{"title":"基于机器学习的健康预测系统,使用IBM云作为PaaS","authors":"A. A. Neloy, S. Alam, R. A. Bindu, N. J. Moni","doi":"10.1109/ICOEI.2019.8862754","DOIUrl":null,"url":null,"abstract":"Adaptable Critical Patient Caring system is a key concern for hospitals in developing countries like Bangladesh. Most of the hospital in Bangladesh lack serving proper health service due to unavailability of appropriate, easy and scalable smart systems. The aim of this project is to build an adequate system for hospitals to serve critical patients with a real-time feedback method. In this paper, we propose a generic architecture, associated terminology and a classificatory model for observing critical patient's health condition with machine learning and IBM cloud computing as Platform as a service (PaaS). Machine Learning (ML) based health prediction of the patients is the key concept of this research. IBM Cloud, IBM Watson studio is the platform for this research to store and maintain our data and ml models. For our ml models, we have chosen the following Base Predictors: Naïve Bayes, Logistic Regression, KNeighbors Classifier, Decision Tree Classifier, Random Forest Classifier, Gradient Boosting Classifier, and MLP Classifier. For improving the accuracy of the model, the bagging method of ensemble learning has been used. The following algorithms are used for ensemble learning: Bagging Random Forest, Bagging Extra Trees, Bagging KNeighbors, Bagging SVC, and Bagging Ridge. We have developed a mobile application named “Critical Patient Management System - CPMS” for real-time data and information view. The system architecture is designed in such a way that the ml models can train and deploy in a real-time interval by retrieving the data from IBM Cloud and the cloud information can also be accessed through CPMS in a requested time interval. To help the doctors, the ml models will predict the condition of a patient. If the prediction based on the condition gets worse, the CPMS will send an SMS to the duty doctor and nurse for getting immediate attention to the patient. Combining with the ml models and mobile application, the project may serve as a smart healthcare solution for the hospitals.","PeriodicalId":212501,"journal":{"name":"2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"Machine Learning based Health Prediction System using IBM Cloud as PaaS\",\"authors\":\"A. A. Neloy, S. Alam, R. A. Bindu, N. J. Moni\",\"doi\":\"10.1109/ICOEI.2019.8862754\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Adaptable Critical Patient Caring system is a key concern for hospitals in developing countries like Bangladesh. Most of the hospital in Bangladesh lack serving proper health service due to unavailability of appropriate, easy and scalable smart systems. The aim of this project is to build an adequate system for hospitals to serve critical patients with a real-time feedback method. In this paper, we propose a generic architecture, associated terminology and a classificatory model for observing critical patient's health condition with machine learning and IBM cloud computing as Platform as a service (PaaS). Machine Learning (ML) based health prediction of the patients is the key concept of this research. IBM Cloud, IBM Watson studio is the platform for this research to store and maintain our data and ml models. For our ml models, we have chosen the following Base Predictors: Naïve Bayes, Logistic Regression, KNeighbors Classifier, Decision Tree Classifier, Random Forest Classifier, Gradient Boosting Classifier, and MLP Classifier. For improving the accuracy of the model, the bagging method of ensemble learning has been used. The following algorithms are used for ensemble learning: Bagging Random Forest, Bagging Extra Trees, Bagging KNeighbors, Bagging SVC, and Bagging Ridge. We have developed a mobile application named “Critical Patient Management System - CPMS” for real-time data and information view. The system architecture is designed in such a way that the ml models can train and deploy in a real-time interval by retrieving the data from IBM Cloud and the cloud information can also be accessed through CPMS in a requested time interval. To help the doctors, the ml models will predict the condition of a patient. If the prediction based on the condition gets worse, the CPMS will send an SMS to the duty doctor and nurse for getting immediate attention to the patient. 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引用次数: 15
摘要
适应性强的重症病人护理系统是孟加拉国等发展中国家医院关注的一个关键问题。由于没有适当、简便和可扩展的智能系统,孟加拉国的大多数医院缺乏适当的卫生服务。该项目的目的是为医院建立一个适当的系统,以实时反馈的方式为危重患者服务。在本文中,我们提出了一个通用架构、相关术语和分类模型,用于使用机器学习和IBM云计算即平台即服务(PaaS)来观察危重患者的健康状况。基于机器学习的患者健康预测是本研究的关键概念。IBM Cloud、IBM Watson studio是本研究存储和维护数据和ml模型的平台。对于我们的ml模型,我们选择了以下基本预测器:Naïve贝叶斯,逻辑回归,KNeighbors分类器,决策树分类器,随机森林分类器,梯度增强分类器和MLP分类器。为了提高模型的准确性,采用了集成学习的bagging方法。以下算法用于集成学习:Bagging Random Forest, Bagging Extra Trees, Bagging KNeighbors, Bagging SVC和Bagging Ridge。我们开发了一款名为“重症患者管理系统- CPMS”的移动应用程序,用于实时数据和信息查看。系统架构的设计使得ml模型可以通过从IBM Cloud检索数据来进行实时训练和部署,并且还可以在请求的时间间隔内通过CPMS访问云信息。为了帮助医生,机器学习模型将预测病人的病情。如果基于病情的预测变得更糟,CPMS会向值班医生和护士发送短信,让他们立即关注患者。结合ml模型和移动应用程序,该项目可作为医院的智能医疗解决方案。
Machine Learning based Health Prediction System using IBM Cloud as PaaS
Adaptable Critical Patient Caring system is a key concern for hospitals in developing countries like Bangladesh. Most of the hospital in Bangladesh lack serving proper health service due to unavailability of appropriate, easy and scalable smart systems. The aim of this project is to build an adequate system for hospitals to serve critical patients with a real-time feedback method. In this paper, we propose a generic architecture, associated terminology and a classificatory model for observing critical patient's health condition with machine learning and IBM cloud computing as Platform as a service (PaaS). Machine Learning (ML) based health prediction of the patients is the key concept of this research. IBM Cloud, IBM Watson studio is the platform for this research to store and maintain our data and ml models. For our ml models, we have chosen the following Base Predictors: Naïve Bayes, Logistic Regression, KNeighbors Classifier, Decision Tree Classifier, Random Forest Classifier, Gradient Boosting Classifier, and MLP Classifier. For improving the accuracy of the model, the bagging method of ensemble learning has been used. The following algorithms are used for ensemble learning: Bagging Random Forest, Bagging Extra Trees, Bagging KNeighbors, Bagging SVC, and Bagging Ridge. We have developed a mobile application named “Critical Patient Management System - CPMS” for real-time data and information view. The system architecture is designed in such a way that the ml models can train and deploy in a real-time interval by retrieving the data from IBM Cloud and the cloud information can also be accessed through CPMS in a requested time interval. To help the doctors, the ml models will predict the condition of a patient. If the prediction based on the condition gets worse, the CPMS will send an SMS to the duty doctor and nurse for getting immediate attention to the patient. Combining with the ml models and mobile application, the project may serve as a smart healthcare solution for the hospitals.