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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\u2019s effect on glucose), and, as such, constrain the model space, allowing for accurate estimation using very little data.<\/jats:p>","DOI":"10.1093\/jamia\/ocy106","type":"journal-article","created":{"date-parts":[[2018,8,17]],"date-time":"2018-08-17T19:29:04Z","timestamp":1534534144000},"page":"1392-1401","source":"Crossref","is-referenced-by-count":31,"title":["Mechanistic machine learning: how data assimilation leverages physiologic knowledge using Bayesian inference to forecast the future, infer the present, and phenotype"],"prefix":"10.1093","volume":"25","author":[{"given":"David J","family":"Albers","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, New York, USA"}]},{"given":"Matthew E","family":"Levine","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, New York, USA"}]},{"given":"Andrew","family":"Stuart","sequence":"additional","affiliation":[{"name":"Department of Computing and Mathematical Sciences, University California Institute of Technology, Pasadena, California, USA"}]},{"given":"Lena","family":"Mamykina","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, New York, USA"}]},{"given":"Bruce","family":"Gluckman","sequence":"additional","affiliation":[{"name":"Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, Pennsylvania, USA"}]},{"given":"George","family":"Hripcsak","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, New York, USA"}]}],"member":"286","published-online":{"date-parts":[[2018,10,12]]},"reference":[{"issue":"3","key":"2020110612233329000_ocy106-B1","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1093\/jamia\/ocv187","article-title":"Data-driven health management: reasoning about personally generated data in diabetes with information technologies","volume":"23","author":"Mamykina","year":"2016","journal-title":"J Am Med Inform Assoc"},{"key":"2020110612233329000_ocy106-B2","volume-title":"Mathematical Physiology II: Systems Physiology","author":"Keener","year":"2008"},{"key":"2020110612233329000_ocy106-B3","volume-title":"Deep Learning","author":"Goodfellow","year":"2016"},{"key":"2020110612233329000_ocy106-B4","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-20325-6","volume-title":"Data Assimilation","author":"Law","year":"2015"},{"key":"2020110612233329000_ocy106-B5","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9781107706804","volume-title":"Probabilistic Forecasting and Bayesian Data Assimilation","author":"Reich","year":"2015"},{"key":"2020110612233329000_ocy106-B6","doi-asserted-by":"crossref","DOI":"10.1137\/1.9781611974546","volume-title":"Data Assimilation","author":"Asch","year":"2016"},{"key":"2020110612233329000_ocy106-B7","doi-asserted-by":"crossref","DOI":"10.1002\/9780470430583","volume-title":"Bayesian Signal Processing: Classical, Modern, and Particle Filtering Methods","author":"Candy","year":"2009"},{"key":"2020110612233329000_ocy106-B8","doi-asserted-by":"crossref","DOI":"10.1002\/9781118287798","volume-title":"Baysian Estimation and Tracking","author":"Haug","year":"2012"},{"key":"2020110612233329000_ocy106-B9","volume-title":"Beyond the Kalman Filter: Particle Filters for Tracking and Applications","author":"Ristic","year":"2004"},{"key":"2020110612233329000_ocy106-B10","volume-title":"Stochastic Processes and Filtering Theory","author":"Jazwinski","year":"1998"},{"issue":"1","key":"2020110612233329000_ocy106-B11","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1115\/1.3662552","article-title":"A new approach to linear filtering and prediction problems","volume":"82","author":"Kalman","year":"1960","journal-title":"J Basic Eng"},{"key":"2020110612233329000_ocy106-B12","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1017\/S0962492910000061","article-title":"Inverse problems: a Bayesian perspective","volume":"19","author":"Stuart","year":"2010","journal-title":"Acta Numerica"},{"issue":"11","key":"2020110612233329000_ocy106-B13","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.0030204","article-title":"From inverse problems in mathematical physiology to quantitative differential diagnoses","volume":"3","author":"Zenker","year":"2007","journal-title":"PLoS Comput Biol"},{"issue":"3","key":"2020110612233329000_ocy106-B14","doi-asserted-by":"crossref","first-page":"319","DOI":"10.2307\/1402616","article-title":"Time series analysis in 1880. 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