WO2021004168A1 - Artificial intelligence-type medical data integration system and method - Google Patents

Artificial intelligence-type medical data integration system and method Download PDF

Info

Publication number
WO2021004168A1
WO2021004168A1 PCT/CN2020/091445 CN2020091445W WO2021004168A1 WO 2021004168 A1 WO2021004168 A1 WO 2021004168A1 CN 2020091445 W CN2020091445 W CN 2020091445W WO 2021004168 A1 WO2021004168 A1 WO 2021004168A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
artificial intelligence
measurement
processing platform
intelligent processing
Prior art date
Application number
PCT/CN2020/091445
Other languages
French (fr)
Chinese (zh)
Inventor
赵峥来
Original Assignee
江苏博子岛智能科技有限公司
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by 江苏博子岛智能科技有限公司 filed Critical 江苏博子岛智能科技有限公司
Publication of WO2021004168A1 publication Critical patent/WO2021004168A1/en

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
    • 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
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • 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
    • G16H80/00—ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring

Definitions

  • the invention relates to the field of intelligent medical care, and more specifically, it relates to a medical data integration system and method with artificial intelligence.
  • Health issues have always been a major issue facing centuries as a whole, especially now that we have entered an aging society, and young people’s health issues have gradually become prominent under the heavy work pressure.
  • we should pay attention to our health seek medical treatment in time when we feel unwell, and deal with it before the disease worsens.
  • the purpose of the present invention is to provide a medical data integration system and method with artificial intelligence, which has the advantage of solving medical resource allocation.
  • a medical data integration system with artificial intelligence including an information input terminal, an intelligent processing platform and a graphic display screen, characterized in that the information input terminal includes a weight scale, a height meter, a blood pressure meter, a blood glucose meter, and a blood oxygen meter And an electronic stethoscope, the intelligent processing platform includes a data collection unit, a data calculation unit, a data storage unit, a data analysis unit and a path allocation unit.
  • the weight data, height data, blood pressure data, blood glucose data, blood oxygen data and heart rate data detected by the tester are uploaded to the intelligent processing platform.
  • the data collection unit collects the data and stores the data in the data storage unit to calculate the data Then carry out data analysis, and display the results of the data analysis on the graphic display.
  • a medical data integration method with artificial intelligence including the following steps:
  • the intelligent processing platform makes judgments based on the detected data, and automatically gives analysis results and suggestions on the graphic display. Select the hospital for the severity level. According to the hospital's positioning and its own positioning, the hospital with the shortest Manhattan distance is calculated as the priority recommended hospital.
  • Manhattan distance is a geometric term used in geometric measurement space to indicate two points in standard coordinates Attach the total absolute wheelbase.
  • the regular testing includes height measurement, weight measurement, blood pressure measurement, blood glucose measurement, blood oxygen measurement, and heart rate measurement.
  • the detection method includes measuring a single data. When the detected data exceeds a prescribed threshold, the examiner is reminded to consult a doctor.
  • the additional measurement method also includes adding height data, weight data, blood pressure data, blood glucose data, blood oxygen data, and Heart rate data is used as a feature to train a large amount of historical data to obtain a disease prediction model, and predict the probability of a related disease based on the tester’s data.
  • the data training method is supervised learning in machine learning.
  • the weight data, height data, blood pressure data, blood glucose data, blood oxygen data and heart rate data detected by the tester are uploaded to the intelligent processing platform.
  • the data collection unit collects the data and stores the data in the data storage unit. After calculating the data Perform data analysis and display the results of the data analysis on the graphic display;
  • the data training method is supervised learning in machine learning, and the newly generated data and detection results will continue to update the weights in the supervised learning algorithm.
  • Figure 1 is a schematic diagram of the system structure of the present invention.
  • a medical data integration system with artificial intelligence including an information input terminal, an intelligent processing platform and a graphic display screen, characterized in that the information input terminal includes a weight scale, a height meter, a blood pressure meter, a blood glucose meter, and a blood oxygen meter And an electronic stethoscope, the intelligent processing platform includes a data collection unit, a data calculation unit, a data storage unit, a data analysis unit and a path allocation unit.
  • the weight data, height data, blood pressure data, blood glucose data, blood oxygen data and heart rate data detected by the tester are uploaded to the intelligent processing platform.
  • the data collection unit collects the data and stores the data in the data storage unit to calculate the data Then carry out data analysis, and display the results of the data analysis on the graphic display.
  • the intelligent processing platform makes judgments based on the detected data, and automatically gives analysis results and suggestions on the graphic display. Select the hospital for the severity level. According to the hospital's positioning and its own positioning, the hospital with the shortest Manhattan distance is calculated as the priority recommended hospital.
  • Manhattan distance is a geometric term used in geometric measurement space to indicate two points in standard coordinates Attach the total absolute wheelbase.
  • the regular testing includes height measurement, weight measurement, blood pressure measurement, blood glucose measurement, blood oxygen measurement, and heart rate measurement.
  • the detection method includes measuring a single data. When the detected data exceeds a prescribed threshold, the examiner is reminded to consult a doctor.
  • the additional measurement method also includes adding height data, weight data, blood pressure data, blood glucose data, blood oxygen data, and Heart rate data is used as a feature to train a large amount of historical data to obtain a disease prediction model, and predict the probability of a related disease based on the tester’s data.
  • the data training method is supervised learning in machine learning.
  • a medical data integration method with artificial intelligence On the basis of the first embodiment, more specifically, the test result of the tester's blood pressure exceeds the normal value, the path allocation unit detects the nearby small clinics, compares the Manhattan distance, and selects the nearest The clinic recommends the examiner to consult the doctor on the graphic display screen and purchase antihypertensive drugs according to the instructions.
  • An artificial intelligence-equipped medical data integration method Based on the first embodiment, more specifically, the heart rate is higher in the first test, and the heart rate is gradually reduced after several tests, and the blood pressure is gradually lowered. If you have cancer, It is detected that the decrease in data is not particularly significant, and it is probably not caused by cancer.
  • the path allocation unit will conduct physical examinations and consultations based on the recommendation of the nearest hospital in Manhattan. If it is obviously consistent with cancer symptoms, immediately warn you to go to a tertiary hospital for medical treatment, and provide an appointment function.
  • a medical data integration method with artificial intelligence can adopt the support vector machine algorithm.
  • a large amount of historical data is used as training data.
  • the training data set is (weight data, height data, blood pressure data, blood glucose data, blood Oxygen data, heart rate data), the output result is the probability and severity level of the disease type, and the patient’s result is whether the patient has the disease as a feedback to re-correct the model, continuously improve the accuracy of the model, and assign it according to the severity level path
  • the unit allocates the corresponding level of hospitals, balances medical resources, and prevents excessive waste of medical resources.

