Recommendation system of scientific articles from discharge summaries

Abstract

Medical professionals are often overwhelmed by the amount of patients they have to care for, leaving little time available to keep up to date in their respective specialities. They usually find it challenging to keep up with the vast amount of medical literature and identify the most relevant articles for their practice, especially those related to their patient’s specific conditions. Therefore, a system that proactively supports healthcare professionals in selecting relevant articles related to the characteristics of the patients is crucial. This paper presents Medical Expert Linguist for Evaluating Nosology and Diagnosis Information (MELENDI) to tackle this issue. It is a recommendation system that effectively and efficiently recommends pertinent medical articles to healthcare professionals based on their patients’ diagnoses. It combines a semantic similarity model generated using the content of discharge summaries, with a relevance estimator produced by analysing scientific publications. To test the system, abstracts were obtained from PubMed and 10 discharge reports from ’Medical Information Mart for Intensive Care (MIMIC-III) were used. A group of 5 medical specialists has been involved in the system’s evaluation. These evaluations demonstrated good overall performance, supporting the implementation of the system in a real-world environment, such as a hospital information system