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Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...

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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

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Developing an ICD-10 Coding Assistant: Pilot Study Using RoBERTa and GPT-4 for Term Extraction and Description-Based

Sander Puts¹, Catharina M L Zegers¹, Andre Dekker¹

¹Department of Radiation Oncology (Maastro), GROW Research Institute for Oncology and Reproduction, Maastricht University Medical Centre+, P.O. Box 616, Maastricht, 6200 MD, Netherlands, 31 43 38 81863.

JMIR Formative Research

|February 12, 2025

Summary

This summary is machine-generated.

This study explored AI for ICD-10 coding, finding lead term extraction promising but RAG-based code assignment less effective. Future work should better align AI with medical coder workflows for improved accuracy.

Keywords:

AI automation Bidirectional Encoder Representations from Transformers GPT-4 ICD-10 International Classification of Diseases LLM NER RAG RoBERTa Robustly Optimized BERT Pretraining Approach artificial intelligence code analysis coding computer assisted coding computer-assisted-coding large language model named entity recognition retrieval-augmented generation term extraction transformer model

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Natural Language Processing

Background:

The World Health Organization's International Classification of Diseases (ICD) standardizes health condition coding for policy, research, and billing.
Current AI automation in medical coding shows promise but lags human accuracy and lacks explainability for clinical adoption.

Purpose of the Study:

To explore the potential of large language models (LLMs) for assisting medical coders with ICD-10 coding.
To develop a computer-assisted coding system augmenting human coders by identifying lead terms and using retrieval-augmented generation (RAG).

Main Methods:

Utilized the CodiEsp-X dataset (1000 Spanish clinical cases with ICD-10 codes) and created CodiEsp-X-lead by replacing full-text evidence with lead terms using GPT-4.
Fine-tuned a Robustly Optimized BERT Pretraining Approach (ROBERTa) model for named entity recognition to extract lead terms.
Employed GPT-4 for generating code descriptions and a RAG approach with OpenAI's text-embedding-ada-002 to assign ICD codes to lead terms via a vector database.

Main Results:

The fine-tuned ROBERTa model achieved an F1-score of 0.80 for ICD lead term extraction on the CodiEsp-X-lead dataset.
GPT-4-generated code descriptions reduced RAG retrieval failures by approximately 5% for diagnoses and procedures.
The overall explainability F1-score for the CodiEsp-X task was 0.305, significantly below the state-of-the-art (0.633), due to reliance on descriptions and workflow misalignment.

Conclusions:

Lead term extraction shows potential, but RAG-based code assignment using GPT-4 and descriptions proved less effective.
Future research must better integrate AI with medical coder workflows, including official coding guidelines and alphabetic indexes, to enhance accuracy and practical utility.