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

Zafar Ali

Yi Huang

Irfan Ullah

Junlan Feng

Chao Deng

Nimbeshaho Thierry

Asad Khan

Asim Ullah Jan

Xiaoli Shen

Wu Rui

Guilin Qi

Data Intelligence

Deep Learning for Medication Recommendation: A Systematic Survey

药物推荐中的深度学习：系统性综述

Category	Quartile
计算机科学	3
计算机科学, 计算机信息系统	3

Deep Learning for Medication Recommendation: A Systematic Survey

ivySCI AI Smartly Parses PDF, Answers Researchers' Questions, and Helps You Understand Papers in Seconds

Journal Info

Category	Quartile
COMPUTER SCIENCE, INFORMATION SYSTEMS	3