Award Number: W81XWH-09-2-0175 TITLE: Proposal for Development of EBM-CDSS (Evidence-Based Clinical Decision Support System) To Aid Prognostication in Terminally Ill Patients

We present a novel knowledge discovery methodology that relies on Rough Set Theory to predict the life expectancy of terminally ill patients in an effort to improve the hospice referral process. Life expectancy prognostication is particularly valuable for terminally ill patients since it enables them and their families to initiate end-of-life discussions and choose the most desired management strategy for the remainder of their lives. We utilize retrospective data from 9105 patients to demonstrate the design and implementation details of a series of classifiers developed to identify potential hospice candidates. Preliminary results confirm the efficacy of the proposed methodology. We envision our work as a part of a comprehensive decision support system designed to assist terminally ill patients in making end-of-life care decisions. INTRODUCTION ACCORDING to Medicare regulations, a patient should be referred to hospice if his/her life expectancy is less than 6 months [1]. However, despite the well-documented advantages of hospice services, terminally ill patients do not reap the maximum benefits of hospice care with the majority of them being referred to hospice either prematurely or too late. In general, premature hospice referral is translated to patients losing the opportunity to receive potentially effective treatment, which may have prolonged their lives. Conversely, late hospice referral reduces the quality of life for patients and their families. It is apparent that accurate prognostication of life expectancy is of vital importance for all parties involved in the hospice referral process (e.g. patients, their families, and their physicians). Here, we propose a novel knowledge discovery methodology developed to identify terminally ill patients with life expectancy less than 6 months. The core of the proposed methodology is Rough Set Theory [2]. The rest of this paper describes implementation details, reports results, and discusses limitations and future directions of our work. Methodology Literature Review Approaches for developing prognostic models for estimating survival for seriously ill patients range from the use of traditional statistical and probabilistic techniques [3]-[6], to models based on artificial intelligence techniques such as neural networks, decision trees and rough set methods [7]-[11]. A recent systematic review of prognostic tools for estimating survival in palliative care highlighted the lack of accurate end-of-life prognostic models [13]. Both statistics based techniques and AI based models rely on data that are precisely well defined. However, medical information, which represents patients records that include symptoms and clinical signs, is not always well defined and, therefore, the data are represented with vagueness [14]. Particularly, for this kind of information, it becomes very difficult to classify borderline cases in which very small differences in the value of a variable of interest may completely change categorization and therefore the following decisions can changes dramatically [15]. Moreover, the dataset is presented with inconsistencies in the sense that it is possible to have Manuscript received March 26, 2011. This work was supported in part by the Department of Army under grant #W81 XWH-09-20175. E. Gil-Herrera and A. Yalcin are with the Department of Industrial and Management System Engineering, University of South Florida, Tampa, FL 33620, USA(e-mail: eleazar@mail.usf.edu, ayalcin@eng.usf.edu). A. Tsalatsanis, L. E. Barnes and B. Djulbegovic are with the Center for Evidence Based Medicine and Health Outcomes Research, University of South Florida, Tampa, FL 33612, USA (e-mail: atsalats@health.usf.edu, lbarnes@health.usf.edu, bdjulbeg@health.usf.edu). Rough Set Theory based Prognostication of Life Expectancy for Terminally Ill Patients Eleazar Gil-Herrera, Ali Yalcin, Athanasios Tsalatsanis, Laura E. Barnes and Benjamin Djulbegovic