Hybrid approach combining deep learning and a rule based expert system for concept extraction from prescriptions

Concept extraction from prescriptions is a very important task that provides a foundation for many of the downstream healthcare applications in decision making across the areas of pharmacovigilance, medication adherence, inventory management, and other matters of value-based care. Although short, these directions can sometimes be complex. With the increase in complexity of direction, it becomes harder to extract various concepts by only rule based expert system. It identifies major concepts like frequency, dosage, duration, etc. from the natural text direction using a combination of rules and deep learning (DL) based methods on a large real world data of a pharmacy chain. The DL module includes a fine-tuned BERT transformer and Gram CNN (Convolutional Neural Network) based NER (Named Entity Recognition) architecture. The proposed method utilizes the domain heuristics along with intelligent labelling and bootstrapping to help DL models extract concepts with high evaluation scores and thus provides a way for carrying out concept extraction using targeted methods instead of one single method. To the best of our knowledge, this is the best performance reported in the literature for concept extraction from doctor's prescription.