Predicting patient’s hospital charges using machine learning

Dolley Shukla, Preeti Chandrakar
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Abstract

As the health care system moves toward value-based care, CMS (Clinical Management System) has designed a number of programs to improve the quality of patient care. One of these programs is called the Hospital Patient Admission Cost Analysis Program, which helps the patient and the hospital to diagnose the disease and estimate the cost of hospitalization. According to the World Health Organization (WHO), personal and medical costs have skyrocketed faster than the global economy. Major attributes which cause increase in expenditure include smoking, ageing and increased BMI (Body Mass Index). In this study, we aim to find a correlation between medical costs and various items using insurance data of different people with characteristics such as smoking, age, number of children, region and BMI. This study can also be used to demonstrate different models of regression that can be used to forecast insurance costs. Machine learning can significantly reduce human efforts because machine learning models can compute cost calculations in short time, for which human being takes long time to perform the same task.
利用机器学习预测病人的住院费用
随着医疗保健系统向以价值为基础的护理方向发展,CMS(临床管理系统)设计了许多项目来提高患者护理的质量。其中一个程序被称为医院病人入院成本分析程序,它可以帮助病人和医院诊断疾病并估计住院费用。根据世界卫生组织(WHO)的数据,个人和医疗费用的飙升速度超过了全球经济的增长速度。导致开支增加的主要因素包括吸烟、老化和身体质量指数上升。在本研究中,我们旨在利用吸烟、年龄、子女数量、地区和BMI等不同特征的不同人群的保险数据,寻找医疗费用与各项目之间的相关性。本研究也可以用来证明不同的回归模型,可用于预测保险费用。机器学习可以大大减少人类的工作量,因为机器学习模型可以在短时间内计算成本计算,而人类需要很长时间才能完成同样的任务。
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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