Using artificial intelligence for improving stroke diagnosis in emergency departments: a practical framework

Simple item page
dc.contributor.authorAbedi, Vidaen
dc.contributor.authorKhan, Ayeshaen
dc.contributor.authorChaudhary, Durgeshen
dc.contributor.authorMisra, Debdiptoen
dc.contributor.authorAvula, Venkateshen
dc.contributor.authorMathrawala, Dhruven
dc.contributor.authorKraus, Chadden
dc.contributor.authorMarshall, Kyle A.en
dc.contributor.authorChaudhary, Nayanen
dc.contributor.authorLi, Xiaoen
dc.contributor.authorSchirmer, Clemens M.en
dc.contributor.authorScalzo, Fabienen
dc.contributor.authorLi, Jiangen
dc.contributor.authorZand, Raminen
dc.contributor.departmentFralin Life Sciences Instituteen
dc.date.accessioned2020-10-09T14:09:06Zen
dc.date.available2020-10-09T14:09:06Zen
dc.date.issued2020-08en
dc.description.abstractStroke is the fifth leading cause of death in the United States and a major cause of severe disability worldwide. Yet, recognizing the signs of stroke in an acute setting is still challenging and leads to loss of opportunity to intervene, given the narrow therapeutic window. A decision support system using artificial intelligence (AI) and clinical data from electronic health records combined with patients' presenting symptoms can be designed to support emergency department providers in stroke diagnosis and subsequently reduce the treatment delay. In this article, we present a practical framework to develop a decision support system using AI by reflecting on the various stages, which could eventually improve patient care and outcome. We also discuss the technical, operational, and ethical challenges of the process.en
dc.description.notesThis work was sponsored in part by funds from the Geisinger Health Plan Quality Fund and National Institute of Health R56HL116832 (subaward) to VA and RZ. The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.en
dc.description.sponsorshipGeisinger Health Plan Quality Fund; National Institute of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R56HL116832]en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1177/1756286420938962en
dc.identifier.eissn1756-2864en
dc.identifier.issn1756-2856en
dc.identifier.other1756286420938962en
dc.identifier.pmid32922515en
dc.identifier.urihttp://hdl.handle.net/10919/100316en
dc.identifier.volume13en
dc.language.isoenen
dc.rightsCreative Commons Attribution-NonCommercial 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/en
dc.subjectacute strokeen
dc.subjectartificial intelligenceen
dc.subjectcerebrovascular diseaseen
dc.subjectstrokeen
dc.subjectcomputer aided diagnosisen
dc.subjectischemic strokeen
dc.subjectMachine learningen
dc.subjectstroke diagnosisen
dc.subjectstroke in emergency departmenten
dc.titleUsing artificial intelligence for improving stroke diagnosis in emergency departments: a practical frameworken
dc.title.serialTherapeutic Advances In Neurological Disordersen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
1756286420938962.pdf
Size:
636.27 KB
Format:
Adobe Portable Document Format
Description:
Download

Collections

Scholarly Works, Fralin Life Sciences Institute
Destination Area: Data and Decisions (D&D)