Literature DB >> 22255812

Rough Set Theory based prognostication of life expectancy for terminally ill patients.

Eleazar Gil-Herrera1, Ali Yalcin, Athanasios Tsalatsanis, Laura E Barnes, Benjamin Djulbegovic.   

Abstract

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.

Entities:  

Mesh:

Year:  2011        PMID: 22255812     DOI: 10.1109/IEMBS.2011.6091589

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  1 in total

1.  R.ROSETTA: an interpretable machine learning framework.

Authors:  Klev Diamanti; Karolina Smolińska; Mateusz Garbulowski; Nicholas Baltzer; Patricia Stoll; Susanne Bornelöv; Aleksander Øhrn; Lars Feuk; Jan Komorowski
Journal:  BMC Bioinformatics       Date:  2021-03-06       Impact factor: 3.169

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.