Building Extractive Question Answering System to Support Human-AI Health Coaching Model for Sleep Domain
This work addresses the need for domain-specific AI tools in health coaching to aid behavior change, though it is incremental as it builds on existing BERT models and focuses on a specific domain.
The paper tackled the problem of supporting preventive healthcare for non-communicable diseases by developing an extractive question answering system for sleep-focused health coaching, which performed well in human evaluations despite not surpassing baseline in automatic tests.
Non-communicable diseases (NCDs) are a leading cause of global deaths, necessitating a focus on primary prevention and lifestyle behavior change. Health coaching, coupled with Question Answering (QA) systems, has the potential to transform preventive healthcare. This paper presents a human-Artificial Intelligence (AI) health coaching model incorporating a domain-specific extractive QA system. A sleep-focused dataset, SleepQA, was manually assembled and used to fine-tune domain-specific BERT models. The QA system was evaluated using automatic and human methods. A data-centric framework enhanced the system's performance by improving passage retrieval and question reformulation. Although the system did not outperform the baseline in automatic evaluation, it excelled in the human evaluation of real-world questions. Integration into a Human-AI health coaching model was tested in a pilot Randomized Controlled Trial (RCT).