# Can Large Language Models abstract Medical Coded Language?

arXiv: https://arxiv.org/abs/2403.10822 · HTML: https://mlsanity.com/paper/2403.10822/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 51 references that are in the map.
- First posted on arXiv 16 Mar 2024.

## Builds on (most cited first, 20 of 20)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [RAFT: Adapting Language Model to Domain Specific RAG](https://mlsanity.com/paper/2403.10131/) (2024)
- [Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization](https://mlsanity.com/paper/2309.07430/) (2023)
- [Explainable Prediction of Medical Codes from Clinical Text](https://mlsanity.com/paper/1802.05695/) (2018)
- [LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks](https://mlsanity.com/paper/2206.06565/) (2022)
- [LLMs Accelerate Annotation for Medical Information Extraction](https://mlsanity.com/paper/2312.02296/) (2023)
- [BioMegatron: Larger Biomedical Domain Language Model](https://mlsanity.com/paper/2010.06060/) (2020)
- [Clinical Camel: An Open Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding](https://mlsanity.com/paper/2305.12031/) (2023)
- [Multimodal LLMs for health grounded in individual-specific data](https://mlsanity.com/paper/2307.09018/) (2023)
- [Multimodal Clinical Benchmark for Emergency Care (MC-BEC): A Comprehensive Benchmark for Evaluating Foundation Models in Emergency Medicine](https://mlsanity.com/paper/2311.04937/) (2023)
- [REALM: RAG-Driven Enhancement of Multimodal Electronic Health Records Analysis via Large Language Models](https://mlsanity.com/paper/2402.07016/) (2024)
- [Emergency Department Decision Support using Clinical Pseudo-notes](https://mlsanity.com/paper/2402.00160/) (2024)
- [MT-Clinical BERT: Scaling Clinical Information Extraction with Multitask Learning](https://mlsanity.com/paper/2004.10220/) (2020)
- [Intelligent EHRs: Predicting Procedure Codes From Diagnosis Codes](https://mlsanity.com/paper/1712.00481/) (2017)

## Built on by (most cited first, 20 of 20)

- [Clinical ModernBERT: An efficient and long context encoder for biomedical text](https://mlsanity.com/paper/2504.03964/) (2025)
- [HiMAE: Hierarchical Masked Autoencoders Discover Resolution-Specific Structure in Wearable Time Series](https://mlsanity.com/paper/2510.25785/) (2025)
- [ChronoFormer: Time-Aware Transformer Architectures for Structured Clinical Event Modeling](https://mlsanity.com/paper/2504.07373/) (2025)
- [A Collection of Innovations in Medical AI for patient records in 2024](https://mlsanity.com/paper/2503.05768/) (2025)
- [Serialized EHR make for good text representations](https://mlsanity.com/paper/2510.13843/) (2025)
- [CP-Env: Evaluating Large Language Models on Clinical Pathways in a Controllable Hospital Environment](https://mlsanity.com/paper/2512.10206/) (2025)
- [Learning Clinical Representations Under Systematic Distribution Shift](https://mlsanity.com/paper/2603.07348/) (2026)
- [Discriminative Representation Learning for Clinical Prediction](https://mlsanity.com/paper/2603.20921/) (2026)
- [Exploring Neural Ordinary Differential Equations as Interpretable Healthcare classifiers](https://mlsanity.com/paper/2503.03129/) (2025)
- [Temporal Entailment Pretraining for Clinical Language Models over EHR Data](https://mlsanity.com/paper/2504.18128/) (2025)
- [Diagnosing our datasets: How does my language model learn clinical information?](https://mlsanity.com/paper/2505.15024/) (2025)
- [Learning Longitudinal Health Representations from EHR and Wearable Data](https://mlsanity.com/paper/2601.12227/) (2026)
- [Unlocking Historical Clinical Trial Data with ALIGN: A Compositional Large Language Model System for Medical Coding](https://mlsanity.com/paper/2411.13163/) (2024)
- [An Optimization Algorithm for Multimodal Data Alignment](https://mlsanity.com/paper/2503.07636/) (2025)
- [Dense Feature Learning via Linear Structure Preservation in Medical Data](https://mlsanity.com/paper/2602.07706/) (2026)
- [Training a Large Language Model for Medical Coding Using Privacy-Preserving Synthetic Clinical Data](https://mlsanity.com/paper/2603.23515/) (2026)
- [Event Fields: Learning Latent Event Structure for Waveform Foundation Models](https://mlsanity.com/paper/2605.08685/) (2026)
- [WISTERIA: Learning Clinical Representations from Noisy Supervision via Multi-View Consistency in Electronic Health Records](https://mlsanity.com/paper/2605.09765/) (2026)
- [AURORA: Contextual Orthogonalization for Geometric Representation Learning in Healthcare Foundation Models](https://mlsanity.com/paper/2605.17765/) (2026)
- [MorphologyFM: A Foundation Model for Morphology-Aware Representation Learning from ECG and Pulse Oximetry Waveforms](https://mlsanity.com/paper/2607.09749/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have become a pivotal research area, potentially making beneficial contributions in fields like healthcare where they can streamline automated billing and decision support. However, the frequent use of specialized coded languages like ICD-10, which are regularly updated and deviate from natural language formats, presents potential challenges for LLMs in creating accurate and meaningful latent representations. This raises concerns among healthcare professionals about potential inaccuracies or ``hallucinations" that could result in the direct impact of a patient. Therefore, this study evaluates whether large language models (LLMs) are aware of medical code ontologies and can accurately generate names from these codes. We assess the capabilities and limitations of both general and biomedical-specific generative models, such as GPT, LLaMA-2, and Meditron, focusing on their proficiency with domain-specific terminologies. While the results indicate that LLMs struggle with coded language, we offer insights on how to adapt these models to reason more effectively.