  Monitoring the health status of patients in the Intensive Care Unit (ICU) is
a critical aspect of providing superior care and treatment. The availability of
large-scale electronic health records (EHR) provides machine learning models
with an abundance of clinical text and vital sign data, enabling them to make
highly accurate predictions. Despite the emergence of advanced Natural Language
Processing (NLP) algorithms for clinical note analysis, the complex textual
structure and noise present in raw clinical data have posed significant
challenges. Coarse embedding approaches without domain-specific refinement have
limited the accuracy of these algorithms. To address this issue, we propose
FINEEHR, a system that utilizes two representation learning techniques, namely
metric learning and fine-tuning, to refine clinical note embeddings, while
leveraging the intrinsic correlations among different health statuses and note
categories. We evaluate the performance of FINEEHR using two metrics, namely
Area Under the Curve (AUC) and AUC-PR, on a real-world MIMIC III dataset. Our
experimental results demonstrate that both refinement approaches improve
prediction accuracy, and their combination yields the best results. Moreover,
our proposed method outperforms prior works, with an AUC improvement of over
10%, achieving an average AUC of 96.04% and an average AUC-PR of 96.48% across
various classifiers.
