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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...

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Language Models for Multilabel Document Classification of Surgical Concepts in Exploratory Laparotomy Operative

Jeremy A Balch^1,2, Sasank S Desaraju³, Victoria J Nolan¹

¹Department of Surgery, University of Florida College of Medicine, Gainesville, FL, United States.

JMIR Medical Informatics

|July 9, 2025

Summary

This summary is machine-generated.

Large language models (LLMs) significantly improve data extraction from surgical notes, outperforming traditional methods. Further refinement is needed for reliable use in surgical research and quality improvement.

Keywords:

chart review exploratory laparotomy general surgery generative large language models natural language processing

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Area of Science:

Natural Language Processing (NLP)
Machine Learning in Healthcare
Surgical Data Science

Background:

Operative notes are crucial for clinical care, research, and billing, but manual data extraction is time-consuming.
Traditional NLP methods like bag-of-words (BoW) and tf-idf have limitations for complex surgical note analysis.
Large Language Models (LLMs) show promise in augmenting or replacing traditional NLP for surgical text mining.

Purpose of the Study:

To develop and evaluate LLMs for expediting data extraction from surgical operative notes.
To compare the performance of LLMs against traditional NLP techniques for multilabel classification of surgical concepts.
To assess the utility of different LLM architectures and the impact of context on classification accuracy.

Main Methods:

A dataset of 388 exploratory laparotomy notes was annotated for 21 surgical concepts.
Compared traditional BoW and tf-idf models with encoder-only (Clinical-Longformer) and decoder-only (Llama 3) transformer models.
Evaluated multilabel classification using 5-fold cross-validation, F1-score, and Hamming Loss (HL), with and without contextual information.

Main Results:

The decoder-only Llama 3 model achieved the highest performance (micro F1-score 0.88, HL 0.11), significantly outperforming BoW, tf-idf, and Clinical-Longformer.
Incorporating context improved Llama 3's F1-scores by an average of 0.16.
Performance varied across concepts, with challenges in classifying contamination and handling complex cases like prior or simultaneous operations.

Conclusions:

Off-the-shelf autoregressive LLMs demonstrate superior performance over traditional NLP and encoder-only models for classifying surgical operative notes.
LLMs offer a potential solution for streamlining retrospective reviews in surgery.
Further development is necessary to address semantic nuances and edge cases for reliable application in research and quality improvement.