RISK MANAGEMENT ADVANCEMENT IN HEALTHCARE USING MACHINE LEARNING AS AN ALTERNATIVE ANALYSIS TOOL– A REVIEW

This paper will examine, analyze, synthesize, and interpret the peer-reviewed paper titled “Machine Learning for Health Services Researchers” published by Patrick Doupe, James Faghmous, and Sanjay Bansu. First, it will explore what it is (The study of computer algorithms that can improve themselves spontaneously through experience and data use). Then the usage of Machine Learning in the Health Care industry and how it is developing. After that to provide a high-level overview of machine learning for healthcare outcomes researchers and decision makers. The paper will provide the methods that they provide to prove this objective. These methods are to first give an insightful explanation on what machine learning is. Then to point out the process for the estimator, which is the algorithm that is developed in machine learning that helps to predict an outcome. Finally, the final technique compares three of the most often used machine learning methods: decision tree method, deep learning method, and ensemble method. Following that, the paper will explain and point out some of the problems in their conclusions were. For example, how many challenges may be formulated when using Machine Learning Algorithms, because machine learning models rely on highquality data that is representative of the population to whom the model's outputs are to be generalized.