Automating annotation of information-giving for analysis of clinical conversation.
Automating annotation of information-giving for analysis of clinical conversation.
复制标题
自动注释信息提供以分析临床对话。
DOI：
10.1136/amiajnl-2013-001898
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
PensteinRosé,Carolyn
中科院分区：
文献类型：
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作者：
Mayfield,Elijah;Laws,MBarton;Wilson,IraB;PensteinRosé,Carolyn
目的对诸如言语行为等细粒度特征的临床交流进行编码已经产生了大量的文献。然而，人工标注既费力又昂贵，限制了这些方法的应用。我们的目的是证明通过机器学习，计算机可以以足够的可靠性对某些类别的言语行为进行编码，从而对临床遭遇进行有用的区分。材料和方法数据来自415名HIV患者的常规门诊记录，这些记录之前使用通用医疗交互分析系统(GMIAS)对言语行为进行了编码；50个使用接触结构综合分析系统(CASE)对更大规模的特征进行了编码。我们将选择的言语行为聚合成信息给予和请求，然后训练机器使用Logistic回归分类进行自动标注。我们通过每个言语行为的准确性来评估可靠性。结果自动编码与人工编码具有中等的可靠性(准确率为71.2%，κ=0.5 7)，机器和人类对信息提供比的预测高度相关(r=0.96)。回归分析对患者报告的5项沟通质量指标中的4项有显著预测作用(r=0.263-0.344)。讨论信息传递率是预测患者对提供者-患者沟通质量感知的有用和直观的指标。这些预测可以通过自动注释进行，这是研究大量临床接触的一个实用选择，具有客观性、一致性和低成本，为护理人员提供了更多的培训和反思机会。
ObjectiveCoding of clinical communication for fine-grained features such as speech acts has produced a substantial literature. However, annotation by humans is laborious and expensive, limiting application of these methods. We aimed to show that through machine learning, computers could code certain categories of speech acts with sufficient reliability to make useful distinctions among clinical encounters.Materials and methodsThe data were transcripts of 415 routine outpatient visits of HIV patients which had previously been coded for speech acts using the Generalized Medical Interaction Analysis System (GMIAS); 50 had also been coded for larger scale features using the Comprehensive Analysis of the Structure of Encounters System (CASES). We aggregated selected speech acts into information-giving and requesting, then trained the machine to automatically annotate using logistic regression classification. We evaluated reliability by per-speech act accuracy. We used multiple regression to predict patient reports of communication quality from post-visit surveys using the patient and provider information-giving to information-requesting ratio (briefly, information-giving ratio) and patient gender.ResultsAutomated coding produces moderate reliability with human coding (accuracy 71.2%, κ=0.57), with high correlation between machine and human prediction of the information-giving ratio (r=0.96). The regression significantly predicted four of five patient-reported measures of communication quality (r=0.263–0.344).DiscussionThe information-giving ratio is a useful and intuitive measure for predicting patient perception of provider–patient communication quality. These predictions can be made with automated annotation, which is a practical option for studying large collections of clinical encounters with objectivity, consistency, and low cost, providing greater opportunity for training and reflection for care providers.