  The current study applies deep learning to herbalism. Toward the goal, we
acquired the de-identified health insurance reimbursements that were claimed in
a 10-year period from 2004 to 2013 in the National Health Insurance Database of
Taiwan, the total number of reimbursement records equaling 340 millions. Two
artificial intelligence techniques were applied to the dataset: residual
convolutional neural network multitask classifier and attention-based recurrent
neural network. The former works to translate from herbal prescriptions to
diseases; and the latter from diseases to herbal prescriptions. Analysis of the
classification results indicates that herbal prescriptions are specific to:
anatomy, pathophysiology, sex and age of the patient, and season and year of
the prescription. Further analysis identifies temperature and gross domestic
product as the meteorological and socioeconomic factors that are associated
with herbal prescriptions. Analysis of the neural machine transitional result
indicates that the recurrent neural network learnt not only syntax but also
semantics of diseases and herbal prescriptions.
