TY - JOUR AU - Radhachandran, Ashwath AU - Garikipati, Anurag AU - Zelin, Nicole S. AU - Pellegrini, Emily AU - Ghandian, Sina AU - Calvert, Jacob AU - Hoffman, Jana AU - Mao, Qingqing AU - Das, Ritankar PY - 2021 DA - 2021/03/31 TI - Prediction of short-term mortality in acute heart failure patients using minimal electronic health record data JO - BioData Mining SP - 23 VL - 14 IS - 1 AB - Acute heart failure (AHF) is associated with significant morbidity and mortality. Effective patient risk stratification is essential to guiding hospitalization decisions and the clinical management of AHF. Clinical decision support systems can be used to improve predictions of mortality made in emergency care settings for the purpose of AHF risk stratification. In this study, several models for the prediction of seven-day mortality among AHF patients were developed by applying machine learning techniques to retrospective patient data from 236,275 total emergency department (ED) encounters, 1881 of which were considered positive for AHF and were used for model training and testing. The models used varying subsets of age, sex, vital signs, and laboratory values. Model performance was compared to the Emergency Heart Failure Mortality Risk Grade (EHMRG) model, a commonly used system for prediction of seven-day mortality in the ED with similar (or, in some cases, more extensive) inputs. Model performance was assessed in terms of area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. SN - 1756-0381 UR - https://doi.org/10.1186/s13040-021-00255-w DO - 10.1186/s13040-021-00255-w ID - Radhachandran2021 ER -