Deep Learning for Medication Recommendation: A Systematic Survey

ABSTRACT Making medication prescriptions in response to the patient's diagnosis is a challenging task. The number of pharmaceutical companies, their inventory medicines, and recommended dosage confront doctor with well-known problem information cognitive overload. To assist medical practitioner making informed decisions regarding prescription patient, researchers have exploited electronic health records (EHRs) automatically recommending medication. In recent years, recommendation using EHRs has been salient research direction, which attracted apply various deep learning (DL) models patients prescriptions. Yet, absence holistic survey article, it needs lot effort time study these publications order understand current state identify best-performing along trends challenges. fill this gap, reports on state-of-the-art DL-based methods. It reviews classification (MR) models, compares performance, unavoidable issues they face. most common datasets metrics used evaluating MR models. findings implications for interested