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dc.contributor.authorHassan, Amier
dc.contributor.authorCritelli, Brian
dc.contributor.authorLahooti, Ila
dc.contributor.authorLahooti, Ali
dc.contributor.authorMatzko, Nate
dc.contributor.authorAdams, Jan Niklas
dc.contributor.authorLiss, Lukas
dc.contributor.authorQuion, Justin
dc.contributor.authorRestrepo, David
dc.contributor.authorNikahd, Melica
dc.contributor.authorCulp, Stacey
dc.contributor.authorNoh, Lydia
dc.contributor.authorTong, Kathleen
dc.contributor.authorPark, Jun Sung
dc.contributor.authorAkshintala, Venkata
dc.contributor.authorWindsor, John A.
dc.contributor.authorMull, Nikhil K.
dc.contributor.authorPapachristou, Georgios I.
dc.contributor.authorCeli, Leo Anthony
dc.contributor.authorLee, Peter J.
dc.date.accessioned2024-04-08T15:46:14Z
dc.date.available2024-04-08T15:46:14Z
dc.date.issued2024-04-02
dc.identifier.issn2397-7523
dc.identifier.urihttps://hdl.handle.net/1721.1/154094
dc.description.abstractAcute pancreatitis (AP) is an acute inflammatory disorder that is common, costly, and is increasing in incidence worldwide with over 300,000 hospitalizations occurring yearly in the United States alone. As its course and outcomes vary widely, a critical knowledge gap in the field has been a lack of accurate prognostic tools to forecast AP patients’ outcomes. Despite several published studies in the last three decades, the predictive performance of published prognostic models has been found to be suboptimal. Recently, non-regression machine learning models (ML) have garnered intense interest in medicine for their potential for better predictive performance. Each year, an increasing number of AP models are being published. However, their methodologic quality relating to transparent reporting and risk of bias in study design has never been systematically appraised. Therefore, through collaboration between a group of clinicians and data scientists with appropriate content expertise, we will perform a systematic review of papers published between January 2021 and December 2023 containing artificial intelligence prognostic models in AP. To systematically assess these studies, the authors will leverage the CHARMS checklist, PROBAST tool for risk of bias assessment, and the most current version of the TRIPOD-AI. (Research Registry ( http://www.reviewregistry1727 .).en_US
dc.publisherSpringer Science and Business Media LLCen_US
dc.relation.isversionof10.1186/s41512-024-00169-1en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceBioMed Centralen_US
dc.subjectApplied Mathematicsen_US
dc.subjectGeneral Mathematicsen_US
dc.titleCritical appraisal of machine learning prognostic models for acute pancreatitis: protocol for a systematic reviewen_US
dc.typeArticleen_US
dc.identifier.citationDiagnostic and Prognostic Research. 2024 Apr 02;8(1):6en_US
dc.contributor.departmentHarvard--MIT Program in Health Sciences and Technology. Laboratory for Computational Physiology
dc.relation.journalDiagnostic and Prognostic Researchen_US
dc.identifier.mitlicensePUBLISHER_CC
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2024-04-07T03:11:39Z
dc.language.rfc3066en
dc.rights.holderThe Author(s)
dspace.date.submission2024-04-07T03:11:39Z
mit.journal.volume8en_US
mit.journal.issue1en_US
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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