Building Extractive Question Answering System to Support Human-AI Health
  Coaching Model for Sleep Domain
Iva Bojic Qi Chwen Ong Shafiq Joty Josip Car
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).