[2309.15039] Can-SAVE: Deploying Low-Cost and Population-Scale Cancer Screening via Survival Analysis Variables and EHR Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2309.15039 (cs) [Submitted on 26 Sep 2023 ( v1 ), last revised 2 Dec 2025 (this version, v4)] Title: Can-SAVE: Deploying Low-Cost and Population-Scale Cancer Screening via Survival Analysis Variables and EHR Authors: Petr Philonenko , Vladimir Kokh , Pavel Blinov View a PDF of the paper titled Can-SAVE: Deploying Low-Cost and Population-Scale Cancer Screening via Survival Analysis Variables and EHR, by Petr Philonenko and 2 other authors View PDF HTML (experimental) Abstract: Conventional medical cancer screening methods are costly, labor-intensive, and extremely difficult to scale. Although AI can improve cancer detection, most systems rely on complex or specialized medical data, making them impractical for large-scale screening. We introduce Can-SAVE, a lightweight AI system that ranks population-wide cancer risks solely based on medical history events. By integrating survival model outputs into a gradient-boosting framework, our approach detects subtle, long-term patient risk patterns - often well before clinical symptoms manifest. Can-SAVE was rigorously evaluated on a real-world dataset of 2.5 million adults spanning five Russian regions, marking the study as one of the largest and most comprehensive deployments of AI-driven cancer risk assessment. In a retrospective oncologist-supervised study over 1.9M patients, Can-SAVE achieves a 4-10x higher detection rate at identical screening volumes and an Average Precision (AP) of 0.228 vs. 0.193 for the best baseline (LoRA-tuned Qwen3-Embeddings via DeepSeek-R1 summarization). In a year-long prospective pilot (426K patients), our method almost doubled the cancer detection rate (+91%) and increased population coverage by 36% over the national screening protocol. The system demonstrates practical scalability: a city-wide population of 1 million patients can be processed in under three hours using standard hardware, enabling seamless clinical integration. This work proves that Can-SAVE achieves nationally significant cancer detection improvements while adhering to real-world public healthcare constraints, offering immediate clinical utility and a replicable framework for population-wide screening. Code for training and feature engineering is available at this https URL . Comments: Accepted to the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2026) Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Applications (stat.AP) Cite as: arXiv:2309.15039 [cs.LG] (or arXiv:2309.15039v4 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2309.15039 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Proc. 32nd ACM SIGKDD Conf. on Knowledge Discovery and Data Mining (KDD '26), 2026, 11 pages Related DOI : https://doi.org/10.1145/3770854.3783930 Focus to learn more DOI(s) linking to related resources Submission history From: Petr Philonenko [ view email ] [v1] Tue, 26 Sep 2023 16:15:54 UTC (331 KB) [v2] Fri, 27 Sep 2024 09:40:34 UTC (2,358 KB) [v3] Sun, 30 Nov 2025 21:08:22 UTC (675 KB) [v4] Tue, 2 Dec 2025 13:59:29 UTC (675 KB) Full-text links: Access Paper: View a PDF of the paper titled Can-SAVE: Deploying Low-Cost and Population-Scale Cancer Screening via Survival Analysis Variables and EHR, by Petr Philonenko and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2023-09 Change to browse by: cs cs.AI stat stat.AP References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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