An Evolutionary Bootstrapping Development Approach for a Mental Health Conversational Agent.

Area of Science:

• Digital Health
• Artificial Intelligence in Mental Healthcare
• Conversational Agent Development

Background:

• Conversational agents are increasingly utilized for mental health screening, assessment, diagnosis, and treatment.
• Existing digital mental health tools face challenges in scalability and evidence-based care delivery.

Purpose of the Study:

• To propose a bootstrapping approach for developing sophisticated digital mental health conversational agents (chatbots).
• To create specialized chatbots for assessing and pre-diagnosing specific mental health disorders using machine learning and natural language processing.

Main Methods:

• Initiating with a basic rule-based expert system and iteratively enhancing it.
• Incorporating user feedback from psychiatrists and patients in each design iteration.
• Employing a 'survival of the fittest' strategy to refine chatbot development and selection.

Main Results:

• The iterative bootstrapping process allows for the development of specialized mental health chatbots.
• User feedback integration ensures clinical relevance and patient-centered design.
• The 'survival of the fittest' approach facilitates the optimization of chatbot performance and utility.

Conclusions:

• The proposed bootstrapping approach offers a unique and novel method for developing effective conversational mental health agents.
• This methodology has the potential to scale mental healthcare services and reduce provider burden.
• The ultimate goal is to create smart chatbots capable of delivering evidence-based mental healthcare.