Using a clinical narrative-aware pre-trained language model for predicting emergency department patient disposition and unscheduled return visits.

The increasing prevalence of overcrowding in Emergency Departments (EDs) threatens the effective delivery of urgent healthcare. Mitigation strategies include the deployment of monitoring systems capable of tracking and managing patient disposition to facilitate appropriate and timely care, which subsequently reduces patient revisits, optimizes resource allocation, and enhances patient outcomes. This study used âˆ¼ 250,000 emergency department visit records from Taipei Medical University-Shuang Ho Hospital to develop a natural language processing model using BlueBERT, a biomedical domain-specific pre-trained language model, to predict patient disposition status and unplanned readmissions. Data preprocessing and the integration of both structured and unstructured data were central to our approach. Compared to other models, BlueBERT outperformed due to its pre-training on a diverse range of medical literature, enabling it to better comprehend the specialized terminology, relationships, and context present in ED data. We found that translating Chinese-English clinical narratives into English and textualizing numerical data into categorical representations significantly improved the prediction of patient disposition (AUROC = 0.9014) and 72-hour unscheduled return visits (AUROC = 0.6475). The study concludes that the BlueBERT-based model demonstrated superior prediction capabilities, surpassing the performance of prior patient disposition predictive models, thus offering promising applications in the realm of ED clinical practice.

Correction: REM-related obstructive sleep apnea and vertigo: A retrospective case-control study.

[This corrects the article DOI: 10.1371/journal.pone.0252844.].

A pre-trained language model for emergency department intervention prediction using routine physiological data and clinical narratives.

INTRODUCTION: The urgency and complexity of emergency room (ER) settings require precise and swift decision-making processes for patient care. Ensuring the timely execution of critical examinations and interventions is vital for reducing diagnostic errors, but the literature highlights a need for innovative approaches to optimize diagnostic accuracy and patient outcomes. In response, our study endeavors to create predictive models for timely examinations and interventions by leveraging the patient's symptoms and vital signs recorded during triage, and in so doing, augment traditional diagnostic methodologies. METHODS: Focusing on four key areas-medication dispensing, vital interventions, laboratory testing, and emergency radiology exams, the study employed Natural Language Processing (NLP) and seven advanced machine learning techniques. The research was centered around the innovative use of BioClinicalBERT, a state-of-the-art NLP framework. RESULTS: BioClinicalBERT emerged as the superior model, outperforming others in predictive accuracy. The integration of physiological data with patient narrative symptoms demonstrated greater effectiveness compared to models based solely on textual data. The robustness of our approach was confirmed by an Area Under the Receiver Operating Characteristic curve (AUROC) score of 0.9. CONCLUSION: The findings of our study underscore the feasibility of establishing a decision support system for emergency patients, targeting timely interventions and examinations based on a nuanced analysis of symptoms. By using an advanced natural language processing technique, our approach shows promise for enhancing diagnostic accuracy. However, the current model is not yet fully mature for direct implementation into daily clinical practice. Recognizing the imperative nature of precision in the ER environment, future research endeavors must focus on refining and expanding predictive models to include detailed timely examinations and interventions. Although the progress achieved in this study represents an encouraging step towards a more innovative and technology-driven paradigm in emergency care, full clinical integration warrants further exploration and validation.