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Novel ECG features and machine learning to optimize culprit lesion detection in patients with suspected acute coronary syndrome.
Bouzid, Zeineb; Faramand, Ziad; Gregg, Richard E; Helman, Stephanie; Martin-Gill, Christian; Saba, Samir; Callaway, Clifton; Sejdic, Ervin; Al-Zaiti, Salah.
Affiliation
  • Bouzid Z; Department of Electrical & Computer Engineering, PA, USA.
  • Faramand Z; Department of Acute & Tertiary Care Nursing, PA, USA; University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA.
  • Gregg RE; Advanced Algorithm Research Center, Philips Healthcare, Andover, MA, USA.
  • Helman S; Department of Acute & Tertiary Care Nursing, PA, USA.
  • Martin-Gill C; Department of Emergency Medicine, PA, USA; University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA.
  • Saba S; Division of Cardiology at University of Pittsburgh, PA, USA; University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA.
  • Callaway C; Department of Emergency Medicine, PA, USA; University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA.
  • Sejdic E; Department of Electrical & Computer Engineering, PA, USA; Department of Bioengineering at Swanson School of Engineering, PA, USA; Department of Biomedical Informatics at School of Medicine, PA, USA; Intelligent Systems Program at School of Computing and Information, PA, USA.
  • Al-Zaiti S; Department of Acute & Tertiary Care Nursing, PA, USA; Department of Emergency Medicine, PA, USA; Division of Cardiology at University of Pittsburgh, PA, USA. Electronic address: ssa33@pitt.edu.
J Electrocardiol ; 69S: 31-37, 2021.
Article in En | MEDLINE | ID: mdl-34332752
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Acute Coronary Syndrome Type of study: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Adult / Aged / Female / Humans / Male / Middle aged Language: En Journal: J Electrocardiol Year: 2021 Document type: Article Affiliation country: Country of publication:
Fulltext
Add to My VHL
Print
XML
PubMed Links
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Acute Coronary Syndrome Type of study: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Adult / Aged / Female / Humans / Male / Middle aged Language: En Journal: J Electrocardiol Year: 2021 Document type: Article Affiliation country: Country of publication: