Preparing for the next pandemic via transfer learning from existing diseases with hierarchical multi-modal BERT: a study on COVID-19 outcome prediction

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dc.contributor.authorAgarwal, Khushbuen
dc.contributor.authorChoudhury, Sutanayen
dc.contributor.authorTipirneni, Sindhuen
dc.contributor.authorMukherjee, Pritamen
dc.contributor.authorHam, Colbyen
dc.contributor.authorTamang, Suzanneen
dc.contributor.authorBaker, Matthewen
dc.contributor.authorTang, Siyien
dc.contributor.authorKocaman, Veyselen
dc.contributor.authorGevaert, Olivieren
dc.contributor.authorRallo, Roberten
dc.contributor.authorReddy, Chandan K.en
dc.date.accessioned2022-11-14T17:59:11Zen
dc.date.available2022-11-14T17:59:11Zen
dc.date.issued2022-06-24en
dc.description.abstractDeveloping prediction models for emerging infectious diseases from relatively small numbers of cases is a critical need for improving pandemic preparedness. Using COVID-19 as an exemplar, we propose a transfer learning methodology for developing predictive models from multi-modal electronic healthcare records by leveraging information from more prevalent diseases with shared clinical characteristics. Our novel hierarchical, multi-modal model (TransMED) integrates baseline risk factors from the natural language processing of clinical notes at admission, time-series measurements of biomarkers obtained from laboratory tests, and discrete diagnostic, procedure and drug codes. We demonstrate the alignment of TransMED's predictions with well-established clinical knowledge about COVID-19 through univariate and multivariate risk factor driven sub-cohort analysis. TransMED's superior performance over state-of-the-art methods shows that leveraging patient data across modalities and transferring prior knowledge from similar disorders is critical for accurate prediction of patient outcomes, and this approach may serve as an important tool in the early response to future pandemics.en
dc.description.notesThe research described in this paper was supported in part by the Laboratory Directed Research and Development Program at Pacific Northwest National Laboratory, a multiprogram national laboratory operated by Battelle for the U.S. Department of Energy and US National Science Foundation under grant 1838730. O.G. was supported by The Weintz Family COVID-19 Research Fund. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authorsand do not necessarily reflect the views of the funding agency.en
dc.description.sponsorshipLaboratory Directed Research and Development Program at Pacific Northwest National Laboratory; US National Science Foundation [1838730]; Weintz Family COVID-19 Research Funden
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1038/s41598-022-13072-wen
dc.identifier.issn2045-2322en
dc.identifier.issue1en
dc.identifier.other10748en
dc.identifier.pmid35750878en
dc.identifier.urihttp://hdl.handle.net/10919/112588en
dc.identifier.volume12en
dc.language.isoenen
dc.publisherNature Portfolioen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectmental-healthen
dc.subjectchinaen
dc.titlePreparing for the next pandemic via transfer learning from existing diseases with hierarchical multi-modal BERT: a study on COVID-19 outcome predictionen
dc.title.serialScientific Reportsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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