Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/45991
Full metadata record
DC FieldValueLanguage
dc.contributor.authorLeming, Matthew-
dc.contributor.authorKim, Kyungsu-
dc.contributor.authorBRUFFAERTS, Rose-
dc.contributor.authorIm, Hyungsoon-
dc.date.accessioned2025-05-14T09:49:04Z-
dc.date.available2025-05-14T09:49:04Z-
dc.date.issued2025-
dc.date.submitted2025-05-08T15:28:47Z-
dc.identifier.citationNeuron, 113 (8) , p. 1129 -1132-
dc.identifier.urihttp://hdl.handle.net/1942/45991-
dc.description.abstractIn this NeuroView, we discuss challenges and best practices when dealing with disease-detection AI models that are trained on heterogeneous clinical data, focusing on the interrelated problems of model bias, causality, and rare diseases.-
dc.description.sponsorshipThe work was supported by the National Institutes of Health grant (R01GM138778-04S1 to H.I.). M.L. was supported by the ECOR FMD Fellowship, which was provided by Massachusetts General Hospital. The funders had no role in study design, data collection and analysis, decision to publish, or manuscript preparation.-
dc.language.isoen-
dc.publisherCELL PRESS-
dc.rights2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.-
dc.subject.otherHumans-
dc.subject.otherNervous System Diseases-
dc.subject.otherArtificial Intelligence-
dc.titleStrategies for mitigating data heterogeneities in AI-based neuro-disease detection-
dc.typeJournal Contribution-
dc.identifier.epage1132-
dc.identifier.issue8-
dc.identifier.spage1129-
dc.identifier.volume113-
local.format.pages4-
local.bibliographicCitation.jcatA1-
dc.description.notesIm, H (corresponding author), Massachusetts Gen Hosp, Ctr Syst Biol, Boston, MA 02114 USA.; Im, H (corresponding author), Massachusetts Gen Hosp, Massachusetts Alzheimers Dis Res Ctr, Boston, MA 02114 USA.; Im, H (corresponding author), Massachusetts Gen Hosp, Dept Radiol, Boston, MA 02114 USA.-
dc.description.notesim.hyungsoon@mgh.harvard.edu-
local.publisher.place50 HAMPSHIRE ST, FLOOR 5, CAMBRIDGE, MA 02139 USA-
local.type.refereedRefereed-
local.type.specifiedArticle-
dc.identifier.doi10.1016/j.neuron.2025.01.028-
dc.identifier.pmid40037359-
dc.identifier.isi001473755700001-
local.provider.typewosris-
local.description.affiliation[Leming, Matthew; Im, Hyungsoon] Massachusetts Gen Hosp, Ctr Syst Biol, Boston, MA 02114 USA.-
local.description.affiliation[Leming, Matthew; Im, Hyungsoon] Massachusetts Gen Hosp, Massachusetts Alzheimers Dis Res Ctr, Boston, MA 02114 USA.-
local.description.affiliation[Kim, Kyungsu] Seoul Natl Univ, Sch Transdisciplinary Innovat, Seoul, South Korea.-
local.description.affiliation[Kim, Kyungsu] Seoul Natl Univ, Coll Med, Dept Biomed Sci, Seoul, South Korea.-
local.description.affiliation[Bruffaerts, Rose] Univ Antwerp, Dept Biomed Sci, Expt Neurobiol Unit ENU, Computat Neurol, Antwerp, Belgium.-
local.description.affiliation[Bruffaerts, Rose] Hasselt Univ, Biomed Res Inst, Diepenbeek, Belgium.-
local.description.affiliation[Im, Hyungsoon] Massachusetts Gen Hosp, Dept Radiol, Boston, MA 02114 USA.-
local.uhasselt.internationalyes-
item.contributorLeming, Matthew-
item.contributorKim, Kyungsu-
item.contributorBRUFFAERTS, Rose-
item.contributorIm, Hyungsoon-
item.fullcitationLeming, Matthew; Kim, Kyungsu; BRUFFAERTS, Rose & Im, Hyungsoon (2025) Strategies for mitigating data heterogeneities in AI-based neuro-disease detection. In: Neuron, 113 (8) , p. 1129 -1132.-
item.accessRightsRestricted Access-
item.fulltextWith Fulltext-
crisitem.journal.issn0896-6273-
crisitem.journal.eissn1097-4199-
Appears in Collections:Research publications
Files in This Item:
File Description SizeFormat 
Strategies for mitigating data heterogeneities in AI-based neuro-disease detection.pdf
  Restricted Access		Published version1.76 MBAdobe PDFView/Open    Request a copy
Show simple item record

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.