ArticleJournal of the American Medical Informatics Association : JAMIA2018
Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype.
Article in Journal of the American Medical Informatics Association : JAMIA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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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
22 citing papers in PubMed.
- Validating medical digital twins for clinical decision support: beyond predictive accuracy.JAMIA open · 2026Review
- Toward universal representations of the living: Physiological invariance for transportable medical AI.PLOS digital health · 2026Review
- A multiobjective optimization approach to data assimilation for complex biological systems with sparse data.Mathematical biosciences · 2026Article
- Navigating the landscape of personalized oncology: overcoming challenges and expanding horizons with computational modeling.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Harnessing artificial intelligence for the diagnosis and treatment of neurological emergencies: a comprehensive review of recent advances and future directions.Frontiers in neurology · 2024Review
- A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimation.Journal of biomedical informatics · 2023Article
- A simple modeling framework for prediction in the human glucose-insulin system.Chaos (Woodbury, N.Y.) · 2023Article
- Missing data matter: an empirical evaluation of the impacts of missing EHR data in comparative effectiveness research.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Evaluating Prediction of Continuous Clinical Values: A Glucose Case Study.Methods of information in medicine · 2022Article
- The Artificial Intelligence Doctor: Considerations for the Clinical Implementation of Ethical AI.Acta neurochirurgica. Supplement · 2022Article
- Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection.Frontiers in bioengineering and biotechnology · 2022Review
- Delay-induced uncertainty in the glucose-insulin system: Pathogenicity for obesity and type-2 diabetes mellitus.Frontiers in physiology · 2022Article
- From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2021Article
- Clinical Decision Support for Traumatic Brain Injury: Identifying a Framework for Practical Model-Based Intracranial Pressure Estimation at Multihour Timescales.JMIR medical informatics · 2021Article
- Delay-induced uncertainty for a paradigmatic glucose-insulin model.Chaos (Woodbury, N.Y.) · 2021Article
- Enabling personalized decision support with patient-generated data and attributable components.Journal of biomedical informatics · 2021Article
- Long-Time Prediction of Arrhythmic Cardiac Action Potentials Using Recurrent Neural Networks and Reservoir Computing.Frontiers in physiology · 2021Article
- Physician understanding, explainability, and trust in a hypothetical machine learning risk calculator.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Bayesian Data Assimilation to Support Informed Decision Making in Individualized Chemotherapy.CPT: pharmacometrics & systems pharmacology · 2020Article
- Designing for engagement with self-monitoring: A user-centered approach with low-income, Latino adults with Type 2 Diabetes.International journal of medical informatics · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
We introduce data assimilation as a computational method that uses machine learning to combine data with human knowledge in the form of mechanistic models in order to forecast future states, to impute missing data from the past by smoothing, and to infer measurable and unmeasurable quantities that represent clinically and scientifically important phenotypes. We demonstrate the advantages it affords in the context of type 2 diabetes by showing how data assimilation can be used to forecast future glucose values, to impute previously missing glucose values, and to infer type 2 diabetes phenotypes. At the heart of data assimilation is the mechanistic model, here an endocrine model. Such models can vary in complexity, contain testable hypotheses about important mechanics that govern the system (eg, nutrition's effect on glucose), and, as such, constrain the model space, allowing for accurate estimation using very little data.
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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.