Patients who effectively manage their symptoms often demonstrate higher levels of engagement in conversations and interventions with healthcare practitioners. This engagement is multifaceted, encompassing cognitive and socio-affective dimensions. Consequently, it is crucial for AI systems to understand the engagement in natural conversations between patients and practitioners to better contribute toward patient care. In this paper, we present a novel dataset (MedNgage), which consists of patient-nurse conversations about cancer symptom management. We manually annotate the dataset with a novel framework of categories of patient engagement from two different angles, namely: i) socio-affective (3.1K spans), and ii) cognitive use of language (1.8K spans). Through statistical analysis of the data that is annotated using our framework, we show a positive correlation between patient symptom management outcomes and their engagement in conversations. Additionally, we demonstrate that pre-trained transformer models fine-tuned on our dataset can reliably predict engagement classes in patient-nurse conversations. Lastly, we use LIME (Ribeiro et al., 2016) to analyze the underlying challenges of the tasks that state-of-the-art transformer models encounter. The de-identified data is available for research purposes upon request.

翻译：有效管理自身症状的患者通常在与医疗从业者的对话和干预中表现出更高的参与度。这种参与度是多维度的，涵盖认知和社会情感层面。因此，人工智能系统理解患者与从业者自然对话中的参与度对于更好地促进患者护理至关重要。本文提出一个新型数据集（MedNgage），该数据集包含关于癌症症状管理的患者-护士对话。我们采用一种新型框架，从两个不同角度手动标注数据集中的患者参与类别，即：i) 社会情感维度（3.1K个片段），及ii) 认知语言使用维度（1.8K个片段）。通过基于该框架标注数据的统计分析，我们发现患者症状管理结果与对话参与度之间存在正相关。此外，我们证明，基于该数据集微调的预训练Transformer模型能够可靠预测患者-护士对话中的参与度类别。最后，我们使用LIME方法（Ribeiro等人，2016）分析当前最先进Transformer模型所面临任务的潜在挑战。经去标识化的数据可根据研究需求申请获取。