背景:通常作为支持性护理提供,治疗师主导的在线支持小组(OSGs)是一种经济有效的方式,可以为受癌症影响的个体提供支持.成功的OSG会话的一个重要指标是组凝聚力;然而,由于在基于文本的OSGs中缺乏非语言线索和面对面互动,因此监控小组凝聚力可能具有挑战性。基于人工智能的联合促进者(AICF)旨在根据上下文从对话中识别治疗结果并产生实时分析。
目的:本研究的目的是开发一种方法来训练和评估AICF监测群体凝聚力的能力。
方法:AICF使用文本分类方法来提取对话中对群体凝聚力的提及。样本数据由人类得分手注释,作为训练数据构建分类模型。还通过使用单词嵌入模型找到上下文相似的组内聚表达来进一步支持注释。还将AICF性能与自然语言处理软件语言查询字数(LIWC)进行了比较。
结果:AICF接受了从CancerChatCanada获得的80,000条消息的培训。我们在34,048条消息上测试了AICF。人类专家对6797(20%)的消息进行了评分,以评估AICF对群体凝聚力进行分类的能力。结果表明,结合人工输入的机器学习算法可以检测群体内聚性,有效OSGs的临床意义指标。经过人工输入的再培训,AICF的F1评分为0.82。与LIWC相比,AICF在识别群体凝聚力方面的表现略好。
结论:AICF有可能通过检测适合实时干预的群体中的不和谐来协助治疗师。总的来说,AICF提供了一个独特的机会,通过关注个人需求,在基于网络的环境中加强以患者为中心的护理。
■RR2-10.2196/21453。
BACKGROUND: Commonly offered as supportive care, therapist-led online support groups (OSGs) are a cost-effective way to provide support to individuals affected by cancer. One important indicator of a successful OSG session is group cohesion; however, monitoring group cohesion can be challenging due to the lack of nonverbal cues and in-person interactions in text-based OSGs. The Artificial Intelligence-based Co-Facilitator (AICF) was designed to contextually identify therapeutic outcomes from conversations and produce real-time analytics.
OBJECTIVE: The aim of this study was to develop a method to train and evaluate AICF\'s capacity to monitor group cohesion.
METHODS: AICF used a text classification approach to extract the mentions of group cohesion within conversations. A sample of data was annotated by human scorers, which was used as the training data to build the classification model. The annotations were further supported by finding contextually similar group cohesion expressions using word embedding models as well. AICF performance was also compared against the natural language processing software Linguistic Inquiry Word Count (LIWC).
RESULTS: AICF was trained on 80,000 messages obtained from Cancer Chat Canada. We tested AICF on 34,048 messages. Human experts scored 6797 (20%) of the messages to evaluate the ability of AICF to classify group cohesion. Results showed that machine learning algorithms combined with human input could detect group cohesion, a clinically meaningful indicator of effective OSGs. After retraining with human input, AICF reached an F1-score of 0.82. AICF performed slightly better at identifying group cohesion compared to LIWC.
CONCLUSIONS: AICF has the potential to assist therapists by detecting discord in the group amenable to real-time intervention. Overall, AICF presents a unique opportunity to strengthen patient-centered care in web-based settings by attending to individual needs.
UNASSIGNED: RR2-10.2196/21453.