Machine Learning for a Heart Failure Electronic Health Care Predictive Analytics System - Faraz Ahmed

Project: Research project

Project Details


Heart failure is a morbid condition affecting a large population of patients. Northwestern Medicine, like other
health systems, has taken a reactive approach to the management of heart failure patients by focusing on
reducing heart failure readmissions. Proactive strategies have become increasingly important as new payment
models are adopted. Health care electronic predictive analytics systems represent a novel approach to heart
failure prevention. By utilizing the massive repository of electronic data at Northwestern and machine learning,
a computer science method that learns from data, models can be derived that predict outcomes. In this
proposal, we aim to develop an electronic health care predictive analytics system to inform a proactive
approach to heart failure management. Using data from the unique and robust Northwestern Medicine
Enterprise Data Warehouse, we propose to develop, validate, implement, and evaluate an electronic heath
care predictive analytics system for incident heart failure hospitalization. To date, there are no systems yet
developed that anticipate the initial hospitalization for heart failure. Our work will be among the first of its kind.
These results will provide the preliminary data and infrastructure for the development of a larger system of
comprehensive care for heart failure patients focused on the promotion of health.
Effective start/end date9/1/168/31/18


  • Northwestern Memorial Hospital (Agreement 6/13/16 (Exhibit B.2))


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