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Towards an Explainable AI Platform to Study Interruptions in Cancer Radiation Therapy.
Shaban-Nejad, Arash; Ammar, Nariman; Kumsa, Fekede; Hashtarkhani, Soheil; White, Brianna; Chinthala, Lokesh K; Owens, Chase A; Hayes, Neil; Schwartz, David L.
Afiliação
  • Shaban-Nejad A; University of Tennessee Health Science Center - Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, Memphis, Tennessee, USA.
  • Ammar N; School of Information Technology, College of Applied Science and Technology, Illinois State University, Normal, Illinois, USA.
  • Kumsa F; University of Tennessee Health Science Center - Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, Memphis, Tennessee, USA.
  • Hashtarkhani S; University of Tennessee Health Science Center - Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, Memphis, Tennessee, USA.
  • White B; University of Tennessee Health Science Center - Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, Memphis, Tennessee, USA.
  • Chinthala LK; University of Tennessee Health Science Center - Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, Memphis, Tennessee, USA.
  • Owens CA; University of Tennessee Health Science Center, Memphis, Tennessee, USA.
  • Hayes N; Division of Med Hematology, Department of Medicine, University of Tennessee Health Science Center, College of Medicine, Memphis, Tennessee, USA.
  • Schwartz DL; Departments of Radiation Oncology & Preventive Medicine University of Tennessee Health Science Center, College of Medicine, Memphis, Tennessee, USA.
Stud Health Technol Inform ; 310: 1501-1502, 2024 Jan 25.
Article em En | MEDLINE | ID: mdl-38269716
ABSTRACT
Radiation therapy interruptions drive cancer treatment failures; they represent an untapped opportunity for improving outcomes and narrowing treatment disparities. This research reports on the early development of the X-CART platform, which uses explainable AI to model cancer treatment outcome metrics based on high-dimensional associations with our local social determinants of health dataset to identify and explain causal pathways linking social disadvantage with increased radiation therapy interruptions.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Benchmarking / Neoplasias Idioma: En Ano de publicação: 2024 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Benchmarking / Neoplasias Idioma: En Ano de publicação: 2024 Tipo de documento: Article