# Interpretable Medical Diagnostics with Structured Data Extraction by Large Language Models

arXiv: https://arxiv.org/abs/2306.05052 · HTML: https://mlsanity.com/paper/2306.05052/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 49 references that are in the map.
- First posted on arXiv 8 Jun 2023.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Towards Reasoning in Large Language Models: A Survey](https://mlsanity.com/paper/2212.10403/) (2022)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [Language Models are Realistic Tabular Data Generators](https://mlsanity.com/paper/2210.06280/) (2022)
- [TabLLM: Few-shot Classification of Tabular Data with Large Language Models](https://mlsanity.com/paper/2210.10723/) (2022)
- [Techniques for Interpretable Machine Learning](https://mlsanity.com/paper/1808.00033/) (2018)
- [Explainable Prediction of Medical Codes from Clinical Text](https://mlsanity.com/paper/1802.05695/) (2018)
- [Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models](https://mlsanity.com/paper/2102.02503/) (2021)
- [Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes](https://mlsanity.com/paper/1704.02801/) (2017)
- [Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis](https://mlsanity.com/paper/1706.03446/) (2017)
- [Deep Entity Matching with Pre-Trained Language Models](https://mlsanity.com/paper/2004.00584/) (2020)
- [Efficient Few-Shot Learning Without Prompts](https://mlsanity.com/paper/2209.11055/) (2022)
- [Emergent autonomous scientific research capabilities of large language models](https://mlsanity.com/paper/2304.05332/) (2023)
- [Secure and Robust Machine Learning for Healthcare: A Survey](https://mlsanity.com/paper/2001.08103/) (2020)
- [Google Dataset Search by the Numbers](https://mlsanity.com/paper/2006.06894/) (2020)
- [Model Explainability in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/2106.07410/) (2021)
- [A Natural Language Processing Pipeline of Chinese Free-text Radiology Reports for Liver Cancer Diagnosis](https://mlsanity.com/paper/2004.13848/) (2020)
- [AD-BERT: Using Pre-trained contextualized embeddings to Predict the Progression from Mild Cognitive Impairment to Alzheimer's Disease](https://mlsanity.com/paper/2212.06042/) (2022)

## Built on by (most cited first, 13 of 13)

- [Large language models in healthcare and medical domain: A review](https://mlsanity.com/paper/2401.06775/) (2023)
- [From Text to Multimodality: Exploring the Evolution and Impact of Large Language Models in Medical Practice](https://mlsanity.com/paper/2410.01812/) (2024)
- [LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs](https://mlsanity.com/paper/2308.01157/) (2023)
- [MedTVT-R1: A Multimodal LLM Empowering Medical Reasoning and Diagnosis](https://mlsanity.com/paper/2506.18512/) (2025)
- [Evaluating Large Language Models for Public Health Classification and Extraction Tasks](https://mlsanity.com/paper/2405.14766/) (2024)
- [Data Analysis in the Era of Generative AI](https://mlsanity.com/paper/2409.18475/) (2024)
- [Large Language Multimodal Models for 5-Year Chronic Disease Cohort Prediction Using EHR Data](https://mlsanity.com/paper/2403.04785/) (2024)
- [DALL-M: Context-Aware Clinical Data Augmentation with LLMs](https://mlsanity.com/paper/2407.08227/) (2024)
- [Critique of Impure Reason: Unveiling the reasoning behaviour of medical Large Language Models](https://mlsanity.com/paper/2412.15748/) (2024)
- [The Role of Language Models in Modern Healthcare: A Comprehensive Review](https://mlsanity.com/paper/2409.16860/) (2024)
- [MedPAO: A Protocol-Driven Agent for Structuring Medical Reports](https://mlsanity.com/paper/2510.04623/) (2025)
- [Uncertainty-Aware Structured Data Extraction from Full CMR Reports via Distilled LLMs](https://mlsanity.com/paper/2605.08045/) (2026)
- [RelationalFactQA: A Benchmark for Evaluating Tabular Fact Retrieval from Large Language Models](https://mlsanity.com/paper/2505.21409/) (2025)

## Abstract (arXiv)

Tabular data is often hidden in text, particularly in medical diagnostic reports. Traditional machine learning (ML) models designed to work with tabular data, cannot effectively process information in such form. On the other hand, large language models (LLMs) which excel at textual tasks, are probably not the best tool for modeling tabular data. Therefore, we propose a novel, simple, and effective methodology for extracting structured tabular data from textual medical reports, called TEMED-LLM. Drawing upon the reasoning capabilities of LLMs, TEMED-LLM goes beyond traditional extraction techniques, accurately inferring tabular features, even when their names are not explicitly mentioned in the text. This is achieved by combining domain-specific reasoning guidelines with a proposed data validation and reasoning correction feedback loop. By applying interpretable ML models such as decision trees and logistic regression over the extracted and validated data, we obtain end-to-end interpretable predictions. We demonstrate that our approach significantly outperforms state-of-the-art text classification models in medical diagnostics. Given its predictive performance, simplicity, and interpretability, TEMED-LLM underscores the potential of leveraging LLMs to improve the performance and trustworthiness of ML models in medical applications.