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

被引：0

作者：

Shaban-Nejad, Arash ^{[1
]}

Ammar, Nariman ^{[2
]}

Kumsa, Fekede ^{[1
]}

Hashtarkhani, Soheil ^{[1
]}

White, Brianna ^{[1
]}

Chinthala, Lokesh K. ^{[1
]}

Owens, Chase A. ^{[3
]}

Hayes, Neil ^{[4
]}

Schwartz, David L. ^{[5
,6
]}

机构：

[1] Univ Tennessee, Dept Pediat, Coll Med,Hlth Sci Ctr, Oak Ridge Natl Lab UTHSC ORNL,Ctr Biomed Informat, Memphis, TN USA

[2] Illinois State Univ, Sch Informat Technol, Coll Appl Sci & Technol, Normal, IL USA

[3] Univ Tennessee, Hlth Sci Ctr, Memphis, TN USA

[4] Univ Tennessee, Div Med Hematol, Dept Med, Coll Med,Hlth Sci Ctr, Memphis, TN USA

[5] Univ Tennessee, Coll Med, Dept Radiat Oncol, Hlth Sci Ctr, Memphis, TN USA

[6] Univ Tennessee, Coll Med, Dept Prevent Med, Hlth Sci Ctr, Memphis, TN USA

来源：

MEDINFO 2023 - THE FUTURE IS ACCESSIBLE | 2024年 / 310卷

关键词：

Cancer informatics; public health informatics; explainable AI; causal inference; social determinants of health; disparities;

D O I：

10.3233/SHTI231264

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：1501 / 1502

页数：2

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