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The role of machine learning applications in diagnosing and assessing critical and non-critical CHD: a scoping review.
Helman, Stephanie M; Herrup, Elizabeth A; Christopher, Adam B; Al-Zaiti, Salah S.
Affiliation
  • Helman SM; Department of Acute and Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA.
  • Herrup EA; Division of Pediatric Critical Care Medicine, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA.
  • Christopher AB; Division of Pediatric Cardiology, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA.
  • Al-Zaiti SS; Department of Acute and Tertiary Care Nursing, University of Pittsburgh, Pittsburgh, PA, USA.
Cardiol Young ; 31(11): 1770-1780, 2021 Nov.
Article in En | MEDLINE | ID: mdl-34725005
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Machine Learning / Heart Defects, Congenital Type of study: Diagnostic_studies / Prognostic_studies / Systematic_reviews Limits: Child / Humans Language: En Journal: Cardiol Young Journal subject: ANGIOLOGIA / CARDIOLOGIA / PEDIATRIA 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: Machine Learning / Heart Defects, Congenital Type of study: Diagnostic_studies / Prognostic_studies / Systematic_reviews Limits: Child / Humans Language: En Journal: Cardiol Young Journal subject: ANGIOLOGIA / CARDIOLOGIA / PEDIATRIA Year: 2021 Document type: Article Affiliation country: Country of publication: