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

David J. Albers, Matthew E. Levine, Andrew Stuart, Lena Mamykina, Bruce Gluckman, George Hripcsak

Research output: Contribution to journalReview articlepeer-review

4 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1392-1401
Number of pages10
JournalJournal of the American Medical Informatics Association
Volume25
Issue number10
DOIs
StatePublished - Oct 1 2018

All Science Journal Classification (ASJC) codes

  • Health Informatics

Fingerprint Dive into the research topics of 'Mechanistic machine learning: How data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype'. Together they form a unique fingerprint.

Cite this