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Classifying the type of delivery from cardiotocographic signals: A machine learning approach.
Ricciardi, C; Improta, G; Amato, F; Cesarelli, G; Romano, M.
Affiliation
  • Ricciardi C; Department of Advanced Biomedical Sciences, University Hospital of Naples Federico II, Naples, Italy.
  • Improta G; Department of Public Health, University Hospital of Naples Federico II, Naples, Italy; Centro Interdipartimentale di Ricerca in Management Sanitario e Innovazione in Sanità (CIRMIS).
  • Amato F; Centro Interdipartimentale di Ricerca in Management Sanitario e Innovazione in Sanità (CIRMIS); Department of Electrical Engineering and Information Technology, DIETI, University of Naples Federico II, Naples 80125, Italy. Electronic address: framato@unina.it.
  • Cesarelli G; Department of Chemical, Materials and Production Engineering, University of Naples "Federico II", Naples, Italy; Istituto Italiano di Tecnologia, Naples, Italy.
  • Romano M; Department of Experimental and Clinical Medicine (DMSC), University "Magna Graecia" of Catanzaro, Italy.
Comput Methods Programs Biomed ; 196: 105712, 2020 Nov.
Article in En | MEDLINE | ID: mdl-32877811
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Cesarean Section / Machine Learning Type of study: Prognostic_studies / Qualitative_research Limits: Female / Humans / Pregnancy Language: En Journal: Comput Methods Programs Biomed Journal subject: INFORMATICA MEDICA Year: 2020 Document type: Article Affiliation country: Italia Country of publication: Irlanda
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Cesarean Section / Machine Learning Type of study: Prognostic_studies / Qualitative_research Limits: Female / Humans / Pregnancy Language: En Journal: Comput Methods Programs Biomed Journal subject: INFORMATICA MEDICA Year: 2020 Document type: Article Affiliation country: Italia Country of publication: Irlanda