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Prediction of postoperative complications after oesophagectomy using machine-learning methods.
Jung, Jin-On; Pisula, Juan I; Bozek, Kasia; Popp, Felix; Fuchs, Hans F; Schröder, Wolfgang; Bruns, Christiane J; Schmidt, Thomas.
Afiliación
  • Jung JO; Department of General, Visceral, Tumour, and Transplantation Surgery, University Hospital of Cologne, Cologne, Germany.
  • Pisula JI; Centre for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital of Cologne, Cologne, Germany.
  • Bozek K; Centre for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital of Cologne, Cologne, Germany.
  • Popp F; Department of General, Visceral, Tumour, and Transplantation Surgery, University Hospital of Cologne, Cologne, Germany.
  • Fuchs HF; Department of General, Visceral, Tumour, and Transplantation Surgery, University Hospital of Cologne, Cologne, Germany.
  • Schröder W; Department of General, Visceral, Tumour, and Transplantation Surgery, University Hospital of Cologne, Cologne, Germany.
  • Bruns CJ; Department of General, Visceral, Tumour, and Transplantation Surgery, University Hospital of Cologne, Cologne, Germany.
  • Schmidt T; Department of General, Visceral, Tumour, and Transplantation Surgery, University Hospital of Cologne, Cologne, Germany.
Br J Surg ; 110(10): 1361-1366, 2023 09 06.
Article en En | MEDLINE | ID: mdl-37343072
The human gullet or stomach can develop tumours. Surgery can help to cure patients with these tumours. But the operation is risky because sometimes adverse events can happen afterwards. So far, there is no reliable prediction model. It may help to predict the risk of adverse events accurately. For example, patients with a high risk could be observed more thoroughly. Patients with a low risk may not need unnecessary procedures. The information of all patients with an operation at a specialized hospital was collected. Machine learning is a complex mathematical method and was used in this study. It is able to analyse big data sets of information. One machine-learning method called neural network was best in predicting adverse events. Right now, the performance may not be strong enough to fully rely on the prediction. However, refinement of the prediction and more data could improve the neural network in the future.
Asunto(s)

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Esofagectomía / Aprendizaje Automático Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Br J Surg Año: 2023 Tipo del documento: Article País de afiliación: Alemania

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Esofagectomía / Aprendizaje Automático Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Br J Surg Año: 2023 Tipo del documento: Article País de afiliación: Alemania