2024/10/21 by Xiang Lv, Jiesi Luo, Yonglin Zhang +5 · 4 citations
Computer Science · Health Professions · Medicine · #Artificial Intelligence in Healthcare #Artificial intelligence #Computer science #Diabetes mellitus #Endocrinology #Explainable Artificial Intelligence (XAI) #Machine Learning in Healthcare #Machine learning #Medicine #Natural language processing #Type (biology) #Type 2 diabetes
paper · doi:10.1016/j.ins.2024.121582
published in Information Sciences 690, 121582 (Elsevier BV)
openalex publication_date 2024/10/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/26
• Developed an explainable machine learning approach for transparent T2DM diagnosis. • Ensures both global and local model interpretability. • Provides patient-specific prediction explanations. • Extracted clinical insights align with existing medical knowledge. • Using data from two hospitals to validate the model internally and externally. The interpretability of disease prediction models is often crucial for their trustworthiness and usability among medical practitioners. Existing methods in interpretable artificial intelligence improve model transparency but fall short in identifying precise, disease-specific primal information. In this work, an interpretable deep learning-based algorithm called the data space landmark refiner was developed, which not only enhances both global interpretability and local interpretability but also reveals the intrinsic information of the data distribution. Using the proposed method, a type 2 diabetes mellitus diagnostic model with high interpretability was constructed on the basis of the electronic health records from two hospitals. Moreover, effective diagnostic information was directly derived from the model’s internal parameters, demonstrating strong alignment with current clinical knowledge. Compared with conventional interpretable machine learning approaches, the proposed method offered more precise and specific interpretability, increasing clinical practitioners’ trust in machine learning-supported diagnostic models.