Integrating multimodal clinical data to predict intravenous (IV) fluid utilization: a comparative analysis of natural language processing techniques

Background Accurate prediction of intravenous (IV) fluid utilization in emergency departments (ED) is essential for optimal clinical decision-making, resource allocation, and patient management. However, existing predictive models typically rely solely on structured clinical variables, overlooking the rich, contextual insights available in unstructured patient narratives. This study aimed to develop and evaluate predictive models that integrate structured clinical data with natural language processing (NLP)-derived embeddings from unstructured patient narratives to enhance prediction accuracy for IV fluid administration in ED settings. Methods We analyzed a large dataset from the National Hospital Ambulatory Medical Care Survey—Emergency Department (NHAMCS-ED, n = 13,115), comprising both structured patient demographics and clinical variables, alongside unstructured chief complaints. Patient narratives were processed using three distinct Natural Language Processing (NLP) techniques: CountVectorizer, Word2Vec embeddings, and pre-trained GPT-2 embeddings. An early fusion strategy was employed, concatenating structured and unstructured features before feeding them into Logistic Regression (LR) and Gradient Boosting Classifier (GBC) models. Model performance was evaluated using 5-fold cross-validation, with the area under the receiver operating characteristic curve (AUC) as the primary metric. Results The integrated models, which combined structured clinical variables with NLP features from patient narratives, consistently demonstrated superior predictive performance over models using either data type alone. We compared several NLP techniques, including CountVectorizer, Word2Vec, and pre-trained Generative Pre-trained Transformer 2 (GPT-2) embeddings. Unexpectedly, the integrated models using traditional NLP features, particularly CountVectorizer, achieved the best performance, reaching an AUC of 0.786 with the Gradient Boosting Classifier (GBC) and 0.781 with logistic regression. These results outperformed the integrated model using GPT-2 embeddings (GBC AUC = 0.772). This suggests that for the short, keyword-driven patient complaints found in the emergency department, frequency-based methods may be more effective at extracting predictive signals. Conclusions Our findings confirm that integrating patient narratives with structured clinical data significantly enhances predictive accuracy for IV fluid utilization. This multimodal approach, particularly when leveraging traditional NLP models like CountVectorizer, provides a robust framework for improving clinical decision-making, resource allocation, and patient care in the ED. The study highlights the critical importance of aligning NLP model choice with the specific characteristics of clinical text data.