Navigating the landscape of personalized oncology: overcoming challenges and expanding horizons with computational modeling.
1/5 보강
PICO 자동 추출 (휴리스틱, conf 3/4)
유사 논문P · Population 대상 환자/모집단
13 patients included in this study.
I · Intervention 중재 / 시술
novel therapies using clinical oncology as an example context
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
[CONCLUSION] The HM techniques are likely required to overcome data scarcity and sparsity issues in broad medical settings. Developing these techniques requires dedicated interdisciplinary teams.
[OBJECTIVES] We discuss challenges using computational modeling approaches for personalized prediction in clinical practice to predict treatment response for rare diseases treated by novel therapies u
APA
Sirlanci M, Albers D, et al. (2026). Navigating the landscape of personalized oncology: overcoming challenges and expanding horizons with computational modeling.. Journal of the American Medical Informatics Association : JAMIA, 33(1), 242-251. https://doi.org/10.1093/jamia/ocaf144
MLA
Sirlanci M, et al.. "Navigating the landscape of personalized oncology: overcoming challenges and expanding horizons with computational modeling.." Journal of the American Medical Informatics Association : JAMIA, vol. 33, no. 1, 2026, pp. 242-251.
PMID
40907963 ↗
Abstract 한글 요약
[OBJECTIVES] We discuss challenges using computational modeling approaches for personalized prediction in clinical practice to predict treatment response for rare diseases treated by novel therapies using clinical oncology as an example context. Several challenges are discussed, including data scarcity, data sparsity, and difficulties in establishing interdisciplinary teams. Machine learning (ML), mechanistic modeling (MM), and hybrid modeling (HM) are discussed in the context of these challenges.
[MATERIALS AND METHODS] We present an HM approach, combining ML and MM techniques for improved personalized model estimation in the context of chimeric antigen receptor T-cell therapy for aggressive lymphoma.
[RESULTS] The HM approach improved the root mean squared error by 61.27±23.21% compared to using MM alone (MM: 2.36*105∓1.68*105and HM: 9.57*104∓8.37*104, where the units are in cells), computed from 13 patients included in this study.
[DISCUSSION] By exploiting the complementary strengths of ML and MM approaches, the developed HM method addresses common limitations such as data scarcity and sparsity in medical settings, especially common for rare diseases.
[CONCLUSION] The HM techniques are likely required to overcome data scarcity and sparsity issues in broad medical settings. Developing these techniques requires dedicated interdisciplinary teams.
[MATERIALS AND METHODS] We present an HM approach, combining ML and MM techniques for improved personalized model estimation in the context of chimeric antigen receptor T-cell therapy for aggressive lymphoma.
[RESULTS] The HM approach improved the root mean squared error by 61.27±23.21% compared to using MM alone (MM: 2.36*105∓1.68*105and HM: 9.57*104∓8.37*104, where the units are in cells), computed from 13 patients included in this study.
[DISCUSSION] By exploiting the complementary strengths of ML and MM approaches, the developed HM method addresses common limitations such as data scarcity and sparsity in medical settings, especially common for rare diseases.
[CONCLUSION] The HM techniques are likely required to overcome data scarcity and sparsity issues in broad medical settings. Developing these techniques requires dedicated interdisciplinary teams.
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🏷️ 같은 키워드 · 무료전문 — 이 논문 MeSH/keyword 기반
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