A Machine Learning and Data Analytics Approach to Patient Risk Stratification

In this paper, we introduce a robust machine learning (ML) and data analytic solution for risk stratification of patients to promote clinical decision support systems (CDSS) that provide enhanced protection of the patient health interests. Our approach, which we have shown would minimally intrude into peoples' lives, uses predictive models to analyze patient data to identify high-risk patients early in the history of a wide range of medical conditions. Utilizing integrated structured data (e.g., lab results, vital signs) and unstructured data (e.g., clinical notes), our system improves the accuracy of medical decisions made by healthcare providers. We use a combination of supervised learning algorithms and real-time data processing to recalibrate risk scores, which serve as actionable insights for anticipatory interventions. The strategy also comes with extensive validation protocols to verify the efficacy of her model in a healthcare environment. Our results indicate that this practice dramatically increases the accuracy of risk assessments, with important implications for patient care and resource utilization.