Risk Stratification for Hospital Readmission of Heart Failure Patients: A Machine Learning Approach

被引：5

作者：

Hon, Chun Pan ^{[1
]}

Pereira, Mayana ^{[1
]}

Sushmita, Shanu ^{[1
]}

Teredesai, Ankur ^{[1
]}

De Cock, Martine ^{[1
]}

机构：

[1] Univ Washington, Inst Technol, Ctr Data Sci, Tacoma, WA 98402 USA

来源：

PROCEEDINGS OF THE 7TH ACM INTERNATIONAL CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY, AND HEALTH INFORMATICS | 2016年

关键词：

D O I：

10.1145/2975167.2985648

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Being able to stratify patients according to 30-day hospital readmission risk, anticipated length and cost of stay can guide clinicians in discharge planning and intervention recommendation, leading to an increase of quality of care, and a decrease of healthcare cost. We present a comparative performance analysis of decision trees, boosted decision trees and logistic regression models that can flag, at the time of discharge, patients with an anticipated early, lengthy and expensive readmission. We validate our models using discharge records of 500K congestive heart failure patients from California-licensed hospitals.

引用

页码：491 / 492

页数：2

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