Specific medical cancer screening methods are often costly, time-consuming, and weakly applicable on a large scale. Advanced Artificial Intelligence (AI) methods greatly help cancer detection but require specific or deep medical data. These aspects prevent the mass implementation of cancer screening methods. For this reason, it is a disruptive change for healthcare to apply AI methods for mass personalized assessment of the cancer risk among patients based on the existing Electronic Health Records (EHR) volume. This paper presents a novel Can-SAVE cancer risk assessment method combining a survival analysis approach with a gradient-boosting algorithm. It is highly accessible and resource-efficient, utilizing only a sequence of high-level medical events. We tested the proposed method in a long-term retrospective experiment covering more than 1.1 million people and four regions of Russia. The Can-SAVE method significantly exceeds the baselines by the Average Precision metric of 22.8%$\pm$2.7% vs 15.1%$\pm$2.6%. The extensive ablation study also confirmed the proposed method's dominant performance. The experiment supervised by oncologists shows a reliable cancer patient detection rate of up to 84 out of 1000 selected. Such results surpass the medical screening strategies estimates; the typical age-specific Number Needed to Screen is only 9 out of 1000 (for colorectal cancer). Overall, our experiments show a 4.7-6.4 times improvement in cancer detection rate (TOP@1k) compared to the traditional healthcare risk estimation approach.

翻译：特定的医学癌症筛查方法通常成本高昂、耗时且难以大规模应用。先进的人工智能方法极大地助力了癌症检测，但需要特定或深度的医疗数据。这些因素阻碍了癌症筛查方法的大规模实施。因此，基于现有电子健康记录数据，应用人工智能方法对患者进行大规模个性化癌症风险评估，是医疗保健领域的一项颠覆性变革。本文提出了一种新颖的Can-SAVE癌症风险评估方法，该方法将生存分析方法与梯度提升算法相结合。它易于获取且资源高效，仅需利用一系列高层级医疗事件序列。我们在一个覆盖超过110万人及俄罗斯四个地区的长期回顾性实验中测试了所提出的方法。Can-SAVE方法在平均精确率指标上显著超越基线模型，达到22.8%±2.7%对比15.1%±2.6%。广泛的消融研究也证实了所提出方法的优越性能。由肿瘤学家监督的实验显示，在每1000名被选中的个体中，可靠的癌症患者检出率高达84例。这一结果超越了医学筛查策略的预估；典型的年龄特异性需筛查人数仅为每1000人中有9例（针对结直肠癌）。总体而言，我们的实验表明，与传统医疗保健风险评估方法相比，癌症检出率（TOP@1k）提高了4.7至6.4倍。