Marlo Verket

Abstract Diagnosis of heart failure is complex but crucial for patient outcomes and often hindered by the untapped potential of unstructured clinical notes. We introduce a novel end-to-end pipeline for heart failure diagnosis, leveraging electronic health records (EHR) and German clinical notes from 846 patients. Our pipeline synthesizes abbreviation disambiguation, translation of German clinical notes to English, medical entity linking to SNOMED-CT, and subsequent classification.

Abstract Patients with coronary heart disease (CHD) and type 2 diabetes mellitus (T2DM) have a substantially increased risk for major cardiovascular events and mortality. Increasing physical activity and improving a healthy diet may effectively reduce cardiovascular risk factors; however, the effects are often transient.

Abstract Serious clinical complications (SCC; CTCAE grade ≥ 3) occur frequently in patients treated for hematological malignancies. Early diagnosis and treatment of SCC are essential to improve outcomes. Here we report a deep learning model-derived SCC-Score to detect and predict SCC from time-series data recorded continuously by a medical wearable.