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Machine Learning and Surgical Outcomes Prediction: A Systematic Review.
Elfanagely, Omar; Toyoda, Yoshiko; Othman, Sammy; Mellia, Joseph A; Basta, Marten; Liu, Tony; Kording, Konrad; Ungar, Lyle; Fischer, John P.
Afiliação
  • Elfanagely O; Division of Plastic Surgery, Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania. Electronic address: oelfanagely@gmail.com.
  • Toyoda Y; Division of Plastic Surgery, Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Othman S; Division of Plastic Surgery, Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Mellia JA; Division of Plastic Surgery, Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Basta M; Department of Plastic and Reconstructive Surgery, Brown University, Providence, Rhode Island.
  • Liu T; Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Kording K; Department of Neuroscience, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Ungar L; Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Fischer JP; Division of Plastic Surgery, Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania.
J Surg Res ; 264: 346-361, 2021 08.
Article em En | MEDLINE | ID: mdl-33848833
BACKGROUND: Machine learning (ML) has garnered increasing attention as a means to quantitatively analyze the growing and complex medical data to improve individualized patient care. We herein aim to critically examine the current state of ML in predicting surgical outcomes, evaluate the quality of currently available research, and propose areas of improvement for future uses of ML in surgery. METHODS: A systematic review was conducted in accordance with the Preferred Reporting Items for a Systematic Review and Meta-Analysis (PRISMA) checklist. PubMed, MEDLINE, and Embase databases were reviewed under search syntax "machine learning" and "surgery" for papers published between 2015 and 2020. RESULTS: Of the initial 2677 studies, 45 papers met inclusion and exclusion criteria. Fourteen different subspecialties were represented with neurosurgery being most common. The most frequently used ML algorithms were random forest (n = 19), artificial neural network (n = 17), and logistic regression (n = 17). Common outcomes included postoperative mortality, complications, patient reported quality of life and pain improvement. All studies which compared ML algorithms to conventional studies which used area under the curve (AUC) to measure accuracy found improved outcome prediction with ML models. CONCLUSIONS: While still in its early stages, ML models offer surgeons an opportunity to capitalize on the myriad of clinical data available and improve individualized patient care. Limitations included heterogeneous outcome and imperfect quality of some of the papers. We therefore urge future research to agree upon methods of outcome reporting and require basic quality standards.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Planejamento de Assistência ao Paciente / Complicações Pós-Operatórias / Procedimentos Cirúrgicos Operatórios / Aprendizado de Máquina Tipo de estudo: Etiology_studies / Guideline / Prognostic_studies / Risk_factors_studies / Systematic_reviews Limite: Humans Idioma: En Revista: J Surg Res Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Planejamento de Assistência ao Paciente / Complicações Pós-Operatórias / Procedimentos Cirúrgicos Operatórios / Aprendizado de Máquina Tipo de estudo: Etiology_studies / Guideline / Prognostic_studies / Risk_factors_studies / Systematic_reviews Limite: Humans Idioma: En Revista: J Surg Res Ano de publicação: 2021 Tipo de documento: Article