Abstract

An artificial intelligence-type medical data integration system and method, which solve the technical problem of medical resource allocation. Data in respect of measured weight, height, blood pressure, blood glucose, blood oxygen and heart rate of a patient are uploaded to an intelligent processing platform; a data collection unit collects data and stores same in a data storage unit; the data is calculated and analyzed; and results of the data analysis are displayed on a graphical display screen. By means of a measurement device present inside the home, a person may regularly take body measurements and upload measurement data to an intelligent processing platform. The intelligent processing platform makes a determination on the basis of the measurement data, and automatically provides analysis results and recommendations on a graphical display screen. Hospitals may be selected for assignment on the basis of the severity of the disease and, on the basis of the locations of the hospitals and the location of the patient, calculation is performed to identify the hospital having the shortest Manhattan distance as the preferred recommended hospital.

Description

一种具备人工智能的医疗数据整合系统及方法A medical data integration system and method with artificial intelligence 技术领域Technical field
本发明涉及智能医疗领域，更具体地说，它涉及一种具备人工智能的医疗数据整合系统及方法。The invention relates to the field of intelligent medical care, and more specifically, it relates to a medical data integration system and method with artificial intelligence.
背景技术Background technique
健康问题一直是整个人类面临的重大问题，尤其现在步入老龄化社会，而且年轻人在繁重的工作压力下将康问题也逐渐凸显。在日常生活中，我们就应当注重健康，当出现身体不适及时就医，在疾病恶化之前就及时处理。Health issues have always been a major issue facing mankind as a whole, especially now that we have entered an aging society, and young people’s health issues have gradually become prominent under the heavy work pressure. In daily life, we should pay attention to our health, seek medical treatment in time when we feel unwell, and deal with it before the disease worsens.
但是，现在的医疗资源非常紧缺，一旦生病需要就医往往要排队很久，延误治疗时机，而且，医疗资源分配不均衡，很多微小的病情都去少数几个优质三甲医院，导致优质三甲医院医疗资源难以匹配需求，也难以对真正需要高端治疗的病人做出服务，非三甲医院的医疗资源却极大闲置，资源供需错配，对社会是极大地损失和浪费。However, the current medical resources are very scarce. Once you are sick, you will often have to queue for a long time to see a doctor, delaying the timing of treatment. Moreover, the distribution of medical resources is uneven. Many minor illnesses go to a few high-quality Class-A hospitals, which makes it difficult for high-quality Class-A hospitals to have medical resources. It is also difficult to provide services to patients who really need high-end treatment. The medical resources of non-Third-A hospitals are extremely idle. The mismatch of resource supply and demand is a great loss and waste to society.
因此，我们需要一种具备人工智能的医疗数据整合系统及方法。Therefore, we need a medical data integration system and method with artificial intelligence.
发明内容Summary of the invention
针对上述问题，本发明的目的在于提供一种具备人工智能的医疗数据整合系统及方法，其具有解决医疗资源配置的优点。In view of the above problems, the purpose of the present invention is to provide a medical data integration system and method with artificial intelligence, which has the advantage of solving medical resource allocation.
本发明的上述发明目的是通过以下技术方案得以实现的：The above-mentioned object of the present invention is achieved through the following technical solutions:
一种具备人工智能的医疗数据整合系统，包括信息输入端、智能处理平台和图形显示屏，其特征在于，所述信息输入端包括体重计、身高仪、血压计、血糖仪、血氧测量仪和电子听诊器，所述智能处理平台包括数据收集单元、数据计算单元、数据存储单元、数据分析单元和路径分配单元。A medical data integration system with artificial intelligence, including an information input terminal, an intelligent processing platform and a graphic display screen, characterized in that the information input terminal includes a weight scale, a height meter, a blood pressure meter, a blood glucose meter, and a blood oxygen meter And an electronic stethoscope, the intelligent processing platform includes a data collection unit, a data calculation unit, a data storage unit, a data analysis unit and a path allocation unit.
