Validation of a Machine Learning Approach for Venous Thromboembolism Risk Prediction in Oncology.
Validation of a Machine Learning Approach for Venous Thromboembolism Risk Prediction in Oncology.
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DOI：
10.1155/2017/8781379
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Roselli M
中科院分区：
文献类型：
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作者：
Ferroni P;Zanzotto FM;Scarpato N;Riondino S;Guadagni F;Roselli M
使用内核机器学习（ML）和随机优化（RO）技术，我们最近开发了一组静脉血栓栓塞（VTE）风险预测因子，这可能有助于设计一个网络界面，用于化疗治疗的癌症患者的VTE风险分层。本研究旨在验证一个模型，其中包含两个最佳预测因子，并将其综合性能与目前推荐的Khorana评分（KS）进行比较。608例癌症门诊患者的年龄、性别、肿瘤部位/分期、血液学属性、血脂、血糖指标、肝肾功能、BMI、体力状态、支持和抗癌药物均输入模型，数值属性分析为连续值。静脉血栓栓塞率为7.1%。组合模型的VTE风险预测性能为2.30阳性似然比（+LR）、0.46阴性LR（−LR）和4.88 HR（95% CI：2.54-9.37），与KS相比有显著改善[HR 1.73（95% CI：0.47-6.37）]。这些结果证实，ML的方法可能是化疗治疗的癌症门诊患者的VTE风险分层的临床价值，并建议ML-RO模型建议可能是有用的设计一个Web服务，能够为医生提供一个图形界面，帮助在关键阶段的决策。
Using kernel machine learning (ML) and random optimization (RO) techniques, we recently developed a set of venous thromboembolism (VTE) risk predictors, which could be useful to devise a web interface for VTE risk stratification in chemotherapy-treated cancer patients. This study was designed to validate a model incorporating the two best predictors and to compare their combined performance with that of the currently recommended Khorana score (KS). Age, sex, tumor site/stage, hematological attributes, blood lipids, glycemic indexes, liver and kidney function, BMI, performance status, and supportive and anticancer drugs of 608 cancer outpatients were all entered in the model, with numerical attributes analyzed as continuous values. VTE rate was 7.1%. The VTE risk prediction performance of the combined model resulted in 2.30 positive likelihood ratio (+LR), 0.46 negative LR (−LR), and 4.88 HR (95% CI: 2.54–9.37), with a significant improvement over the KS [HR 1.73 (95% CI: 0.47–6.37)]. These results confirm that a ML approach might be of clinical value for VTE risk stratification in chemotherapy-treated cancer outpatients and suggest that the ML-RO model proposed could be useful to design a web service able to provide physicians with a graphical interface helping in the critical phase of decision making.