开发一个AI框架以自动检测医患对话中的共享决策
Developing an AI framework to automatically detect shared decision-making in patient-doctor conversations
AI总结：
本研究提出一种基于语言建模和对话对齐分数的自动化AI框架，用于在医患对话中大规模测量共享决策，并通过实验验证了CA分数与SDM结果之间的显著关联。
摘要：
共享决策（SDM）是实现以患者为中心护理的必要条件。目前尚不存在可大规模自动测量SDM的方法。本研究旨在通过使用语言建模和对话对齐（CA）分数，开发一种自动测量SDM的方法。共转录了来自一项随机多中心试验的157段视频记录的医患对话，该试验评估了用于房颤抗凝治疗的SDM决策辅助工具，并将对话分割为42,559个句子。采用上下文-响应对和负采样来训练深度学习（DL）模型，并通过下一句预测（NSP）任务微调BERT模型。每个表现最佳的模型被用于计算四种类型的CA分数。通过按临床医生进行的随机效应分析，调整年龄、性别、种族和试验组别，评估了CA分数与SDM结果之间的关联：决策冲突量表（DCS）和观察患者在决策中的参与度12（OPTION12）分数。使用Benjamini-Hochberg方法对多重比较的p值进行了校正。在157名患者中（34%为女性，平均年龄70岁，标准差10.8），临床医生平均说的话比患者多（1911词对773词）。未采用样式手册策略的DL模型实现了0.227的recall@1，而微调后的BERTbase（110M）实现了最高的recall@1，为0.640。使用未采用样式手册的DL模型生成的AbsMax（18.36，标准误7.74，p=0.025）和Max CA（21.02，标准误7.63，p=0.012）分数与OPTION12相关。使用微调后的BERTbase（110M）生成的Max CA分数与DCS分数相关（-27.61，标准误12.63，p=0.037）。BERT模型规模对CA分数与SDM之间的关联没有影响。本研究引入了一种自动化、可扩展的方法，通过可解释的CA分数来测量医患对话中的SDM，具有大规模评估SDM策略的潜力。
英文摘要：
Shared decision-making (SDM) is necessary to achieve patient-centred care. Currently no methodology exists to automatically measure SDM at scale. This study aimed to develop an automated approach to measure SDM by using language modelling and the conversational alignment (CA) score. A total of 157 video-recorded patient-doctor conversations from a randomized multi-centre trial evaluating SDM decision aids for anticoagulation in atrial fibrillations were transcribed and segmented into 42,559 sentences. Context-response pairs and negative sampling were employed to train deep learning (DL) models and fine-tuned BERT models via the next sentence prediction (NSP) task. Each top-performing model was used to calculate four types of CA scores. A random-effects analysis by clinician, adjusting for age, sex, race, and trial arm, assessed the association between CA scores and SDM outcomes: the Decisional Conflict Scale (DCS) and the Observing Patient Involvement in Decision-Making 12 (OPTION12) scores. p-values were corrected for multiple comparisons with the Benjamini-Hochberg method. Among 157 patients (34% female, mean age 70 SD 10.8), clinicians on average spoke more words than patients (1911 vs 773). The DL model without the stylebook strategy achieved a recall@1 of 0.227, while the fine-tuned BERTbase (110M) achieved the highest recall@1 with 0.640. The AbsMax (18.36 SE7.74 p=0.025) and Max CA (21.02 SE7.63 p=0.012) scores generated with the DL without stylebook were associated with OPTION12. The Max CA score generated with the fine-tuned BERTbase (110M) was associated with the DCS score (-27.61 SE12.63 p=0.037). BERT model sizes did not have an impact the association between CA scores and SDM. This study introduces an automated, scalable methodology to measure SDM in patient-doctor conversations through explainable CA scores, with potential to evaluate SDM strategies at scale.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
