Automating Disease Management Using Answer Set Programming: Heart Failure
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Management of chronic diseases such as heart failure (HF), diabetes and chronic obstructive pulmonary disease is a major health care problem. A standard approach that the medical community has devised to manage widely prevalent chronic diseases such as heart failure is to have a committee of experts develop guidelines that all physicians should follow. These guidelines typically consist of a series of complex rules that make recommendations based on a patient's information. Due to their complexity, often the guidelines are either ignored or not complied with at all, which can result in poor medical practices. It is not even clear whether it is humanly possible to follow these clinical guidelines due to their length and complexity. In the case of heart failure management, the guidelines run nearly 80 pages. In this dissertation we describe a physician advisory system that codes the entire set of clinical practice guidelines for heart failure management using answer set programming (ASP). ASP is a form of declarative programming geared toward solving NP-hard search problems. Our approach is based on developing reasoning templates that we call knowledge patterns and using these patterns to systematically code the clinical guideline for HF management as ASP rules. Use of the knowledge patterns greatly facilitates the development of the physician advisory system. Given a patient's medical information, the system generates a set of guideline-compliant recommendations just as a human physician would. The system works even in the presence of incomplete information. Abductive reasoning is implemented in the system to find missing symptoms and conditions that the patient must exhibit in order for a treatment prescribed by a physician to work effectively. The physician advisory system is validated by using data of representative patients with heart failure.