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Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype

Albers, David J. and Levine, Matthew E. and Stuart, Andrew and Mamykina, Lena and Gluckman, Bruce and Hripcsak, George (2018) Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype. Journal of the American Medical Informatics Association, 25 (10). pp. 1392-1401. ISSN 1067-5027. PMCID PMC6188514. https://resolver.caltech.edu/CaltechAUTHORS:20181023-111929468

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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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1093/jamia/ocy106DOIArticle
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6188514PubMed CentralArticle
Additional Information:© The Author(s) 2018. Published by Oxford University Press on behalf of the American Medical Informatics Association. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. Received 8 December 2017; Revised 14 June 2018; Editorial Decision 20 July 2018; Accepted 16 August 2018. Published: 12 October 2018. This work was funded by grants from the National Institutes of Health R01 LM006910 “Discovering and applying knowledge in clinical databases,” U01 HG008680 “Columbia GENIE (GENomic Integration with EHR),” and “Mechanistic machine learning,” LM012734. Conflict of interest statement. None. Contributors: All authors made substantial contributions to the conception and design of the work; DJA wrote the original draft and all authors revised it critically for important intellectual content; had final approval of the version to be published; and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Funders:
Funding AgencyGrant Number
NIHR01 LM006910
NIHU01 HG008680
NIHLM012734
Subject Keywords:data assimilation, Bayesian inverse methods, state space models, self-monitoring data, machine learning, data mining, type 2 diabetes, Gaussian process model, glucose forecasting, precision medicine
Issue or Number:10
PubMed Central ID:PMC6188514
Record Number:CaltechAUTHORS:20181023-111929468
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20181023-111929468
Official Citation:David J Albers, Matthew E Levine, Andrew Stuart, Lena Mamykina, Bruce Gluckman, George Hripcsak; Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype, Journal of the American Medical Informatics Association, Volume 25, Issue 10, 1 October 2018, Pages 1392–1401, https://doi.org/10.1093/jamia/ocy106
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:90365
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:23 Oct 2018 18:37
Last Modified:03 Oct 2019 20:24

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