Predicting and Exploring Readmission Patterns for Heart Failure Patients
Brown Bag Lecture by Dr. Ahmed Allam | 1/30/2018 11:00AM – 12PM | 7th Floor Conference Room, Bldg 38A
Hospitalization data in the US reveals that heart failure (HF) is one of the leading primary diagnoses for hospital admissions. A significant problem with HF admissions is the readmission of these patients to hospital within 30 days of discharge. Beyond the cost burden on the healthcare system, readmissions have negative consequences affecting patients’ health status, leading to complications. However, predicting 30 days all-cause readmissions for patients hospitalized with HF is still an open problem. This talk will introduce research in progress that deals with predicting and exploring readmission patterns for HF patients, and focus on a different view of the HF readmission problem that utilizes and models the temporal information encoded in the patients’ histories for predicting their readmission outcomes. Ideas for inspecting and visualizing the learned patterns in addition to the interpretability of models’ prediction will be highlighted.
Ahmed Allam joined CEB as a Postdoctoral Fellow in November 2017. Previously, he was a Postdoctoral Associate at Yale University, working on biomedical informatics research projects in the Krauthammer Lab. He holds a PhD in Health Communication from University of Lugano (Università della Svizzera italiana), Switzerland, and Master of Science degrees in Computer Engineering from Politecnico di Milano and Politecnico di Torino, Italy.