通过上述技术方案，检测者检测到的体重数据、身高数据、血压数据、血糖数据、血氧数据和心率数据上传到智能处理平台，数据收集单元收集数据并将数据存储到数据存储单元，计算数据后进行数据分析，并将数据分析的结果显示在图形显示屏上。Through the above technical solutions, the weight data, height data, blood pressure data, blood glucose data, blood oxygen data and heart rate data detected by the tester are uploaded to the intelligent processing platform. The data collection unit collects the data and stores the data in the data storage unit to calculate the data Then carry out data analysis, and display the results of the data analysis on the graphic display.
一种具备人工智能的医疗数据整合方法，包括如下步骤：A medical data integration method with artificial intelligence, including the following steps:
人们在家中设置有检测设备，定期检测身体并将检测数据上传至智能处理平台，智能处理平台根据检测后的数据进行判断，并自动在图形显示屏上给出分析结果和建议，同时根据病情的严重程度选择医院进行分配，根据医院的定位和自己的定位，将计算出曼哈顿距离最短的医院作为优先推荐医院，曼哈顿距离是使用在几何度量空间的几何用语，用以标明两个点在标准坐标系上绝对轴距总和。People are equipped with testing equipment at home to check the body regularly and upload the testing data to the intelligent processing platform. The intelligent processing platform makes judgments based on the detected data, and automatically gives analysis results and suggestions on the graphic display. Select the hospital for the severity level. According to the hospital's positioning and its own positioning, the hospital with the shortest Manhattan distance is calculated as the priority recommended hospital. Manhattan distance is a geometric term used in geometric measurement space to indicate two points in standard coordinates Attach the total absolute wheelbase.
进一步地，所述定期检测包括身高测量、体重测量、血压测量、血糖测量、血氧测量和心率测量。Further, the regular testing includes height measurement, weight measurement, blood pressure measurement, blood glucose measurement, blood oxygen measurement, and heart rate measurement.
进一步地，检测方法包括对单一数据进行测量，当检测到的数据超过规定阈值时，提醒检测者咨询医生，加测方法还包括将身高数据、体重数据、血压数据、血糖数据、血氧数据和心率数据作为一个特征，将大量历史数据进行训练得到疾病预测模型，根据检测者的 数据预测可能患有相关疾病的概率。Further, the detection method includes measuring a single data. When the detected data exceeds a prescribed threshold, the examiner is reminded to consult a doctor. The additional measurement method also includes adding height data, weight data, blood pressure data, blood glucose data, blood oxygen data, and Heart rate data is used as a feature to train a large amount of historical data to obtain a disease prediction model, and predict the probability of a related disease based on the tester’s data.
进一步地，所述数据训练方法为机器学习中的监督式学习。Further, the data training method is supervised learning in machine learning.
进一步地，新产生的数据和检测结果会继续更新监督式学习算法中的权重。Furthermore, the newly generated data and detection results will continue to update the weights in the supervised learning algorithm.
与现有技术相比，本发明的有益效果是：Compared with the prior art, the beneficial effects of the present invention are:
(1)通过检测者检测到的体重数据、身高数据、血压数据、血糖数据、血氧数据和心率数据上传到智能处理平台，数据收集单元收集数据并将数据存储到数据存储单元，计算数据后进行数据分析，并将数据分析的结果显示在图形显示屏上；(1) The weight data, height data, blood pressure data, blood glucose data, blood oxygen data and heart rate data detected by the tester are uploaded to the intelligent processing platform. The data collection unit collects the data and stores the data in the data storage unit. After calculating the data Perform data analysis and display the results of the data analysis on the graphic display;
