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Use of machine learning to predict bladder cancer survival outcomes: a systematic literature review.
Liu, Yi-Shao; Thaliffdeen, Ryan; Han, Sola; Park, Chanhyun.
Afiliación
  • Liu YS; College of Pharmacy, The University of Texas at Austin, 2409 University Ave, Austin, TX, USA.
  • Thaliffdeen R; College of Pharmacy, The University of Texas at Austin, 2409 University Ave, Austin, TX, USA.
  • Han S; College of Pharmacy, The University of Texas at Austin, 2409 University Ave, Austin, TX, USA.
  • Park C; College of Pharmacy, The University of Texas at Austin, 2409 University Ave, Austin, TX, USA.
Expert Rev Pharmacoecon Outcomes Res ; 23(7): 761-771, 2023.
Article en En | MEDLINE | ID: mdl-37306511
An analysis type known as machine learning has recently become popular to predict survival in bladder cancer patients. However, there is debate on how to best use this method, as well as how to report the results of studies. This review looks at recently published machine learning studies, comparing various model details. Most studies found used hospital data, were clear about model factors, and used a model type called artificial neural networks. While these studies may be better at prediction compared to previous methods, there are consistency and clarity issues. Future studies should ensure that models are explainable and relevant to healthcare leaders.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Vejiga Urinaria / Algoritmos Tipo de estudio: Prognostic_studies / Risk_factors_studies / Systematic_reviews Límite: Humans Idioma: En Revista: Expert Rev Pharmacoecon Outcomes Res Asunto de la revista: FARMACOLOGIA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Vejiga Urinaria / Algoritmos Tipo de estudio: Prognostic_studies / Risk_factors_studies / Systematic_reviews Límite: Humans Idioma: En Revista: Expert Rev Pharmacoecon Outcomes Res Asunto de la revista: FARMACOLOGIA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido