Predictive Analytics for Re-Hospitalization and Disease Prediction

Abstract

Hospital readmissions can have serious consequences for both patients and healthcare providers. They can lead to increased healthcare costs, lower patient satisfaction, and negative health outcomes. When patients are readmitted to the hospital soon after being discharged, their risk of morbidity and mortality increases. Additionally, hospitals may face penalties for having too many readmissions. To address this issue, researchers are investigating the potential of using machine learning techniques to predict a patient’s risk for readmission and illness progression. A large dataset of electronic health records from a major hospital was analyzed, containing patient information on medical histories, diagnoses, treatments, and personal characteristics. Several machine learning techniques, such as logistic regression, decision trees, random forests, and neural networks, were utilized to detect important risk factors and develop predictive models for re-hospitalization and disease progression.

The performance of each machine learning model was assessed by the researchers using diverse metrics, including accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve. The study showed that machine learning techniques have the potential to improve patient outcomes by identifying high-risk individuals who may benefit from targeted interventions.

The paper presents a range of approaches to explore the factors that contribute to hospital readmissions and introduces a predictive model that employs machine learning algorithms to identify patients who are at a higher risk of readmission. The research involved patients who were admitted to a tertiary hospital, and the model’s effectiveness was assessed using several metrics. The article discusses the possible implications of the model for clinical practice and future research, emphasizing the potential of machine learning algorithms to predict hospital readmissions and enhance patient outcomes.[1]