Deep Learning for Medication Recommendation:A Systematic Survey

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.

Reliable Medical Recommendation Based on Privacy-Preserving Collaborative Filtering

Collaborative filtering(CF)methods are widely adopted by existing medical recommendation systems,which can help clinicians perform their work by seeking and recommending appropriate medical advice.However,privacy issue arises in this process as sensitive patient private data are collected by the recommendation server.Recently proposed privacy-preserving collaborative filtering methods,using computation-intensive cryptography techniques or data perturbation techniques are not appropriate in medical online service.The aim of this study is to address the privacy issues in the context of neighborhoodbased CF methods by proposing a Privacy Preserving Medical Recommendation(PPMR)algorithm,which can protect patients’treatment information and demographic information during online recommendation process without compromising recommendation accuracy and efficiency.The proposed algorithm includes two privacy preserving operations:Private Neighbor Selection and Neighborhood-based Differential Privacy Recommendation.Private Neighbor Selection is conducted on the basis of the notion of k-anonymity method,meaning that neighbors are privately selected for the target user according to his/her similarities with others.Neighborhood-based Differential Privacy Recommendation and a differential privacy mechanism are introduced in this operation to enhance the performance of recommendation.Our algorithm is evaluated using the real-world hospital EMRs dataset.Experimental results demonstrate that the proposed method achieves stable recommendation accuracy while providing comprehensive privacy for individual patients.