Supporting clinical decisions utilizing machine learning and radiology notes.

In recent years the use of large language models (LLMs) in the clinical domain has enabled clinical decision support through the use of electronic medical records (EMRs). While these advancements have opened up the way for many complex tasks such as report coding, sentence classification, and summarization their impact on prediction tasks has been overshadowed. In this study, the ability of LLMs is evaluated with traditional processing techniques and models in binary prediction tasks of coronary atherosclerosis diagnosis and patient mortality using MIMIC IV radiology reports. It finds that XGBoost on a bag of words (BOW) dataset is competitive with a pre-trained RadBERT model.

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