Patient care classification using machine learning techniques

Shatha Melhem, Ahmad Al-Aiad, M. Al-Ayyad
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引用次数: 1

Abstract

Doctors and Specialists use the lab test results of patients to classify their medical needed care into inpatient care or outpatient care, which is a time-consuming process and needs a lot of efforts from doctors to decide whether the patient needs to be in the hospital and monitored or not. In addition, the likelihood of making the wrong decision is high, thus it may endanger the patient’s life. the purpose of this study is to utilize machine learning to classify patient care into inpatient or outpatient, in order to reduce the efforts and time expanded by the doctors which reflect on the type of services provided to the patient, also this kind of studies can help in reducing the human errors that result in risks to the patient’s life and may increase the total bill of patients which led to pay significant amounts. machine-learning was utilized to build four models: Support Vector Machine, Decision Tree, Random Forest, and K-Nearest Neighbors (KNN), that could predict whether the patient should be classified as inpatient or outpatient-based on their conditions and lab test results. The best model has been chosen based on the highest accuracy, sensitivity, specificity, and precision score, and the lowest false-negative rate, and false-positive rate. (EHR) the dataset has been used which consists of patients’ laboratory test results from a private hospital in Indonesia to build these models and test them. The results show that Random Forest achieved the highest accuracy (77%), Sensitivity (65%), and Precision (72%), respectively, the model also has the lowest false-negative rate (35%), and almost the lowest false positive rate (16%).
使用机器学习技术的病人护理分类
医生和专科医生根据患者的实验室检测结果,将其医疗需要的护理分为住院治疗或门诊治疗,这是一个耗时的过程,需要医生做出大量的努力来决定患者是否需要住院和监测。此外,做出错误决定的可能性很高,从而可能危及患者的生命。本研究的目的是利用机器学习将患者护理分为住院或门诊,以减少医生对提供给患者的服务类型进行反思所花费的精力和时间,并且这种研究可以帮助减少导致患者生命风险的人为错误,并可能增加患者的总账单,从而导致支付大量费用。利用机器学习建立了四个模型:支持向量机、决策树、随机森林和k近邻(KNN),可以根据患者的病情和实验室测试结果预测患者是应该被分类为住院患者还是门诊患者。以最高的准确率、灵敏度、特异度、精密度评分和最低的假阴性率、假阳性率为标准选择最佳模型。(EHR)数据集由印度尼西亚一家私立医院的患者实验室测试结果组成,用于建立这些模型并对其进行测试。结果表明,随机森林模型的准确率最高(77%),灵敏度最高(65%),精度最高(72%),假阴性率最低(35%),假阳性率几乎最低(16%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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