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Monitoring the health status of patients in the Intensive Care Unit (ICU) isa critical aspect of providing superior care and treatment. The availability oflarge-scale electronic health records (EHR) provides machine learning modelswith an abundance of clinical text and vital sign data, enabling them to makehighly accurate predictions. Despite the emergence of advanced Natural LanguageProcessing (NLP) algorithms for clinical note analysis, the complex textualstructure and noise present in raw clinical data have posed significantchallenges. Coarse embedding approaches without domain-specific refinement havelimited the accuracy of these algorithms. To address this issue, we proposeFINEEHR, a system that utilizes two representation learning techniques, namelymetric learning and fine-tuning, to refine clinical note embeddings, whileleveraging the intrinsic correlations among different health statuses and notecategories. We evaluate the performance of FINEEHR using two metrics, namelyArea Under the Curve (AUC) and AUC-PR, on a real-world MIMIC III dataset. Ourexperimental results demonstrate that both refinement approaches improveprediction accuracy, and their combination yields the best results. Moreover,our proposed method outperforms prior works, with an AUC improvement of over10%, achieving an average AUC of 96.04% and an average AUC-PR of 96.48% acrossvarious classifiers.

FineEHR: Refine Clinical Note Representations to Improve Mortality Prediction