Using Temporal Data Mining on Patient Data for Clinical Decision Making in the Care of the Sick Newborn

In a neonatal intensive care unit, streaming healthcare data comes from many sources, but humans are unable to understand relationships between data variables. Data mining and analysis are just beginning to get utilized in critical care. We present a case study using electronic medical record data in the neonatal intensive care unit and explore possible avenues of advancement using temporal data analytics.

Electronic medical record data were collected for physiological monitor data. Heart rate, respiratory rate, oxygen saturation and temperature data were retrospectively analyzed by temporal data mining. Three premature babies were selected and data de-identified. The first case of a urinary tract infection showed nursing ability to synthesize data streams coming from a patient. For the second case of necrotizing enterocolitis, Temporal-Data-Mining analysis of combinations of clinical events based on deviations from the mean showed specific heuristic biomarkers related to events before discovery of necrotizing enterocolitis. Specific sequences 6-event and 5-event in length were identified with nursing unease at clinical deterioration, which were 100- and 87-times unlikely to occur randomly with 99.5% confidence. No such sequences were found in the rest of the 37 days for the second case and entire 133 days of stay in the third case of an uneventful premature baby.

Temporal data mining is a possible clinical tool in providing useful information in the neonatal intensive care unit for diagnosis of adverse clinical occurrences such as necrotizing enterocolitis. There is the possibility of changing the clinical paradigm of episodic watchfulness to constant vigilance using real-time data gathering.