ArticleJournal of the American Medical Informatics Association : JAMIA2021
Machine learning for initial insulin estimation in hospitalized patients.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 58 citations in OpenAlex.
- Real-Time AI-Assisted Insulin Titration System for Glucose Control in Patients With Type 2 Diabetes: A Randomized Clinical Trial.JAMA network open · 2025Trial
- Exploring the potential of XAI methods in generating clinically meaningful explanations for glycemia prediction in diabetes patients.BMC medical informatics and decision making · 2026Article
- Mapping neighbourhood-level drivers of type 2 diabetes for precision public health using predictive and causal machine learning.Scientific reports · 2026Article
- Comparison of logistic regression and artificial neural network models for predicting hypoglycemia in non-ICU inpatients with diabetes.BMC endocrine disorders · 2025Article
- Artificial Intelligence in Diabetes Care: Applications, Challenges, and Opportunities Ahead.Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists · 2025Review
- Monitoring strategies for continuous evaluation of deployed clinical prediction models.Journal of biomedical informatics · 2025Article
- A distributional reinforcement learning model for optimal glucose control after cardiac surgery.NPJ digital medicine · 2025Article
- Visual Analysis of Hyperglycemia After Enteral Nutrition in Critically Ill Patients.Journal of nutrition and metabolism · 2025Article
- 16. Diabetes Care in the Hospital: Standards of Care in Diabetes-2025.Diabetes care · 2025Review
- Starting Insulin Algorithms for Noncritical Illness: A Survey of 32 Academic Hospitals in the United States.Diabetes technology & therapeutics · 2024Article
- Advancing Pharmacy Practice: The Role of Intelligence-Driven Pharmacy Practice and the Emergence of Pharmacointelligence.Integrated pharmacy research & practice · 2024Article
- 16. Diabetes Care in the Hospital: Standards of Care in Diabetes-2024.Diabetes care · 2024Review
- Artificial intelligence in diabetes management: Advancements, opportunities, and challenges.Cell reports. Medicine · 2023Review
- DEPLOYR: a technical framework for deploying custom real-time machine learning models into the electronic medical record.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Insight on physicochemical properties governing peptide MS1 response in HPLC-ESI-MS/MS: A deep learning approach.Computational and structural biotechnology journal · 2023Article
- 16. Diabetes Care in the Hospital: Standards of Care in Diabetes-2023.Diabetes care · 2023Review
- Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review.Diabetology & metabolic syndrome · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
objectiveThe study sought to determine whether machine learning can predict initial inpatient total daily dose (TDD) of insulin from electronic health records more accurately than existing guideline-based dosing recommendations. MATERIALS AND
methodsUsing electronic health records from a tertiary academic center between 2008 and 2020 of 16,848 inpatients receiving subcutaneous insulin who achieved target blood glucose control of 100-180 mg/dL on a calendar day, we trained an ensemble machine learning algorithm consisting of regularized regression, random forest, and gradient boosted tree models for 2-stage TDD prediction. We evaluated the ability to predict patients requiring more than 6 units TDD and their point-value TDDs to achieve target glucose control.
resultsThe method achieves an area under the receiver-operating characteristic curve of 0.85 (95% confidence interval [CI], 0.84-0.87) and area under the precision-recall curve of 0.65 (95% CI, 0.64-0.67) for classifying patients who require more than 6 units TDD. For patients requiring more than 6 units TDD, the mean absolute percent error in dose prediction based on standard clinical calculators using patient weight is in the range of 136%-329%, while the regression model based on weight improves to 60% (95% CI, 57%-63%), and the full ensemble model further improves to 51% (95% CI, 48%-54%). DISCUSSION: Owingto the narrow therapeutic window and wide individual variability, insulin dosing requires adaptive and predictive approaches that can be supported through data-driven analytic tools.
conclusionsMachine learning approaches based on readily available electronic medical records can discriminate which inpatients will require more than 6 units TDD and estimate individual doses more accurately than standard guidelines and practices.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.