Authors:
Senjuti Basu Roy
and
Si-Chi Chin
Affiliation:
The University of Washington - Tacoma, United States
Keyword(s):
Hospital Readmission Risk Prediction, Readmission Risk Management, Predictive Modeling.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Decision Support Systems
;
Enterprise Information Systems
;
Health Information Systems
;
Healthcare Management Systems
;
Pattern Recognition and Machine Learning
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
This position paper investigates the problem of 30-day readmission risk prediction and management for Congestive Heart Failure (CHF), which has been identified as one of the leading causes of hospitalization, especially for adults older than 65 years. The underlying solution is deeply related to using predictive analytics to compute the readmission risk score of a patient, and investigating respective risk management strategies for her by leveraging statistical analysis or sequence mining techniques. The outcome of this paper leads to developing a framework that suggests appropriate interventions to a patient during a hospital stay, at discharge, or post hospital-discharge period that potentially would reduce her readmission risk. The primary beneficiaries of this paper are the physicians and different
entities involved in the pipeline of health care industry, and most importantly, the patients. This paper outlines the opportunities in applying data mining techniques in readmission r
isk prediction and management, and sheds deeper light on healthcare informatics.
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