Data-Driven Stratification and Prediction of Complex Diseases

Many common diseases such as cancer, chronic heart failure, and diabetes exhibit substantial biological and clinical heterogeneity, which complicates diagnosis, risk assessment, and treatment decisions. In this talk, I introduce a data-driven framework for disease stratification and prediction using machine learning applied to multidimensional medical data. First, unsupervised machine learning methods are used to identify previously unrecognized disease subtypes based on clinical and biomarker data. These stratification approaches reveal hidden patient groups with distinct clinical characteristics and prognoses. To enable practical application in clinical datasets, we further develop supervised learning models that reproduce and generalize unsupervised clusters, allowing robust subtype estimation even in datasets with missing variables. Next, I present approaches for early disease detection using large-scale medical history data, focusing on combinations of comorbidities as early indicators of severe diseases. Finally, I discuss how large-scale deep learning models can be leveraged to predict disease prognosis from medical images and other high-dimensional data. These studies demonstrate how machine learning can redefine disease categories and enable earlier detection and more precise prediction in heterogeneous diseases.