Effective Natural Language Processing Algorithms for Early Alerts of Gout Flares from Chief Complaints

Early identification of acute gout is crucial, enabling healthcare professionals to implement targeted interventions for rapid pain relief and preventing disease progression, ensuring improved long-term joint function. In this study, we comprehensively explored the potential early detection of gout flares (GFs) based on nurses’ chief complaint notes in the Emergency Department (ED). Addressing the challenge of identifying GFs prospectively during an ED visit, where documentation is typically minimal, our research focused on employing alternative Natural Language Processing (NLP) techniques to enhance detection accuracy. We investigated GF detection algorithms using both sparse representations by traditional NLP methods and dense encodings by medical domain-specific Large Language Models (LLMs), distinguishing between generative and discriminative models. Three methods were used to alleviate the issue of severe data imbalances, including oversampling, class weights, and focal loss. Extensive empirical studies were performed on the Gout Emergency Department Chief Complaint Corpora. Sparse text representations like tf-idf proved to produce strong performances, achieving F1 scores higher than 0.75. The best deep learning models were RoBERTa-large-PM-M3-Voc and BioGPT, which had the best F1 scores for each dataset, with a 0.8 on the 2019 dataset and a 0.85 F1 score on the 2020 dataset, respectively. We concluded that although discriminative LLMs performed better for this classification task when compared to generative LLMs, a combination of using generative models as feature extractors and employing a support vector machine for classification yielded promising results comparable to those obtained with discriminative models

Effective Natural Language Processing Algorithms for Gout Flare Early Alert from Chief Complaints

In this study, we extend the exploration of gout flare detection initiated by Osborne, J. D. 16et al, through the utilization of their dataset of Emergency Department (ED) triage nurse chief com- 17plaint notes. Addressing the challenge of identifying gout flares prospectively during an ED visit, 18where documentation is typically minimal, our research focuses on employing alternative Natural 19Language Processing (NLP) techniques to enhance the detection accuracy. This study investigates 20the application of medical domain-specific Large Language Models (LLMs), distinguishing between 21generative and discriminative models. Models such as BioGPT, RoBERTa-large-PubMed-M3, and 22BioElectra were implemented to compare their efficacy with the original implementation by Os- 23borne, J. D. et al. The best model was Roberta-large-PM-M3 with a 0.8 F1 Score on the Gout-CC-2019 24dataset followed by BioElectra with 0.76 F1 Score. We concluded that discriminative LLMs per- 25formed better for this classification task compared to generative LLMs. However, a combination of 26using generative models as feature extractors and employing SVM for the classification of embed- 27dings yielded promising results comparable to those obtained with discriminative models. Never- 28theless, all our implementations surpassed the results obtained in the original publication