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 engagement (3.1K spans), and ii) cognitive engagement (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 categories 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水平的参与度。这种参与是多维的，涵盖认知和社会情感维度。因此，AI系统理解患者与从业者之间自然对话中的参与度，对于更好地促进患者护理至关重要。本文提出一个新数据集（MedNgage），包含关于癌症症状管理的患者-护患对话。我们采用一种新的分类框架，从两个不同角度手动标注数据集中的患者参与类别，即：i) 社会情感参与（3100个跨度），和 ii) 认知参与（1800个跨度）。通过使用我们框架标注数据的统计分析，我们展示了患者症状管理结果与其对话参与度之间的正相关性。此外，我们证明，在我们的数据集上微调的预训练Transformer模型能够可靠地预测患者-护患对话中的参与类别。最后，我们使用LIME（Ribeiro等人，2016）分析了最先进Transformer模型在任务中遇到的潜在挑战。去标识化数据可根据研究需求提供。