# From EMR Data to Clinical Insight: An LLM-Driven Framework for Automated Pre-Consultation Questionnaire Generation

arXiv: https://arxiv.org/abs/2508.00581 · HTML: https://mlsanity.com/paper/2508.00581/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 1 Aug 2025.

## Builds on (most cited first, 7 of 7)

- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [Can large language models reason about medical questions?](https://mlsanity.com/paper/2207.08143/) (2022)
- [Machine learning based disease diagnosis: A comprehensive review](https://mlsanity.com/paper/2112.15538/) (2021)
- [Knowledge Graph-based Question Answering with Electronic Health Records](https://mlsanity.com/paper/2010.09394/) (2020)
- [Transformers and Large Language Models for Chemistry and Drug Discovery](https://mlsanity.com/paper/2310.06083/) (2023)
- [UniOQA: A Unified Framework for Knowledge Graph Question Answering with Large Language Models](https://mlsanity.com/paper/2406.02110/) (2024)

## Built on by (most cited first, 1 of 1)

- [RE-MCDF: Closed-Loop Multi-Expert LLM Reasoning for Knowledge-Grounded Clinical Diagnosis](https://mlsanity.com/paper/2602.01297/) (2026)

## Abstract (arXiv)

Pre-consultation is a critical component of effective healthcare delivery. However, generating comprehensive pre-consultation questionnaires from complex, voluminous Electronic Medical Records (EMRs) is a challenging task. Direct Large Language Model (LLM) approaches face difficulties in this task, particularly regarding information completeness, logical order, and disease-level synthesis. To address this issue, we propose a novel multi-stage LLM-driven framework: Stage 1 extracts atomic assertions (key facts with timing) from EMRs; Stage 2 constructs personal causal networks and synthesizes disease knowledge by clustering representative networks from an EMR corpus; Stage 3 generates tailored personal and standardized disease-specific questionnaires based on these structured representations. This framework overcomes limitations of direct methods by building explicit clinical knowledge. Evaluated on a real-world EMR dataset and validated by clinical experts, our method demonstrates superior performance in information coverage, diagnostic relevance, understandability, and generation time, highlighting its practical potential to enhance patient information collection.