Towards an Explainable AI Platform to Study Interruptions in Cancer Radiation Therapy-Reference-Cited by-同舟云学术

Towards an Explainable AI Platform to Study Interruptions in Cancer Radiation Therapy

Published:2024-01-25 Issue: Volume: Page:
ISSN:0926-9630
Container-title:Studies in Health Technology and Informatics
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Author:

Shaban-Nejad Arash¹^ORCID,Ammar Nariman²,Kumsa Fekede¹,Hashtarkhani Soheil¹,White Brianna¹,Chinthala Lokesh K.¹,Owens Chase A.³,Hayes Neil⁴,Schwartz David L.⁵

Affiliation:

1. 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

2. School of Information Technology, College of Applied Science and Technology, Illinois State University, Normal, Illinois, USA

3. University of Tennessee Health Science Center, Memphis, Tennessee, USA

4. Division of Med Hematology, Department of Medicine, University of Tennessee Health Science Center, College of Medicine, Memphis, Tennessee, USA

5. Departments of Radiation Oncology & Preventive Medicine University of Tennessee Health Science Center, College of Medicine, Memphis, Tennessee, USA

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.

Publisher

IOS Press

Link

https://ebooks.iospress.nl/pdf/doi/10.3233/SHTI231264