Neural Machine Translation between Herbal Prescriptions and Diseases
This work addresses the problem of automating herbal medicine translation for healthcare practitioners, but it is incremental as it applies existing neural network methods to a new domain-specific dataset.
The study applied deep learning to translate between herbal prescriptions and diseases using a large health insurance dataset, finding that prescriptions are specific to patient demographics and environmental factors, and that the model learned both syntax and semantics.
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.