These results validate the feasibility of applying artificial intelligence to Brazilian hospital oncology records, demonstrating the potential of these models for future clinical decision-support applications within the Unified Health System (SUS).
Seven survival models representing linear, tree-based, and neural-network techniques were assessed and demonstrated competitive performance compared with published studies, with chemoradiotherapy, extent of resection, MGMT methylation status, age, and performance status identified as the most influential predictors.
Alina Morozova, Elizabeth Ford, Giles Critchley et al.· Neuro-Oncology· 0 citations
OBJECTIVE
To evaluate the performance and interpretability of multiple ML algorithms for survival prediction in prostate cancer (CaP) and to assess whether these approaches can improve upon established clinical risk prediction models.
METHODS
Using the Surveillance, Epidemiology, and End Results (SEER) 17 Database, w...
Isaac E. Kim, Cecile P. G. Meier-Scherling, S. Zhou et al.· Urologic oncology· 0 citations
The analysis revealed that age and genetics were the primary influencing factors in prostate cancer mortality, and support the utility of multimodal risk modeling while highlighting challenges in clinical translation.
Shwas Churi, Bodhayan Prasad, B. Jani· European Journal of Cancer P...· 0 citations
The deep learning-based multi-cancer disease-free survival model performed robustly across cancer subtypes, with the nomogram improving risk stratification, particularly for stage I malignancies, complementing existing staging.
Si-Teng Chen, En-Cheng Zhang, Fu-Kang Sun et al.· Cancers· 0 citations
A multi-tier, explainable AI framework designed to risk-stratify patients and predict overall survival using clinical and genomic covariates is developed and demonstrates that explainable machine learning models can robustly predict survivability and highlight actionable features for oncology dashboards.
This independent study develops and evaluates an endto-end longitudinal diabetes-risk modeling pipeline using twelve years of annual health-check data from 6,892 Thai patients, developing a dual-track recommendation system: a horizonspecific pure-prediction leaderboard for passive patient-risk screening, and an interve...
Pitikorn Khlaisamniang· 0 citations
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