Community Detection in Medicine: Preserved Ejection Fraction Heart Failure (HFpEF)

Abstract

The COVID-19 pandemic raised awareness of the complexities of the patient, the disease, and the practice of medicine. The impact of these reaches beyond healthcare (e.g., supply chains, politics, socioeconomic factors) to include nations, individuals, and molecules. In personalized medicine, “accurate diagnosis” is critical as it affects patient management, clinical trial recruitment, regulatory approval, and reimbursement policies for payers. Conventional statistics evaluate hypothesis-driven reductionist practices in medicine, e.g., the use of “scores” combining individual measurements, and are often limited by the data:variables ratio. True personalization (N of 1) is not practical but better stratification of diseases and patients can improve diagnoses. This work describes our approach and tests its ability to identify patient complexity and clinical markers in the trial of a candidate HFpEF drug better than prior methods.