e.g. mhealth
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Skip search results from other journals and go to results- 2 JMIR Formative Research
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Medical dialogue references for these methods were sourced from the web and developed by experts. Furthermore, we collected 1000 questions on genetic counseling through crowdsourcing and carefully selected 120 questions for assessment of the JGCLLM. Two certified genetic counselors and 1 ophthalmologist (SK, YU, and AY) were tasked with evaluating the response of the JGCLLM to these questions. The JGCLLMs were domain adapted using various combinations of methods.
JMIR Med Inform 2025;13:e65047
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We annotated lifestyle management utterances (for the HFDM cohort) with dialogue acts to describe dialogue structure. Dialogue acts reflect high-level communication actions that a speaker makes through the utterance, such as exchanging information, understanding information, performing action, evaluation of health condition, or social-emotional utterances [35]. We conducted an interrater reliability test of the dialogue acts annotated by 3 annotators (LC, SUMS, and HL) using Fleiss kappa.
J Med Internet Res 2024;26:e46983
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The computational power (ie, how fast a system can process data and perform a computational task) of m Health tools can provide unique functions compared to conventional interventions, such as providing personalized behavior change recommendations and delivering them in real time with the support of dialogue systems. It is also worth noting that m Health interventions differ from in-person interventions in the resources they deliver.
JMIR Res Protoc 2023;12:e39093
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Further specific aims are to explore how many questions each dialogue contains and the time of day the chatbot is used. We subcategorized questions that led to a fallback message from the chatbot to obtain a deeper understanding of which type of questions the chatbot was unable to answer. This knowledge may provide insight into the use of health chatbots and potentially establish more general theoretical knowledge on this type of chatbot.
JMIR Form Res 2022;6(4):e28091
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The purpose of the dialogue classifier was to detect when the doctor and patient were engaging in conversation. The input of the classifier was the audio captured by the doctor's computer’s microphone, and the output was a binary classification of the doctor-patient conversation: no dialogue or dialogue.
We used a library based on the web RTC voice activity detection engine (an open source project maintained by the Google Web RTC team [54]).
J Med Internet Res 2021;23(5):e25218
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