Assessing Large Language Models in Building a Structured Dataset From AskDocs Subreddit Data: Methodological Study.

Area of Science:

• Digital Health
• Natural Language Processing
• Computational Social Science

Background:

• The subreddit r/AskDocs is a key platform for digital health consultations.
• Analyzing unstructured user-generated content from forums like r/AskDocs is challenging.
• Large language models (LLMs) offer advanced tools for extracting health information from social media.

Purpose of the Study:

• To evaluate the efficacy of LLMs in transforming unstructured r/AskDocs data into a structured format.
• To compare LLM data extraction performance against human annotators.
• To assess the alignment of LLM-based data extraction with human cognitive processes.

Main Methods:

• Data extraction from 2800 r/AskDocs posts using human annotators (medical students) and LLMs.
• Human annotation included demographics, inquiry type, proxy relationship, chronic conditions, and consultation status.
• LLM data extraction utilized engineered prompts (JSON, few-shot) with models like Llama 3, Genna, and GPT; Cohen κ assessed inter-annotator reliability.

Main Results:

• Llama 3 70B (7 few-shot examples) and GPT-4 (2 few-shot examples) achieved the highest accuracy (87.4%) against the human-annotated gold standard.
• Llama 3 70B demonstrated superior performance in coding health-related content.
• GPT-4 excelled in extracting demographic information from unstructured posts.

Conclusions:

• LLMs demonstrate comparable performance to human annotators in extracting demographic and health information from social media health forums.
• This study validates LLMs as reliable tools for analyzing digital health communications.
• LLMs show potential for advancing methodologies in digital research by understanding online behaviors and interactions.