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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icuARM – An ICU Clinical Decision Support System Using Association Rule Mining https://www.embs.org/jtehm/wp-content/uploads/sites/17/2013/11/icuarm.jpg 540 295 IEEE Journal of Translational Engineering in Health and Medicine (JTEHM) //www.embs.org/jtehm/wp-content/uploads/sites/17/2022/06/ieee-jtehm-logo2x.png