Learning Doctors' Medicine Prescription Pattern for Chronic Disease Treatment by Mining Electronic Health Records: A Multi-Task Learning Approach
Increasing learning ability from massive medical data and building learning methods robust to data quality issues are key factors toward building data-driven clinical decision support systems for medicine prescription decision support. Here, we attempted accordingly to address the factors using a multi-task neural network approach, benefiting from multi-task learning's advantage in modeling commonalities to increase learning performance and neural network's robustness to imprecise data. By mining electronic health record data, we learned medicine prescription patterns of multiple correlated antidiabetic agents in blood glucose control and antihypertensive drugs in blood pressure control scenarios. We achieved AUC increases of 0.02 to 0.06 in single drug prescription and an accuracy increase of 0.05 in prescription pattern prediction compared to logistic regression, demonstrating the efficacy of multi-task neural network approach in learning medicine prescription patterns.