icuARM – An ICU Clinical Decision Support System Using Association Rule Mining

November 21, 2013

icuARM – An ICU Clinical Decision Support System Using Association Rule Mining

The rapid development of biomedical monitoring technologies has enabled modern intensive care units (ICUs) to gather vast amounts of multimodal measurement data about their patients. However, processing large volumes of complex data in real-time has become a big challenge. Together with ICU physicians, we have designed and developed an ICU clinical decision support system based on associate rule mining (ARM) and a publicly available research database MIMIC-II (Multi-parameter Intelligent Monitoring in Intensive Care II).  MIMIC-II contains more than 40,000 ICU records for 30,000+ patients. icuARM is constructed with multiple association rules and an easy-to-use graphical user interface (GUI) for users to perform real-time data and information mining in the ICU setting. To validate icuARM, we have investigated the associations between patients’ conditions such as comorbidities, demographics, and medications and their ICU outcomes such as ICU length of stay. Coagulopathy surfaced as the most dangerous co-morbidity that leads to the highest possibility (54.1%) of prolonged ICU stay, and women who are older than 50 year have the highest possibility (38.8%) of prolonged ICU stay. For clinical conditions treatable with multiple drugs, icuARM suggests that medication choice can be optimized based on patient-specific characteristics. Overall, icuARM can provide valuable insights for ICU physicians to tailor a patient’s treatment based on his/her clinical status in real time.

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Authors

See complete bios of the authors in the full version of this article.

CW ChengCW Cheng
Mr. Cheng received a B.S. in Electrical Engineering from Chung Cheng University, Chia-Yi, Taiwan and a M.S. degree in electrical and computer engineering from Georgia Institute of Technology, Atlanta. He is currently pursuing a Ph.D. at the School of Electrical and Computer Engineering.

N ChananiN Chanani
Mr. Chanani is an Assistant Professor of Pediatrics at Emory University School of Medicine and an attending physician in the Cardiac Intensive Care Unit at Children’s Healthcare of Atlanta. His research interests include quality and device improvement for patients with congenital heart disease.

J VenugopalanJ Venugopalan
Ms. Venugopalan has worked as a systems design engineer in Perfint Healthcare, and interned in Christian Medical College and All India Institute of Medical Sciences. She is currently pursuing a Ph.D. in biomedical engineering at Georgia Institute of Technology, Atlanta, Georgia. Her research focuses on mobile health and health informatics.

K MaherK Maher
Mr. Maher is an associate professor of Pediatrics at Emory University Schoolf of Medicine. His research interests are varied and include neonatal resuscitation, biomarkers in pediatric heart disease, and the application of advanced technologies to pediatric disease.

MD WangMD Wang
Dr. Wang is currently a tenured associate professor in the joint Wallace H. Coulter Department of Biomedical Engineering, School of Electrical and Computer Engineering, Department of Hematology and Oncology, the Winship Cancer Institute, The Parker H. Petit Institute for Bioengineering and Biosciences, and The Institute for People and Technology at Georgia Institute of Technology and Emory University, Atlanta, USA.

Editorial Comments

Real-time monitoring/evaluation and decision support systems aids are important and relevant to intensive care. The authors describe a tool that monitors real-time patient parameters, co-morbidities, and demographics, and utilizes a database of past patient encounters and evidence based medicine rules to create recommendations for patient care. These tools have a high level of clinical significance and use, especially in the light of widespread growth of electronic health records. The paper presents an innovative decision tool, ICU-ARM tool with a systemic approach to allow real time predictions of ICU stay and decision assistance in patient management while in the ICU.

JTEHM 2013Issue

This article appeared in the 2013 issue of IEEE Journal of Translational Engineering in Health and Medicine.
View all articles on IEEE Xplore

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