在固体和液体瘤中,与癌症相关的疲劳相关的特征和预测因素
Poolakkad S Satheeshkumar1, Roberto Pili2, Joel B Epstein3
1Division of Hematology and Oncology, Department of Medicine, University at Buffalo, Buffalo, NY, 14203, USA. spoolakk@buffalo.edu.
Journal of cancer research and clinical oncology
|August 4, 2023
概括
机器学习有效地预测癌症相关疲劳 (CRF) 患者的固体和液体瘤. 这种方法确定了关键的风险因素和改善患者管理的结果.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 机器学习 机器学习
背景情况:
- 与癌症相关的疲劳 (CRF) 是癌症护理中的重要并发症,风险因素不明.
- 有效预测CRF对于管理患者的治疗结果和治疗负担至关重要.
研究的目的:
- 在固体和液体癌症中调查与CRF相关的临床特征.
- 应用机器学习 (ML) 模型来预测住院癌症患者的CRF.
主要方法:
- 利用了2017年国家住院患者样本数据库.
- 采用了通用线性模型和ML技术 (拉索,,随机森林) 来构建预测模型.
- 评估了CRF与疾病负担和不同癌症类型的死亡率之间的关联.
主要成果:
- 机器学习模型表现出强大的预测性能,具有高的AUC值 (例如,前列腺癌中拉索/的0.91).
- CRF与白血病和多发性骨髓瘤的疾病负担增加以及前列腺癌的死亡率增加有关.
- 该研究分析了前列腺癌,白血病,多发性髓瘤和CLOP癌症队列中超过34万名患者的数据.
结论:
- 机器学习为评估CRF风险和不同癌症群体的结果提供了一个强大的平台.
- ML方法在固体和液体瘤中预测CRF方面显著有前途,有助于临床决策.
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