(2)通过人们在家中设置有检测设备，定期检测身体并将检测数据上传至智能处理平台，智能处理平台根据检测后的数据进行判断，并自动在图形显示屏上给出分析结果和建议，同时根据病情的严重程度选择医院进行分配，根据医院的定位和自己的定位，将计算出曼哈顿距离最短的医院作为优先推荐医院；(2) People set up detection equipment at home to check the body regularly and upload the detection data to the intelligent processing platform. The intelligent processing platform makes judgments based on the detected data and automatically gives analysis results and suggestions on the graphic display. At the same time, select hospitals for allocation according to the severity of the disease. According to the hospital's positioning and its own positioning, the hospital with the shortest Manhattan distance will be calculated as the preferred hospital;
(3)通过所述数据训练方法为机器学习中的监督式学习，新产生的数据和检测结果会继续更新监督式学习算法中的权重。(3) The data training method is supervised learning in machine learning, and the newly generated data and detection results will continue to update the weights in the supervised learning algorithm.
附图说明Description of the drawings
图1为本发明的系统结构示意图。Figure 1 is a schematic diagram of the system structure of the present invention.
具体实施方式Detailed ways
下面结合附图和实施例，对本发明进行详细描述。The present invention will be described in detail below with reference to the drawings and embodiments.
实施例一Example one
一种具备人工智能的医疗数据整合系统，包括信息输入端、智能处理平台和图形显示屏，其特征在于，所述信息输入端包括体重计、身高仪、血压计、血糖仪、血氧测量仪和电子听诊器，所述智能处理平台包括数据收集单元、数据计算单元、数据存储单元、数据分析单元和路径分配单元。A medical data integration system with artificial intelligence, including an information input terminal, an intelligent processing platform and a graphic display screen, characterized in that the information input terminal includes a weight scale, a height meter, a blood pressure meter, a blood glucose meter, and a blood oxygen meter And an electronic stethoscope, the intelligent processing platform includes a data collection unit, a data calculation unit, a data storage unit, a data analysis unit and a path allocation unit.
通过上述技术方案，检测者检测到的体重数据、身高数据、血压数据、血糖数据、血氧数据和心率数据上传到智能处理平台，数据收集单元收集数据并将数据存储到数据存储单元，计算数据后进行数据分析，并将数据分析的结果显示在图形显示屏上。Through the above technical solutions, the weight data, height data, blood pressure data, blood glucose data, blood oxygen data and heart rate data detected by the tester are uploaded to the intelligent processing platform. The data collection unit collects the data and stores the data in the data storage unit to calculate the data Then carry out data analysis, and display the results of the data analysis on the graphic display.
人们在家中设置有检测设备，定期检测身体并将检测数据上传至智能处理平台，智能处理平台根据检测后的数据进行判断，并自动在图形显示屏上给出分析结果和建议，同时根据病情的严重程度选择医院进行分配，根据医院的定位和自己的定位，将计算出曼哈顿距离最短的医院作为优先推荐医院，曼哈顿距离是使用在几何度量空间的几何用语，用以标明两个点在标准坐标系上绝对轴距总和。People are equipped with testing equipment at home to check the body regularly and upload the testing data to the intelligent processing platform. The intelligent processing platform makes judgments based on the detected data, and automatically gives analysis results and suggestions on the graphic display. Select the hospital for the severity level. According to the hospital's positioning and its own positioning, the hospital with the shortest Manhattan distance is calculated as the priority recommended hospital. Manhattan distance is a geometric term used in geometric measurement space to indicate two points in standard coordinates Attach the total absolute wheelbase.
进一步地，所述定期检测包括身高测量、体重测量、血压测量、血糖测量、血氧测量和心率测量。Further, the regular testing includes height measurement, weight measurement, blood pressure measurement, blood glucose measurement, blood oxygen measurement, and heart rate measurement.
