In Clinical pharmacology and therapeutics
In a general inpatient population, we predicted patient-specific medication orders based on structured information in the electronic health record. Data on over 3 million medication orders from an academic medical center were used to train two machine learning models: a deep learning sequence model and a logistic regression model. Both were compared to a baseline that ranked the most frequently ordered medications based on a patient's discharge hospital service and amount of time since admission. Models were trained to predict from 990 possible medications at the time of order entry. Fifty five percent of medications ordered by physicians were ranked in the sequence model's top-10 predictions (logistic model: 49%) and 75% ranked in the top-25 (logistic model: 69%). Ninety-three percent of the sequence model's top-10 prediction sets contained at least one medication that physicians ordered within the next day. These findings demonstrate that medication orders can be predicted from information present in the electronic health record.
Rough Kathryn, Dai Andrew M, Zhang Kun, Xue Yuan, Vardoulakis Laura M, Cui Claire, Butte Atul J, Howell Michael D, Rajkomar Alvin
hospital, inpatient, machine learning, medication order, model evaluation, modeling, prediction, prescribing, utilization