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Applications of AI in Predicting Drug Responses for Type 2 Diabetes
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Machine Learning to Identify Predictors of Glycemic Control in Type 2 Diabetes: An Analysis of Target HbA1c Reduction Using Empagliflozin/Linagliptin Data
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Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review
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A manifesto on explainability for artificial intelligence in medicine
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Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011–2022)
inferredcites →https://doi.org/10.1016/j.compbiomed.2018.10.017openalex-snapshot
Stacked classifiers for individualized prediction of glycemic control following initiation of metformin therapy in type 2 diabetes
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Artificial intelligence in cancer diagnosis and therapy: Current status and future perspective
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Ensemble deep learning: A review
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Machine learning and statistic analysis to predict drug treatment outcome in pediatric epilepsy patients with tuberous sclerosis complex
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Prediction of response after cardiac resynchronization therapy with machine learning
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Ensembles for feature selection: A review and future trends
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Tree smoothing: Post-hoc regularization of tree ensembles for interpretable machine learning
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Mixed effect machine learning: A framework for predicting longitudinal change in hemoglobin A1c
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Predicting and understanding the response to short-term intensive insulin therapy in people with early type 2 diabetes
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Estimating individual treatment effect on disability progression in multiple sclerosis using deep learning
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The curse(s) of dimensionality
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Prediction of treatment outcome in clinical trials under a personalized medicine perspective
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On evaluation metrics for medical applications of artificial intelligence
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A performance evaluation of drug response prediction models for individual drugs
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Predicting three-month fasting blood glucose and glycated hemoglobin changes in patients with type 2 diabetes mellitus based on multiple machine learning algorithms
inferredcites →https://doi.org/10.1038/s41746-018-0029-1openalex-snapshot
Scalable and accurate deep learning with electronic health records
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Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
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An overview of machine learning methods for monotherapy drug response prediction
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Deep learning for drug response prediction in cancer
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Status of glycosylated hemoglobin and prediction of glycemic control among patients with insulin-treated type 2 diabetes in North China: a multicenter observational study
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A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects
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Prognosis and Treatment Prediction of Type-2 Diabetes Using Deep Neural Network and Machine Learning Classifiers
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Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis
inferredcites →https://doi.org/10.1109/jbhi.2020.3040225openalex-snapshot
Deep Learning for Diabetes: A Systematic Review
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Deep ROC Analysis and AUC as Balanced Average Accuracy, for Improved Classifier Selection, Audit and Explanation
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Estimating treatment effects with machine learning
inferredcites →https://doi.org/10.1111/dom.13860openalex-snapshot
Predicting short‐ and long‐term glycated haemoglobin response after insulin initiation in patients with type 2 diabetes mellitus using machine‐learning algorithms
inferredcites →https://doi.org/10.1136/bmj.g7594openalex-snapshot
Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement
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A Practical Tutorial on Explainable AI Techniques
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Ensemble Deep Learning for Biomedical Time Series Classification
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Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future
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Machine learning model for identifying important clinical features for predicting remission in patients with rheumatoid arthritis treated with biologics
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Clinical predictors of response to methotrexate in patients with rheumatoid arthritis: a machine learning approach using clinical trial data
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Machine learning and deep learning predictive models for type 2 diabetes: a systematic review
inferredcites →https://doi.org/10.21203/rs.3.rs-1868105/v1openalex-snapshot
Predicting three-month fasting blood glucose and glycated hemoglobin of patients with type 2 diabetes based on multiple machine learning algorithms
inferredcites →https://doi.org/10.2196/25237openalex-snapshot
Improving Current Glycated Hemoglobin Prediction in Adults: Use of Machine Learning Algorithms With Electronic Health Records
inferredcites →https://doi.org/10.2196/27858openalex-snapshot
Effective Treatment Recommendations for Type 2 Diabetes Management Using Reinforcement Learning: Treatment Recommendation Model Development and Validation
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Precision Medicine in Type 2 Diabetes: Using Individualized Prediction Models to Optimize Selection of Treatment
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The Probability of A1C Goal Attainment in Patients With Uncontrolled Type 2 Diabetes in a Large Integrated Delivery System: A Prediction Model
inferredcites →https://doi.org/10.2337/dci20-0061openalex-snapshot
Response to Comment on Pantalone et al. The Probability of A1C Goal Attainment in Patients With Uncontrolled Type 2 Diabetes in a Large Integrated Delivery System: A Prediction Model. Diabetes Care 2020;43:1910–1919
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Pathophysiology of Type 2 Diabetes Mellitus
inferredcites →https://doi.org/10.4230/oasics.dx.2024.27openalex-snapshot
On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/s11428-025-01362-6openalex-snapshot
Das Potenzial von künstlicher Intelligenz in der Diabetologie
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Harnessing technology and AI-driven innovations in diabetes management: addressing clinical challenges and the rising global burden
inferred← citeshttps://doi.org/10.1093/jpp/rgag001openalex-snapshot
Precision pharmaceutics: bioinformatics and pharmacogenomics as catalysts for personalized drug therapy
inferred← citeshttps://doi.org/10.1109/iatmsi68868.2026.11465615openalex-snapshot
SmartDiabX: An End-to-End AI System for Predicting Undiagnosed Type II Diabetes
inferred← citeshttps://doi.org/10.1111/dom.70648openalex-snapshot
Identifying Predictors of Early Treatment Intensification in Individuals With Type 2 Diabetes Treated With GLP ‐1 Receptor Agonists: A Machine Learning–Based Analysis of a Large Real World Cohort
inferred← citeshttps://doi.org/10.2196/82635openalex-snapshot
Predictors of Glycemic Response to Sulfonylurea Therapy in Type 2 Diabetes Over 12 Months: Comparative Analysis of Linear Regression and Machine Learning Models
inferred← citeshttps://doi.org/10.3389/fendo.2026.1887643openalex-snapshot
From explainability to clinical actionability: translating artificial intelligence models into decision support for endocrine disease management
inferred← citeshttps://doi.org/10.3389/fpubh.2025.1606751openalex-snapshot
A machine learning model for predicting obesity risk in patients with diabetes mellitus: analysis of NHANES 2007–2018
inferred← citeshttps://doi.org/10.4103/ijhas.ijhas_71_26openalex-snapshot
A comprehensive review on the integration of artificial intelligence and machine learning in pharmacology: Innovations, applications, and future directions
inferred← citeshttps://doi.org/10.51847/raxqlhomh9openalex-snapshot
Machine learning-based statistical prediction of disease progression in patients with diabetes mellitus
inferred← citeshttps://doi.org/10.56294/shp2025236openalex-snapshot
Ai for autonomous health care on diabetes diagnostics
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