进一步地，检测方法包括对单一数据进行测量，当检测到的数据超过规定阈值时，提醒检测者咨询医生，加测方法还包括将身高数据、体重数据、血压数据、血糖数据、血氧数据和心率数据作为一个特征，将大量历史数据进行训练得到疾病预测模型，根据检测者的数据预测可能患有相关疾病的概率。Further, the detection method includes measuring a single data. When the detected data exceeds a prescribed threshold, the examiner is reminded to consult a doctor. The additional measurement method also includes adding height data, weight data, blood pressure data, blood glucose data, blood oxygen data, and Heart rate data is used as a feature to train a large amount of historical data to obtain a disease prediction model, and predict the probability of a related disease based on the tester’s data.
进一步地，所述数据训练方法为机器学习中的监督式学习。Further, the data training method is supervised learning in machine learning.
进一步地，新产生的数据和检测结果会继续更新监督式学习算法中的权重。Furthermore, the newly generated data and detection results will continue to update the weights in the supervised learning algorithm.
实施例二Example two
一种具备人工智能的医疗数据整合方法，在实施例一的基础上，更具体地，检测者测试结果血压超出正常值，路径分配单元检测附近的小诊所，并比较曼哈顿距离，选择出最近的诊所在图形显示屏上推荐给检测者咨询医生并根据指示购买降压药。A medical data integration method with artificial intelligence. On the basis of the first embodiment, more specifically, the test result of the tester's blood pressure exceeds the normal value, the path allocation unit detects the nearby small clinics, compares the Manhattan distance, and selects the nearest The clinic recommends the examiner to consult the doctor on the graphic display screen and purchase antihypertensive drugs according to the instructions.
实施例三Example three
一种具备人工智能的医疗数据整合方法，在实施例一的基础上，更具体地，第一次检测结果心率较高，之后几次检测心率逐渐降低，血压逐渐降低，可能患有癌症，如果检测到数据降低不是特别显著，很可能不是癌症引起，路径分配单元会根据推荐附近曼哈顿距离最近的医院进行体检和咨询。如果明显与癌症症状相吻合，立即警告去三甲医院就医，同时提供预约功能。An artificial intelligence-equipped medical data integration method. Based on the first embodiment, more specifically, the heart rate is higher in the first test, and the heart rate is gradually reduced after several tests, and the blood pressure is gradually lowered. If you have cancer, It is detected that the decrease in data is not particularly significant, and it is probably not caused by cancer. The path allocation unit will conduct physical examinations and consultations based on the recommendation of the nearest hospital in Manhattan. If it is obviously consistent with cancer symptoms, immediately warn you to go to a tertiary hospital for medical treatment, and provide an appointment function.
实施例四Example four
一种具备人工智能的医疗数据整合方法，预测模型的实现方式可以采用支持向量机算法，将大量的历史数据作为训练数据，训练数据集为(体重数据，身高数据，血压数据，血糖数据，血氧数据，心率数据)，输出结果为患病种类的概率和严重性等级，并且将患者的结果是否患有该种病作为反馈重新修正模型，不断提高模型的准确度，根据严重性等级路径分配单元分配相应等级的医院，平衡医疗资源，防止出现医疗资源过度浪费的情况。A medical data integration method with artificial intelligence. The implementation of the prediction model can adopt the support vector machine algorithm. A large amount of historical data is used as training data. The training data set is (weight data, height data, blood pressure data, blood glucose data, blood Oxygen data, heart rate data), the output result is the probability and severity level of the disease type, and the patient’s result is whether the patient has the disease as a feedback to re-correct the model, continuously improve the accuracy of the model, and assign it according to the severity level path The unit allocates the corresponding level of hospitals, balances medical resources, and prevents excessive waste of medical resources.
以上所述仅是本发明的优选实施方式，本发明的保护范围并不仅局限于上述实施例，凡属于本发明思路下的技术方案均属于本发明的保护范围。应当指出，对于本技术领域的普通技术人员来说，在不脱离本发明原理前提下的若干改进和润饰，这些改进和润饰也应视为本发明的保护范围。The above are only the preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments. All technical solutions under the idea of the present invention belong to the protection scope of the present invention. It should be pointed out that for those of ordinary skill in the art, several improvements and modifications made without departing from the principles of the present invention should also be regarded as the protection scope of the present invention.

