Conditional Outlier Approach for Detection of Unusual Patient Care Actions

Developing methods that can identify important patterns in complex large-scale temporal datasets is one of the key challenges in machine learning and data mining research. Our work focuses on the development of methods that can, based on past data, identify unusual patient-management actions in the Electronic Medical Record (EMR) of the current patient and raise alerts if such actions are encountered. We developed and evaluated a conditional-outlier detection approach for identifying clinical actions such as omissions of medication orders or laboratory orders in the intensive care unit (ICU) that are unusual with respect to past patient care. We used data from 24,658 ICU patient admissions to first learn the outlier models and then to generate 240 medication and laboratory omission alerts. The alerts were evaluated by a group of 18 intensive care physicians. The results show the true positive alert rate for all study alerts ranged from 0.42 to 0.53, which is promising and compares favorably to the positive alert rates of existing clinical alerting systems.