Prediction of Breast Cancer Remission

In conclusion, our research demonstrates the feasibility of predicting breast cancer non-remission using EHR and SDOH data, achieving acceptable performance without complex predictors. Addressing the data quality limitations and refining remission indicators can further improve the models' utility for early treatment decisions, fostering improved patient outcomes and support throughout the cancer journey.

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Publication Details

Journal
Quality Management in Health Care
Published
2025-04-01
DOI
https://doi.org/10.1097/qmh.0000000000000513
Primary Topic
Global Cancer Incidence and Screening
Type
article
Field-Weighted Citation Impact
0.00
Controls
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article

Prediction of Breast Cancer Remission

Yalin Li, Hong Xue, S Shrestha, Vicky Cárdenas
Quality Management in Health Care
Global Cancer Incidence and Screening
article

Prediction of Breast Cancer Remission

Yalin Li, Hong Xue, S Shrestha, Vicky Cárdenas
article en

Abstract

In conclusion, our research demonstrates the feasibility of predicting breast cancer non-remission using EHR and SDOH data, achieving acceptable performance without complex predictors. Addressing the data quality limitations and refining remission indicators can further improve the models' utility for early treatment decisions, fostering improved patient outcomes and support throughout the cancer journey.

Quality Management in Health CareVol. 34(2)
George Mason University (US)
Good health and well-being
Openalex Percentile: Top 91%
Global Cancer Incidence and Screening
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