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Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities.
Gonzalez, Ricardo; Saha, Ashirbani; Campbell, Clinton J V; Nejat, Peyman; Lokker, Cynthia; Norgan, Andrew P.
Afiliação
  • Gonzalez R; DeGroote School of Business, McMaster University, Hamilton, Ontario, Canada.
  • Saha A; Division of Computational Pathology and Artificial Intelligence, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States.
  • Campbell CJV; Department of Oncology, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada.
  • Nejat P; Escarpment Cancer Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, Ontario, Canada.
  • Lokker C; William Osler Health System, Brampton, Ontario, Canada.
  • Norgan AP; Department of Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada.
J Pathol Inform ; 15: 100347, 2024 Dec.
Article em En | MEDLINE | ID: mdl-38162950
ABSTRACT
This paper discusses some overlooked challenges faced when working with machine learning models for histopathology and presents a novel opportunity to support "Learning Health Systems" with them. Initially, the authors elaborate on these challenges after separating them according to their mitigation strategies those that need innovative approaches, time, or future technological capabilities and those that require a conceptual reappraisal from a critical perspective. Then, a novel opportunity to support "Learning Health Systems" by integrating hidden information extracted by ML models from digitalized histopathology slides with other healthcare big data is presented.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Clinical_trials / Health_economic_evaluation / Prognostic_studies Idioma: En Revista: J Pathol Inform Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Canadá

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Clinical_trials / Health_economic_evaluation / Prognostic_studies Idioma: En Revista: J Pathol Inform Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Canadá