹配评价信息或研究成果则通过决策辅助系统网络平台共享。用户获得精准的诊断结果、优化的解决方案及充分的干预措施信息，实现医疗保健自主决策或参与决策。因此，通过真实世界循证寻求那些利用不充分或未被利用的干预证据，特别是效果显著的低成本干预措施的证据转化应用具有重大意义，可有效遏制医疗费用过快增长。On the one hand, this embodiment establishes a continuous relationship between people and electronic information decision-making assistance systems and regional population health information systems, and then extends it to natural systems (monitoring intervention equipment) and social service systems, making individual and regional group health and intervention measures , policies form a closed relationship, which is conducive to individual intervention, regional population intervention, integration and coordination of policy measures, and multi-party joint decision-making to maximize individual-based group benefits. On the other hand, it proposes intervention paths and specific measures for users based on health data, classifies the intervention paths, and further provides optimized matching intervention option information and prognosis predictions to form an intervention plan to provide implementation suggestions for users. For non-individualized Intervention matching evaluation information or research results are shared through the decision-making assistance system network platform. Users obtain accurate diagnostic results, optimized solutions and sufficient information on intervention measures, allowing them to make independent decisions or participate in decision-making in healthcare. Therefore, it is of great significance to seek evidence of underutilized or unutilized interventions through real-world evidence, especially the evidence translation and application of low-cost interventions with significant effects, which can effectively curb the excessive growth of medical expenses.
实施例二：Example 2:
本实施例提供一种电子设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时实现如本发明任一实施例所述的围绕全人健康的干预措施循证与决策辅助方法。This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method described in any embodiment of the present invention. Evidence-based and decision-aiding methods for interventions around whole-person health.
以上所述仅为本发明的实施例，并非因此限制本发明的专利范围，凡是利用本发明说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本发明的专利保护范围内。The above are only examples of the present invention, and do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the description and drawings of the present invention, or directly or indirectly applied to other related technologies fields are equally included in the scope of patent protection of the present invention.
Claims (10)
- 围绕全人健康的干预措施循证与决策辅助方法，其特征在于，包括以下步骤：Evidence-based and decision-making aid methods for interventions focusing on whole-person health are characterized by including the following steps:建立干预措施数据库，所述干预措施数据库中包括多种不同类型的干预措施子数据库，各所述干预措施子数据库中收集有若干对应类型的干预措施以及各所述干预措施的特征信息；Establishing an intervention measure database, the intervention measure database includes a plurality of different types of intervention measure sub-databases, and each intervention measure sub-database collects several corresponding types of intervention measures and characteristic information of each of the intervention measures;获取用户的个体特征数据以及个体健康数据，通过个体健康数据确定该用户的干预路径；Obtain the user's individual characteristic data and individual health data, and determine the user's intervention path through the individual health data;将用户个体作为整体系统，细化干预路径并形成干预方案，所述干预路径包括若干节点，每一节点包含有所需的干预措施所要具备的特征信息，根据用户的个体健康数据确定可选的节点，将可选的节点组成至少一种干预方案；Treat the individual user as an overall system, refine the intervention path and form an intervention plan. The intervention path includes several nodes. Each node contains characteristic information required for the required intervention measures. Optional options are determined based on the user's individual health data. Node, combine optional nodes to form at least one intervention plan;提供干预方案给用户，当存在多种干预方案时，由用户选择最终干预方案；当干预方案确定后，根据干预方案中的每一节点的干预措施所要具备的特征信息以及用户的个体特征数据，到干预措施数据库中匹配适合用户的干预措施，并提供给用户。Intervention plans are provided to the user. When there are multiple intervention plans, the user selects the final intervention plan; when the intervention plan is determined, based on the characteristic information of the intervention measures at each node in the intervention plan and the user's individual characteristic data, Match interventions suitable for users in the intervention database and provide them to users.
- 根据权利要求1所述的围绕全人健康的干预措施循证与决策辅助方法，其特征在于：The evidence-based and decision-making aid method for intervention measures around whole-person health according to claim 1, characterized by:所述干预措施子数据库包括中医药干预数据库、西医药干预数据库、补充和替代疗法数据库，其中，西医指的是现代医学；The intervention sub-database includes a traditional Chinese medicine intervention database, a western medicine intervention database, and a complementary and alternative therapy database, where western medicine refers to modern medicine;所述中医药干预数据库中包括中医药领域中的若干项干预措施；The traditional Chinese medicine intervention database includes several intervention measures in the field of traditional Chinese medicine;所述西医药干预数据库中包括西医药领域中的若干项干预措施；The Western medicine intervention database includes several intervention measures in the field of Western medicine;所述补充和替代疗法干预数据库中包括在中医药领域和西医药领域之外的若干项干预措施。The complementary and alternative therapy intervention database includes several interventions outside the fields of traditional Chinese medicine and Western medicine.