Claims (6)

  1. 一种具备人工智能的医疗数据整合系统，包括信息输入端、智能处理平台和图形显示屏，其特征在于，所述信息输入端包括体重计、身高仪、血压计、血糖仪、血氧测量仪和电子听诊器，所述智能处理平台包括数据收集单元、数据计算单元、数据存储单元、数据分析单元和路径分配单元。A medical data integration system with artificial intelligence, including an information input terminal, an intelligent processing platform and a graphic display screen, characterized in that the information input terminal includes a weight scale, a height meter, a blood pressure meter, a blood glucose meter, and a blood oxygen meter And an electronic stethoscope, the intelligent processing platform includes a data collection unit, a data calculation unit, a data storage unit, a data analysis unit and a path allocation unit.
  2. 一种具备人工智能的医疗数据整合方法，其特征在于，包括如下步骤：A medical data integration method with artificial intelligence is characterized in that it includes the following steps:
    人们在家中设置有检测设备，定期检测身体并将检测数据上传至智能处理平台，智能处理平台根据检测后的数据进行判断，并自动在图形显示屏上给出分析结果和建议，同时根据病情的严重程度选择医院进行分配，根据医院的定位和自己的定位，将计算出曼哈顿距离最短的医院作为优先推荐医院。People are equipped with testing equipment at home to check the body regularly and upload the testing data to the intelligent processing platform. The intelligent processing platform makes judgments based on the detected data, and automatically gives analysis results and suggestions on the graphic display. Select the hospital for the severity of the assignment. According to the hospital's positioning and its own positioning, the hospital with the shortest Manhattan distance will be calculated as the preferred hospital.
  3. 根据权利要求2所述的具备人工智能的医疗数据整合方法，其特征在于，所述定期检测包括身高测量、体重测量、血压测量、血糖测量、血氧测量和心率测量。The medical data integration method with artificial intelligence according to claim 2, characterized in that the regular detection includes height measurement, weight measurement, blood pressure measurement, blood glucose measurement, blood oxygen measurement, and heart rate measurement.
  4. 根据权利要求3所述的具备人工智能的医疗数据整合方法，其特征在于，检测方法包括对单一数据进行测量，当检测到的数据超过规定阈值时，提醒检测者咨询医生，加测方法还包括将身高数据、体重数据、血压数据、血糖数据、血氧数据和心率数据作为一个特征，将大量历史数据进行训练得到疾病预测模型，根据检测者的数据预测可能患有相关疾病的概率。The medical data integration method with artificial intelligence according to claim 3, characterized in that the detection method comprises measuring a single data, when the detected data exceeds a prescribed threshold, reminding the examiner to consult a doctor, and the additional measuring method further comprises Take height data, weight data, blood pressure data, blood glucose data, blood oxygen data and heart rate data as a feature, train a large amount of historical data to obtain a disease prediction model, and predict the probability of a related disease based on the tester's data.
  5. 根据权利要求4所述的具备人工智能的医疗数据整合方法，其特征在于，所述数据训练方法为机器学习中的监督式学习。The medical data integration method with artificial intelligence according to claim 4, wherein the data training method is supervised learning in machine learning.
  6. 根据权利要求5所述的具备人工智能的医疗数据整合方法，其特征在于，新产生的数据和检测结果会继续更新监督式学习算法中的权重。The medical data integration method with artificial intelligence according to claim 5, wherein the newly generated data and detection results will continue to update the weights in the supervised learning algorithm.
PCT/CN2020/091445 2019-07-10 2020-05-21 Artificial intelligence-type medical data integration system and method WO2021004168A1 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201910619026.7 2019-07-10
CN201910619026.7A CN110428900A (en) 2019-07-10 2019-07-10 A kind of medical data integration system having artificial intelligence and method

