Enhancing Patient Intake Process in Mental Health Consultations Using RAG-Driven Chatbot
Bibliographic record
Abstract
In this paper, we develop and evaluate an empathic chatbot, called EmoBot, for a mental healthcare patient in-take scenario. EmoBot employs Retrieval-Augmented Generation (RAG) methods with Large Language Models (LLMs), to provide empathic support and advice, while ensuring the intake process is completed. We evaluate EmoBot's performance both by automated metrics using available datasets and through dialogues with a simulated patient model that mimics varying levels of depression severity. Our evaluations showed good agreement between EmoBot's categorization of depression severity levels and human raters. EmoBot's topic classification system achieved 78.48% accuracy on the Primate2022 dataset [1] without fine-tuning. EmoBot's responses showed a 0.93 similarity with patient inputs, proving contextual relevance. These findings highlight the effectiveness of RAG-based methods in guiding and providing safety guardrails for LLMs and their potential as supportive tools for health assessment and support.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".