- 根据权利要求2所述的围绕全人健康的干预措施循证与决策辅助方法，其特征在于：所述干预路径包括日常保健干预路径、自我干预路径、医疗卫生系统干预路径；The evidence-based and decision-making aid method for intervention measures around whole-person health according to claim 2, characterized in that: the intervention path includes a daily health care intervention path, a self-intervention path, and a medical and health system intervention path;所述通过个体健康数据确定该用户的干预路径的步骤具体为：The steps of determining the user's intervention path through individual health data are specifically:建立基于神经网络的循证模型，获取各种疾病的患病人群的个体健康数据作为训练样本，通过神经网络进行训练，生成预测用户罹患的疾病以及疾病状态的循证模型，所述疾病状态表明患病的严重程度；Establish an evidence-based model based on neural networks, obtain individual health data of people suffering from various diseases as training samples, conduct training through neural networks, and generate evidence-based models that predict the diseases and disease states suffered by users. The disease states indicate severity of illness;建立干预路径分类表，根据对应疾病的类型以及疾病状态分别将各状态的疾病划分至日常保健干预路径、自我干预路径或医疗卫生系统干预路径中；Establish an intervention path classification table, and classify the diseases in each state into daily health care intervention paths, self-intervention paths or medical and health system intervention paths according to the corresponding disease types and disease states;将用户的个体健康数据输入循证模型，循证模型预测当前用户罹患的疾病以及疾病状态；Input the user's individual health data into the evidence-based model, and the evidence-based model predicts the current disease and disease status of the user;根据预测得到的当前用户罹患的疾病以及疾病状态在所述干预路径分类表中查询该状态的疾病对应的干预路径。According to the predicted disease and disease state suffered by the current user, the intervention path corresponding to the disease in this state is queried in the intervention path classification table.
- 根据权利要求3所述的围绕全人健康的干预措施循证与决策辅助方法，其特征在于，所述根据用户的个体健康数据确定可选的节点，将可选的节点组成至少一种干预方案的具体步骤为：The evidence-based and decision-making assistance method for intervention measures around whole-person health according to claim 3, characterized in that the optional nodes are determined according to the user's individual health data, and the optional nodes are composed of at least one intervention plan. The specific steps are:对应每一种干预路径预先构建对应的决策树，所述决策树中包括从下至上的n层决策层，每一决策层中包含若干节点，且每一下层节点至少与一上层节点连接，每一节点均配置有所需的干预措施；A corresponding decision tree is pre-constructed for each intervention path. The decision tree includes n layers of decision-making layers from bottom to top. Each decision-making layer contains several nodes, and each lower-layer node is connected to at least one upper-layer node. Each lower-layer node is connected to at least one upper-layer node. Each node is configured with the required interventions;对应每一决策层设置一评分值范围，且对应每一节点设置优选评分值；A scoring value range is set corresponding to each decision-making layer, and a preferred scoring value is set corresponding to each node;根据所述用户的个体健康数据对该用户进行健康评分，获取该用户的健康评分值；判断该用户的健康评分值属于哪一层决策层的评分值范围，并以该决策层作为起始层，从所述起始层中选取优选评分值最接近用户的健康评分值的节点作为起始节点；Perform a health score on the user based on the user's individual health data to obtain the user's health score; determine which decision-making layer's score range the user's health score belongs to, and use this decision-making layer as the starting layer , select the node whose preferred score value is closest to the user's health score value from the starting layer as the starting node;以所述起始节点开始，不断通过上一层连接的节点进行递进，直至连接至最上层决策层中的节点，形成至少一种干预方案。Starting from the starting node, it continues to advance through the nodes connected in the upper layer until it is connected to the node in the uppermost decision-making layer, forming at least one intervention plan.
