Condition Monitoring for Evidence-based Care of Psychiatric Patients

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Last update: 01/22/2008

Objective

To optimize patient recovery in today’s world of higher drug costs and shorter hospital stays, physicians are moving towards evidence-based patient care which promotes the collection, interpretation, and integration of valid, important and applicable patient-reported, clinician-observed, and research-derived evidence to improve the quality of clinical judgments and facilitate cost-effective healthcare.  This is an admirable but elusive goal because mature supporting technologies are lacking.  The philosophy of evidence-based patient care is similar to that of condition-based maintenance (CBM) in manufacturing, which advocates the monitoring of production equipment using various sensors to enable real-time diagnosis of impending failures so the right maintenance actions can take place in a timely fashion and on an as-needed basis.  CBM research is relatively mature and the knowledge generated, namely, diagnosis/prognosis modeling (i.e., techniques for interpreting sensor data), is applicable to patient care.  The goal of this proposal is to develop a condition monitoring system to deliver high quality care to psychiatric patients in a cost-effective manner.  It focuses on psychiatric patients because of the high cost of psychotropic medication and pressing questions regarding the safety of antidepressant drugs.  The research challenges are mainly due to the nature of data acquisition in healthcare; namely, gaps (missing values) in the data and sparse data samples for an individual patient.  It was proposed to use an iterative missing data imputation technique to fill data gaps and a dimensionality reduction approach to tackle the problem of modeling with sparse data.  In addition, diagnosis/prognosis models will be built through the integration of first-principle knowledge and data mining, an approach proven to be successful in our prior CBM research.

Project Organization

Principal Investigator: Dr. Samuel H. Huang

    Co-Principal Investigator: Dr. Lawson R. Wulsin (College of Medicine)

Collaborator: Dr. Jeff J. Guo (College of Pharmacy)

Research Assistant: Dengyao Mo, Hua Li, Nuo Xu, Xing Li (College of Pharmacy)
REU Participants: James Penebaker

Acknowledgement and Disclaimer

This material is based upon work supported by the National Science Foundation under Grant No. 0555962. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.