Teaching Analytics Medical-Data Common Sense

The availability of Electronic Medical Records (EMR) has spawned the development of analytics designed to assist caregivers in monitoring, diagnosis, and treatment of patients. The long-term adoption of these tools hinges upon caregivers’ confidence in them, and subsequently, their robustness to data anomalies. Unfortunately, both complex machine-learning-based tools, which require copious amounts of data to train, and a simple trend graph presented in a patient-centered dashboard, may be sensitive to noisy data. While a caregiver would dismiss a heart rate of 2000, a medical analytic relying on it may fail or mislead its users. Developers should endow their systems with medical-data common sense to shield them from improbable values. To effectively do so, they require the ability to identify them. We motivate the need to teach analytics common sense by evaluating how anomalies impact visual-analytics, score-based sepsis-analytics SOFA and qSOFA, and a machine-learning-based sepsis predictor. We then describe the anomalous patterns designers should look for in medical data using a popular public medical research database - MIMIC-III. For each data type, we highlight methods to find these patterns. For numerical data, statistical methods are limited to high-throughput scenarios and large aggregations. Since deployed analytics monitor a single patient and must rely on a limited amount of data, rule-based methods are needed. In light of the dearth of medical guidelines to support such systems, we outline the dimensions upon which they should be defined upon.