Publications (1)

Publication Number Publication Date
WO2021004168A1 true WO2021004168A1 (en) 2021-01-14

Family

ID=68409137

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2020/091445 WO2021004168A1 (en) 2019-07-10 2020-05-21 Artificial intelligence-type medical data integration system and method

Country Status (2)

Country Link
CN (1) CN110428900A (en)
WO (1) WO2021004168A1 (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112102952A (en) * 2020-08-24 2020-12-18 广州金域医学检验中心有限公司 Method for identifying pathological category based on distance calculation method and related device

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110428900A (en) * 2019-07-10 2019-11-08 江苏博子岛智能科技有限公司 A kind of medical data integration system having artificial intelligence and method
CN111415449A (en) * 2020-03-17 2020-07-14 北京声智科技有限公司 Registration method, electronic equipment, server and registration system
US20220223266A1 (en) * 2021-01-12 2022-07-14 Ways Investments, LLC System, Method, and Apparatus for Staff or Resource Deployment

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160135755A1 (en) * 2012-10-24 2016-05-19 Zte Corporation Remote home healthcare system
CN105912870A (en) * 2016-04-21 2016-08-31 张明飞 Portable multi-sensor monitoring medical system
CN105956372A (en) * 2016-04-21 2016-09-21 青岛大学 Remote multi-sensor monitoring medical system
CN106137152A (en) * 2015-04-28 2016-11-23 天创聚合科技（上海）有限公司 Remote health detection and method of servicing and system thereof
CN110428900A (en) * 2019-07-10 2019-11-08 江苏博子岛智能科技有限公司 A kind of medical data integration system having artificial intelligence and method

