ArticleScientific reports2023
The importance of interpreting machine learning models for blood glucose prediction in diabetes: an analysis using SHAP.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis.Frontiers in digital health · 2025Pooled it
- Caffeine and Beetroot Juice Optimize 1,000-m Performance: Shapley Additive Explanations Analysis.American journal of men's healthTrial
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- Multimodal oral cancer detection with embedding-level oversampling of fused image and clinical data.Scientific reports · 2026Article
- Next-generation intelligent framework for pan evaporation prediction: introducing Chebyshev polynomial-based Kolmogorov-Arnold networks.Scientific reports · 2026Article
- Integrative Multidimensional Machine Learning Models for Stroke Prognosis: Age-Stratified and History Engineered Perspectives.Diagnostics (Basel, Switzerland) · 2026Article
- Machine Learning-Based Biomarker Discovery from Serum Trace Elements and Biochemical Parameters in Patients with Nasal Polyps.Biological trace element research · 2026Article
- Machine learning assisted intelligent identification strategy for adulteration peanut oil based on fatty acid GC fingerprint.Food chemistry: X · 2026Article
- Quality, Reliability, and Dissemination of In Vitro Fertilization-Related Videos on Chinese Social Media: Cross-Sectional Analysis of 300 Short Videos.JMIR infodemiology · 2026Article
- Article
- An explainable ensemble machine learning model using baseline blood transcriptomics to predict Parkinson's disease motor progression.Frontiers in digital health · 2026Article
- Inflammatory-neurological deficit versus metabolic dysregulation: unsupervised clustering and SHAP analysis.Frontiers in immunology · 2026Article
- Interpretable glucose forecasting for type 2 diabetes across traditional, deep, and large language models.Scientific reports · 2025Article
- Innovations in early detection of chronic non-communicable diseases among adolescents through an easy-to-Use AutoML paradigm.Health care management science · 2025Observational
- Predicting postprandial glucose excursions to personalize dietary interventions for type-2 diabetes management.Scientific reports · 2025Article
- Supervised machine learning for predicting drug release from acetalated dextran nanofibers.Biomaterials science · 2025Article
- A deep learning framework for virtual continuous glucose monitoring and glucose prediction based on life-log data.Scientific reports · 2025Article
- Clinical Decision Support for Patient Cases with Asymptomatic Carotid Artery Stenosis Using AI Models and Electronic Medical Records.Journal of cardiovascular development and disease · 2025Article
- Machine Learning Models for Predicting Thermal Properties of Radiative Cooling Aerogels.Gels (Basel, Switzerland) · 2025Article
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6 authors.
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Abstract
Machine learning has become a popular tool for learning models of complex dynamics from biomedical data. In Type 1 Diabetes (T1D) management, these models are increasingly been integrated in decision support systems (DSS) to forecast glucose levels and provide preventive therapeutic suggestions, like corrective insulin boluses (CIB), accordingly. Typically, models are chosen based on their prediction accuracy. However, since patient safety is a concern in this application, the algorithm should also be physiologically sound and its outcome should be explainable. This paper aims to discuss the importance of using tools to interpret the output of black-box models in T1D management by presenting a case-of-study on the selection of the best prediction algorithm to integrate in a DSS for CIB suggestion. By retrospectively "replaying" real patient data, we show that two long-short term memory neural networks (LSTM) (named p-LSTM and np-LSTM) with similar prediction accuracy could lead to different therapeutic decisions. An analysis with SHAP-a tool for explaining black-box models' output-unambiguously shows that only p-LSTM learnt the physiological relationship between inputs and glucose prediction, and should therefore be preferred. This is verified by showing that, when embedded in the DSS, only p-LSTM can improve patients' glycemic control.
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