The Role of Machine Learning in Predicting Patient Outcomes and Hospital Readmissions

With an aging population, ascendent prevalence of chronic disease and rising therapy costs, the demands on global health care systems have reached new levels, calling for new solutions to improve patients’ care and health care delivery efficiency. Thus, in a clinical context, Machine Learning (ML) is a rapidly evolving subbranch of Artificial Intelligence (AI) which can provide a transformational potential to automate the data-intensive decision making. Vast and complicated datasets spawned from electronic health records (EHRs), laboratory results, diagnostic imaging, patient histories and other sources can be analysed by ML algorithms to find patterns that humans cannot. Moreover, these predictive capabilities come into play when it comes to predicting patient outcome or patients at high risk of readmission so that suitable interventions can be taken place and healthcare costs can be claimed. This paper systematically studies the application of ML in predicting clinical outcomes and readmissions through a comparative analysis of different ML model: such as logistic regression, decision trees, ensemble, and different deep learning architectures. We evaluate the performance, accuracy, and practical utility of these models in hospital settings by leveraging real world datasets. We also discuss broader ML adoption related to healthcare, including model interpretability and integration issues and ethics. We show that ML has the unique potential to drive precision medicine and improve the entire healthcare delivery.