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105224804A (en) * 2015-10-10 2016-01-06 成都贝贝信科技有限公司 Intelligent medical comprehensive detection system
JP2019504402A (en) * 2015-12-18 2019-02-14 コグノア， インコーポレイテッド Platforms and systems for digital personalized medicine
CN109119130A (en) * 2018-07-11 2019-01-01 上海夏先机电科技发展有限公司 A kind of big data based on cloud computing is health management system arranged and method

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160135755A1 (en) * 2012-10-24 2016-05-19 Zte Corporation Remote home healthcare system
CN106137152A (en) * 2015-04-28 2016-11-23 天创聚合科技（上海）有限公司 Remote health detection and method of servicing and system thereof
CN105912870A (en) * 2016-04-21 2016-08-31 张明飞 Portable multi-sensor monitoring medical system
CN105956372A (en) * 2016-04-21 2016-09-21 青岛大学 Remote multi-sensor monitoring medical system
CN110428900A (en) * 2019-07-10 2019-11-08 江苏博子岛智能科技有限公司 A kind of medical data integration system having artificial intelligence and method

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112102952A (en) * 2020-08-24 2020-12-18 广州金域医学检验中心有限公司 Method for identifying pathological category based on distance calculation method and related device

Also Published As

Publication number Publication date
CN110428900A (en) 2019-11-08

Similar Documents

Publication Publication Date Title
WO2021004168A1 (en) Artificial intelligence-type medical data integration system and method
US9775569B2 (en) Electronic fetal monitoring applications and display
US8550996B2 (en) Medical vital sign indication tool, system and method
Yumashev et al. Electronic apps in assessing risk and monitoring of patients with arterial hypertension
US20050171451A1 (en) System and method for managing growth and development of a user
JP2019509101A (en) System and method for determining a hemodynamic instability risk score for pediatric subjects
US11769591B2 (en) Computer aided diagnosis and monitoring of heart failure patients
Takahashi et al. Validation of Omron RS8, RS6, and RS3 home blood pressure monitoring devices, in accordance with the European Society of Hypertension International Protocol revision 2010
WO2022099668A1 (en) Method and system for precise health management and risk early warning based on association between familial genetic disease and sign data
Jiménez-Puente et al. Readmission rate as an indicator of hospital performance: the case of Spain
Putri et al. Reliability and validity analysis of smartwatches use for healthcare
US20160220127A1 (en) Wellness or illness assessment system, method, and computer program product
US20210065856A1 (en) Patient management based on sensed inputs
JP2021103582A (en) Method, system, and program for making health degree positioning map and health function, and method for using the same
US20230240592A1 (en) Methods of Treating Deep and Early-Stage Pressure Induced Tissue Damage
US20220095977A1 (en) Monitoring and prediction system of diuresis for the calculation of kidney failure risk, and the method thereof
Marshall Misleading measurements: modeling the effects of blood pressure misclassification in a United States population
Saito et al. Validation of two automatic devices, Omron HEM-6232T and HEM-6181, for self-measurement of blood pressure at the wrist according to the ANSI/AAMI/ISO 81060-2: 2013 protocol and the European Society of Hypertension International Protocol revision 2010
Sen et al. Continuous hemodynamic monitoring in acute stroke: an exploratory analysis
De Lusignan et al. Measuring blood pressure at the wrist: more comfortable for patients and more convenient for doctors?
CN104305965A (en) Health management system
Kim et al. A personal health index system with IoT devices
Nascimben et al. Technical aspects and validation of custom digital algorithms for hand volumetry
KR102535494B1 (en) Method and server apparatus for providing diet and exercise management services based on biometric information of pregnant woman
Mahajan et al. Doctor's innovative clinic: An application of artificial intelligence and physiological sensors

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20836747

Country of ref document: EP

Kind code of ref document: A1

122 Ep: pct application non-entry in european phase

Ref document number: 20836747

Country of ref document: EP

Kind code of ref document: A1