Machine-learning-derived classifier predicts absence of persistent pain after breast cancer surgery with high accuracy
Machine-learning-derived classifier predicts absence of persistent pain after breast cancer surgery with high accuracy
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DOI：
10.1007/s10549-018-4841-8
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发表时间：
2018-09-01
影响因子：
3.8
通讯作者：
Ultsch, Alfred
中科院分区：
文献类型：
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作者：
Loetsch, Joern;Sipila, Reetta;Ultsch, Alfred
通过早期识别高危患者来预防乳腺癌术后持续性疼痛是临床需要。有监督的机器学习被用来识别预测显著pains.Methods的持续性的参数超过500人口统计学，临床和心理学参数获得长达6个月后，从1,000名妇女（年龄28-75岁）谁是乳腺癌治疗。在手术前和第1、6、12、24和36个月时，使用11点数字评定量表评估疼痛。第12、24和36个月的评级用于将患者分配到“持续性疼痛”或“非持续性疼痛”组。无监督的机器学习被应用到映射的参数，这些diagnosis.Results的符号规则为基础的分类工具，包括21个单一或聚合的参数，包括人口统计学特征，心理和疼痛相关的参数，形成一个问卷调查与“是/否”的项目（决策规则）。如果应用21条规则中的至少10条，则预测持续性疼痛的交叉验证准确率为86%，阴性预测值约为95%.Conclusions目前的机器学习分析表明，即使从大型队列中获取大量参数，这些患者的早期识别也仅部分成功。这表明需要更多的参数来准确预测持续性疼痛。然而，根据目前的参数，几乎95%的确定性可以排除正在接受乳腺癌治疗的女性发生持续性疼痛的可能性。
Background Prevention of persistent pain following breast cancer surgery, via early identification of patients at high risk, is a clinical need. Supervised machine-learning was used to identify parameters that predict persistence of significant pain.Methods Over 500 demographic, clinical and psychological parameters were acquired up to 6 months after surgery from 1,000 women (aged 28-75 years) who were treated for breast cancer. Pain was assessed using an 11-point numerical rating scale before surgery and at months 1, 6, 12, 24, and 36. The ratings at months 12, 24, and 36 were used to allocate patents to either "persisting pain" or "non-persisting pain" groups. Unsupervised machine learning was applied to map the parameters to these diagnoses.Results A symbolic rule-based classifier tool was created that comprised 21 single or aggregated parameters, including demographic features, psychological and pain-related parameters, forming a questionnaire with "yes/no" items (decision rules). If at least 10 of the 21 rules applied, persisting pain was predicted at a cross-validated accuracy of 86% and a negative predictive value of approximately 95%.Conclusions The present machine-learned analysis showed that, even with a large set of parameters acquired from a large cohort, early identification of these patients is only partly successful. This indicates that more parameters are needed for accurate prediction of persisting pain. However, with the current parameters it is possible, with a certainty of almost 95%, to exclude the possibility of persistent pain developing in a woman being treated for breast cancer.