- 根据权利要求4所述的围绕全人健康的干预措施循证与决策辅助方法，其特征在于，所述根据所述用户的个体健康数据对该用户进行健康评分，获取该用户的健康评分值的方法具体为：The evidence-based and decision-making assistance method for intervention measures around whole-person health according to claim 4, characterized in that: performing a health score on the user based on the user's individual health data, and obtaining the user's health score value The specific method is:收集若干患病或健康人群的个体健康数据作为样本，分别对各样本进行人为评分，作为标签值；将所有样本划分为样本集、验证集和测试集；Collect individual health data of several sick or healthy people as samples, manually score each sample as a label value; divide all samples into sample sets, verification sets and test sets;将样本集输入至神经网络进行迭代训练，得到健康评分模型；通过验证集调整健康评分模型中的特征参数；通过测试集对健康评分模型的评分准确率进行测试；得到训练好的健康评分模型；Input the sample set into the neural network for iterative training to obtain a health score model; adjust the characteristic parameters in the health score model through the verification set; test the scoring accuracy of the health score model through the test set; obtain the trained health score model;将用户的个体健康数据输入至训练好的健康评分模型中，得出该用户的健康评分值。Input the user's individual health data into the trained health score model to obtain the user's health score value.
- 根据权利要求4所述的围绕全人健康的干预措施循证与决策辅助方法，其特征在于，所述根据干预方案中的每一节点的干预措施所要具备的特征信息以及用户的个体特征数据，到干预措施数据库中匹配适合用户的干预措施的具体步骤为：The evidence-based and decision-making aid method for intervention measures focusing on whole-person health according to claim 4, characterized in that the characteristic information required for the intervention measures according to each node in the intervention plan and the user's individual characteristic data, The specific steps to match user-appropriate interventions in the intervention database are:获取每一所需的干预措施及该干预措施具备的特征信息，到干预措施数据库中搜寻具有对应特征信息的干预措施作为候选资源；Obtain each required intervention and the characteristic information of the intervention, and search for intervention measures with corresponding characteristic information in the intervention database as candidate resources;获取用户的个体特征，根据用户的个体特征与候选资源的特征信息的匹配度，筛选出适合用户的推荐干预措施。Obtain the user's individual characteristics, and select recommended interventions suitable for the user based on the matching degree between the user's individual characteristics and the characteristic information of the candidate resources.
- 根据权利要求4所述的围绕全人健康的干预措施循证与决策辅助方法，其特征在于：在将干预方案发送给用户之前，根据干预路径的类型匹配相对应的医生，由医生进行干预方案的补正和确认。The evidence-based and decision-making aid method for intervention measures around whole-person health according to claim 4, characterized in that: before sending the intervention plan to the user, the corresponding doctor is matched according to the type of intervention path, and the doctor carries out the intervention plan. Correction and confirmation.
- 根据权利要求7所述的围绕全人健康的干预措施循证与决策辅助方法，其特征在于：所述干预方案的补正和确认由预防医学医生、临床医学医生交替或组合的方式循环进行，同时还生成干预方案相应的预后预测及优选的干预实施建议事项提供用户作为选择干预方案的依据，其中临床医学医生包括中医医生、全科医生、专科医生。The evidence-based and decision-making aid method for intervention measures around whole-person health according to claim 7, characterized in that: the correction and confirmation of the intervention plan are performed cyclically by preventive medicine doctors and clinical medicine doctors alternately or in combination, and at the same time It also generates prognosis predictions corresponding to the intervention plan and preferred intervention implementation recommendations to provide users with a basis for selecting intervention plans, among which clinical doctors include traditional Chinese medicine doctors, general practitioners, and specialists.
- 根据权利要求6所述的围绕全人健康的干预措施循证与决策辅助方法，其特征在于：用户在实施一项干预措施后，对干预措施效果进行评价，将评价结果作为用户的个体特征进行存储，用于干预措施的实时训练和匹配。The evidence-based and decision-making aid method for intervention measures focusing on whole-person health according to claim 6, characterized in that: after the user implements an intervention measure, the effect of the intervention measure is evaluated, and the evaluation results are used as the user's individual characteristics. Storage,for real-time training and matching of interventions.
- 一种电子设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，其特征在于，所述处理器执行所述程序时实现如权利要求1所述的围绕全人健康的干预措施循证与决策辅助方法。An electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that when the processor executes the program, it implements the method of surrounding the whole person as claimed in claim 1 Evidence-based and decision aid methods for healthy interventions.
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