期刊文章%@ 1438-8871 %I Gunther Eysenbach %V 8 %N 4 %P e30 %T用于实时健康数据收集的口语对话技术评估%A Levin,Esther %A Levin,Alex %+计算机科学系，纽约城市学院，计算机科学系，NAC大楼，7-312室，138街和Convent大道，纽约10031，美国，+1 973 568 5843，esther@cs.ccny.cuny.edu %K人为因素%K生态即时评估%K数据收集%K语音识别%D 2006 %7 11.12.2006 %9原始论文%J J医学互联网Res %G英文%X背景:对患者体验的实时评估是卫生保健、生活质量、行为科学、新药物和治疗开发等研究的重要方法。生态瞬时评估是一种实时评估受试者在自然环境中的经验和行为的方法。最近，采用了电子数据收集技术，包括利用交互式语音响应的系统。目的:本项目的目的是评估用于从患者收集健康、行为和生活方式研究和监测的实时数据的口语对话方法。虽然数据收集过程的管理是基于互联网的，但这一额外的eHealth通信渠道是基于电话自然语言对话，并使用自动语音识别技术的对话系统。在本研究中，我们实施了一个对话系统，用于患者对慢性疼痛的评估和监测。方法:对24名志愿者进行疼痛监测语音日记的可用性实验评价。志愿者被要求在2周的时间内与该系统进行10次交流;实际上，每个科目的课数从1到20不等。 The subjects were asked to either relate to pain episodes in their past while answering the system’s questions, or use as a guidance one of nine provided medical scenarios compiled by a pain specialist, ranging from migraines and back pain to post-surgical pain (knee injury) and cancer- and chemotherapy-related afflictions. Results: From 24 volunteers, we collected a total of 177 dialogue sessions: 171 sessions were completed, while the caller hung up in the other 6 sessions. There were a total of 2437 dialogue turns, where a dialogue turn corresponds to one system prompt and one user utterance. The data capture rate, measuring the percentage of slots filled automatically, was 98%, while the other 2% were flagged for transcription. Among the utterances sent to transcription, where the user had opted for the “none of those” option, 70% corresponded to the “type of pain” slot, 20% to the “symptoms” slot, and 10% to the “body part” slot, indicating that those are the grammars with the highest out-of-vocabulary rate. Conclusions: The results of this feasibility study indicated that desired accuracy of data can be achieved with a high degree of automation (98% in the study) and that the users were indeed capable of utilizing the flexible interface, the sessions becoming more and more efficient as users’ experience increased, both in terms of session duration and avoidance of troublesome dialogue situations. %R 10.2196/jmir.8.4.e30 %U //www.mybigtv.com/2006/4/e30/ %U https://doi.org/10.2196/jmir.8.4.e30