Patient-Specific Readmission Prediction and Intervention for Health Care

Yan Zhang
Publication Target: 
IJPHM
Publication Issue: 
Special Issue PHM for Human Health and Performance
Submission Type: 
Technical Brief
AttachmentSizeTimestamp
ijphm_19_010.pdf407.59 KBMarch 17, 2019 - 11:59am

Hospital readmission is often associated with unfavorable patient outcomes and a large cost of resources. Therefore, preventing avoidable re-hospitalizations is imperative. To target this problem, one important metric that researchers and practitioners strive to reduce is the 30-day hospital readmission rate. In this paper, we introduce a general decision support system that utilizes machine learning (ML) based patient-specific prediction to guide the suggestion of patient intervention program assignment, with the objective of minimizing the readmission cost for hospitals. This work has three major contributions. First, the proposed solution is highly scalable by using PySpark. Second, we outline solution architecture components including (1) data injection (both real-time sensor reading and data at rest), processing, and analysis; and (2) ML model building, evaluation, deployment and scoring. Third, we discuss how the ML prediction results can be taken into account in a decision support system by presenting a rich visualization.

Publication Year: 
2019
Publication Volume: 
10
Publication Control Number: 
010
Page Count: 
5
Submission Keywords: 
machine learning
hospital readmission
decison support system
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Submitted by: 
  
 
 
 

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