Machine learning for detection of lymphedema among breast cancer survivors.
Machine learning for detection of lymphedema among breast cancer survivors.
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DOI：
10.21037/mhealth.2018.04.02
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发表时间：
2018-01-01
期刊：
影响因子：
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通讯作者：
Cheung, Ying Kuen
中科院分区：
文献类型：
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作者：
Fu, Mei R;Wang, Yao;Cheung, Ying Kuen
背景：在数字时代，移动医疗已成为医疗保健的重要场所，计算机科学的应用，如机器学习，已被证明是医疗保健检测或预测各种医疗状况的强大工具，通过提供比传统统计或基于专家的系统更高的准确性。症状通常是由于疾病或药物治疗副作用引起的身体功能异常变化的指标。实时症状报告是指报告患者在报告时所经历的症状。结合以患者为中心的实时症状报告和实时临床分析，使用机器学习进行实时精确预测，可能会改善淋巴水肿的早期发现，并为面临终身淋巴水肿风险的乳腺癌幸存者提供长期临床决策支持。淋巴水肿与20多种令人痛苦的症状有关，是乳腺癌治疗后期最令人痛苦和可怕的副作用之一。目前还没有治愈淋巴水肿的方法，但早期发现可以帮助患者及时接受干预，有效地控制淋巴水肿。由于淋巴水肿可以在癌症手术后立即发生，也可以在手术后20年发生，因此使用机器学习实时检测淋巴水肿对于实现及时检测至关重要，可以降低淋巴水肿进展为慢性或严重阶段的风险。本研究评估了基于实时症状报告的机器学习算法检测淋巴水肿状态的准确性、敏感性和特异性。方法：开展了一项基于网络的研究，利用移动健康系统收集患者对症状的实时报告。收集有关人口统计学和临床信息、淋巴水肿状况和症状特征的数据。来自美国45个州的355名患者完成了这项研究。执行统计和机器学习程序进行数据分析。比较了C4.5决策树、C5.0决策树、梯度增强模型（GBM）、人工神经网络（ANN）和支持向量机（SVM）这五种著名的机器学习分类算法的性能。每种分类算法都有一定的用户可定义的超参数。采用五重交叉验证对这些超参数进行优化，并选择平均交叉验证精度最高的参数。结果：利用机器学习程序比较不同算法是可行的。人工神经网络检测淋巴水肿的准确率为93.75%，灵敏度为95.65%，特异性为91.03%。结论：基于实时症状报告的训练良好的神经网络分类器可以提供高度准确的淋巴水肿检测。这种检测精度明显高于目前常用的临床方法，如生物阻抗分析。使用训练有素的分类算法来检测基于症状特征的淋巴水肿是一个非常有前途的工具，可以改善淋巴水肿的结果。
BACKGROUND: In the digital era when mHealth has emerged as an important venue for health care, the application of computer science, such as machine learning, has proven to be a powerful tool for health care in detecting or predicting various medical conditions by providing improved accuracy over conventional statistical or expert-based systems. Symptoms are often indicators for abnormal changes in body functioning due to illness or side effects from medical treatment. Real-time symptom report refers to the report of symptoms that patients are experiencing at the time of reporting. The use of machine learning integrating real-time patient-centered symptom report and real-time clinical analytics to develop real-time precision prediction may improve early detection of lymphedema and long term clinical decision support for breast cancer survivors who face lifelong risk of lymphedema. Lymphedema, which is associated with more than 20 distressing symptoms, is one of the most distressing and dreaded late adverse effects from breast cancer treatment. Currently there is no cure for lymphedema, but early detection can help patients to receive timely intervention to effectively manage lymphedema. Because lymphedema can occur immediately after cancer surgery or as late as 20 years after surgery, real-time detection of lymphedema using machine learning is paramount to achieve timely detection that can reduce the risk of lymphedema progression to chronic or severe stages. This study appraised the accuracy, sensitivity, and specificity to detect lymphedema status using machine learning algorithms based on real-time symptom report.METHODS: A web-based study was conducted to collect patients' real-time report of symptoms using a mHealth system. Data regarding demographic and clinical information, lymphedema status, and symptom features were collected. A total of 355 patients from 45 states in the US completed the study. Statistical and machine learning procedures were performed for data analysis. The performance of five renowned classification algorithms of machine learning were compared: Decision Tree of C4.5, Decision Tree of C5.0, gradient boosting model (GBM), artificial neural network (ANN), and support vector machine (SVM). Each classification algorithm has certain user-definable hyper parameters. Five-fold cross validation was used to optimize these hyper parameters and to choose the parameters that led to the highest average cross validation accuracy.RESULTS: Using machine leaning procedures comparing different algorithms is feasible. The ANN achieved the best performance for detecting lymphedema with accuracy of 93.75%, sensitivity of 95.65%, and specificity of 91.03%.CONCLUSIONS: A well-trained ANN classifier using real-time symptom report can provide highly accurate detection of lymphedema. Such detection accuracy is significantly higher than that achievable by current and often used clinical methods such as bio-impedance analysis. Use of a well-trained classification algorithm to detect lymphedema based on symptom features is a highly promising tool that may improve lymphedema outcomes.