Authors:
Si-Chi Chin
1
;
Kiyana Zolfaghar
1
;
Senjuti Basu Roy
1
;
Ankur Teredesai
1
and
Paul Amoroso
2
Affiliations:
1
Institute of Technology and The University of Washington - Tacoma, United States
;
2
Multicare Health System, United States
Keyword(s):
Hospital Readmission Risk Prediction, Discretization, Data Exploration.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Data Engineering
;
Data Management and Quality
;
Data Manipulation
;
Data Mining
;
Data Visualization
;
Databases and Information Systems Integration
;
Datamining
;
Decision Support Systems
;
Enterprise Information Systems
;
Health Information Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
Insightful and principled visualization techniques may successfully help complex clinical data exploration
tasks and aid in the process of knowledge discovery. In this paper, we propose a framework Divide-n-Discover
to visualize and explore clinical data effectively, and demonstrate its effectiveness in predicting readmission
risk for Congestive Heart Failure patients. Our proposed method provides clinicians a mechanism to dynamically
explore the data and to understand how a single factor may influence the risk of readmission for a given
patient. For example, our study indicates that patients between age 47 and 48 have 2.63 time higher chance
of getting readmitted to the hospital within 30 days, compared to other patients; likewise, patients with length
of stay above 13 days are 2.27 times more likely to be readmitted within 30 days. The finding suggests that
hospitals might be under pressure to discharge patients within two week while some patients may benefit from
a longer
stay. These observations may become valid hypotheses leading to further clinical investigation or
discoveries. To the best of our knowledge, this is the first ever work that proposes principled discretization
and visualization techniques in the hospital readmission risk prediction problem.
(More)