We investigated the development of a Machine Learning (ML)-based classifier to identify abnormalities in radiology reports from Emergency Departments (EDs) that can help automate the radiology report reconciliation process. Often, radiology reports become available to the ED only after the patient has been treated and discharged, following ED clinician interpretation of the X-ray. However, occasionally ED clinicians misdiagnose or fail to detect subtle abnormalities on X-rays, so they conduct a manual radiology report reconciliation process as a safety net. Previous studies addressed this problem of automated reconciliation using ML-based classification solutions that require data samples from the target institution that is heavily based on feature engineering, implying lower transferability between hospitals. In this paper, we investigated the benefits of using pre-trained BERT models for abnormality classification in a cross-institutional setting where data for fine-tuning was unavailable from the target institution. We also examined how the inclusion of synthetically generated radiology reports from ChatGPT affected the performance of the BERT models. Our findings suggest that BERT-like models outperform previously proposed ML-based methods in cross-institutional scenarios, and that adding ChatGPT-generated labelled radiology reports can improve the classifier’s performance by reducing the number of misdiagnosed discharged patients.

Bacterial infection (BI) is an important clinical condition and is related to many diseases that are difficult to treat. Early prediction of BI can lead to better treatment and appropriate use of antimicrobial medications. In this paper, we study a variety of NLP models to predict BI for critically ill patients and compare them with a strong baseline based on clinical measurements. We find that choosing the proper text-based model to combine with measurements can lead to substantial improvements. Our results show the value of clinical text in predicting and managing BI. We also find that the NLP model developed using patients with BI can be transferred to the more general patient cohort for patient risk prediction.

Learning from real-world clinical data has potential to promote the quality of care, improve the efficiency of healthcare systems, and support clinical research. As a large proportion of clinical information is recorded only in unstructured free-text format, applying NLP to process and understand the vast amount of clinical text generated in clinical encounters is essential. However, clinical text is known to be highly ambiguous, it contains complex professional terms requiring clinical expertise to understand and annotate, and it is written in different clinical contexts with distinct purposes. All these factors together make clinical NLP research both rewarding and challenging. In this tutorial, we will discuss the characteristics of clinical text and provide an overview of some of the tools and methods used to process it. We will also present a real-world example to show the effectiveness of different NLP methods in processing and understanding clinical text. Finally, we will discuss the strengths and limitations of large language models and their applications, evaluations, and extensions in clinical NLP.