小编导读

精神病院的暴力风险评估可以帮助医护人员采取干预措施，从而避免暴力事件。由执业医师撰写的临床笔记和电子健康记录是获取独特信息的宝贵资源，但很少被充分利用。来自荷兰Utrecht University、 Eindhoven University of Technology、University Medical Center Utrecht、Leiden University Medical Center、 Leiden Institute of Advanced Computer Science 的学者们在期刊Journal of Artificial Intelligence of Medical Sciences(eISSN 2666-1470)上发表了题为“Machine Learning for Violence Risk Assessment Using Dutch Clinical Notes”的文章，研究了利用医生临床记录进行评估精神病患者暴力风险的机器学习方法。

要点介绍

在荷兰临床精神病学机构工作的约三分之二的精神卫生专业人员报告说，他们的职业生涯中至少经历过一次身体暴力事件。这些事件会对从业者产生强烈的心理影响，也会给卫生机构带来经济损失。目前，已经有研究提出了多种暴力风险评估（VRA）方法来预测和避免暴力事件，并且应用到了实践中。传统上，常用的方法是Brøset暴力检查表（BVC），即护士和精神病医生使用问卷调查来评估患者卷入暴力事件的可能性。但填写表格是一个耗时且高度主观的过程。

机器学习方法则可以通过节省时间和使预测更加准确来改进这一过程。电子健康记录（EHR）是一个包含结构化字段和书面笔记的丰富信息源。EHR笔记结合暴力事件报告可以用来训练机器学习模型，将笔记分类为描述潜在暴力患者的笔记。

在本研究中，我们重点探讨使用医生临床笔记进行精神病患者暴力风险评估的机器学习方法。我们发现深度学习模型BERTje的表现比传统的机器学习方法差。此外，我们还评估了实验数据和分类器，以更好地了解实验模型的性能。

图1. 用于超参数调整和统计不确定性估计的数据处理管道的示意图

图3. Kappa曲线为我们的支持向量机（SVM）分类器的外部交叉验证循环的迭代之一。该图中的最大值可以解释为分类器的最佳工作点。对于所有假阳性率，随机猜测结果Cohen's kappa等于0。

图4. 阳性/暴力（左/红色）和阴性/非暴力（右/蓝色）入院期的每个周期的字数直方图注释（顶部）和入院期开始时的年龄（底部）。

研究结论：我们使用荷兰乌得勒支UMC精神病学病房的荷兰语临床笔记，将传统和深度机器学习方法应用于VRA问题。与传统的机器学习方法相比，我们发现使用BERTje并没有改善；事实上，BERTje的结果更差，AUC≈0.66。原因可能是我们使用的数据集很小，不足以微调BERTje中存在的大量参数。为了扩大数据集，来自多个机构的数据可以通过联合学习进行聚合，不同机构训练相同的中心模型，而不相互共享数据；考虑到隐私限制，这一点非常重要。此外，使用来自不同医学领域的荷兰临床笔记，预先培训“医学BERTje”模型将非常有益。最后，我们工作中的一个关键假设是，未报告的暴力事件数量很少。这个假设的有效性需要仔细检查。需要进一步的工作来完善从临床笔记中选择未报告事件的过程。这也有助于在实践中重新评估暴力事件报告。我们已经看到，从业者在报告暴力事件时可能非常主观。采用机器学习的统一事件报告策略可以显著提高数据质量。

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原文信息

P. Mosteiro , E. Rijcken, K. Zervanou, U. Kaymak, F. Scheepers, M. Spruit， "Machine Learning for Violence Risk Assessment Using Dutch Clinical Notes", Journal of Artificial Intelligence for Medical Sciences, 2021, DOI: 10.2991/jaims.d.210225.001.

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