Smart Summary: A Distributed Medical Recommender System for Patients in the ICU Using Neural Networks

• Published in: IEEE access Vol. 12; pp. 83719 - 83732
• Main Authors: Ayad, Ahmad, Tai, Yu-Hsuan, Dartmann, Guido, Schmeink, Anke
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Abnormalities; Artificial intelligence; Codes; Diseases; Distributed machine learning; Drugs; Feedback; green AI; Hospitals; ICU; Machine learning; Medical diagnostic imaging; medical informatics; Medical services; Neural networks; Patients; Physicians; Privacy; Recommender systems; Security; split learning
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3414184

In the medical domain, particularly in intensive care units (ICUs), the immense volume of patient data presents a significant challenge for clinicians, often resulting in the oversight of critical information or excessive time consumption in accessing it. Recommender systems have been introduced to facilitate targeted, data-driven decision-making and ease the burden on healthcare professionals. This paper introduces Smart Summary, a novel distributed medical recommender system aimed at streamlining the analysis of extensive patient data and improving diagnostic accuracy by focusing on essential information. Smart Summary leverages patients' admission reports and past lab values to predict International Classification of Diseases (ICD) codes, extract disease names, and forecast future abnormalities in lab values. Using this information, Smart Summary builds a comprehensive patient profile that covers the patient's case precisely. Additionally, it recommends the most relevant laboratory values for individual patients by analyzing their data through various modules, including lab values abnormality prediction, automatic ICD codes prediction, and disease-named entity recognition. Furthermore, Smart Summary enhances its performance by incorporating doctors' feedback, utilizing this information to refine recommendations for patients with similar profiles within the same cluster. Experimental results demonstrate that Smart Summary effectively learns to recommend relevant lab values for patients, achieving a Precision@10 of 0.92 after training on doctors' feedback. Moreover, Smart Summary employs an efficient distributed machine learning method based on a split learning mechanism to ensure patient data privacy. This mechanism not only guarantees data privacy and security but also reduces communication overhead by 72% and computation overhead by 45.2% compared to the original split learning mechanism. To our knowledge, Smart Summary is the only system that creates comprehensive patient profiles using multiple machine learning models and recommends relevant lab values while ensuring privacy, efficiency, and security across various data sources.