Deep Learning for Medication Recommendation: A Systematic Survey

• Published in: Data intelligence Vol. 5; no. 2; pp. 303 - 354
• Main Authors: Ali, Zafar, Huang, Yi, Ullah, Irfan, Feng, Junlan, Deng, Chao, Thierry, Nimbeshaho, Khan, Asad, Jan, Asim Ullah, Shen, Xiaoli, Rui, Wu, Qi, Guilin
• Format: Journal Article
• Language: English
• Published: One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 09.05.2023; MIT Press Journals, The
• Subjects: Deep Learning; Electronic health records; Medication recommendation; Personalization; Recommendation models; Recommender systems; Systematic review
• ISSN: 2641-435X; 2641-435X
• DOI: 10.1162/dint_a_00197

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.