FineEHR: Refine Clinical Note Representations to Improve Mortality Prediction

TL;DR

FINEEHR enhances clinical note embeddings through metric learning and fine-tuning, significantly improving mortality prediction accuracy in ICU settings using EHR data.

Contribution

The paper introduces FINEEHR, a novel system that refines clinical note representations with domain-specific techniques, outperforming prior methods in mortality prediction.

Findings

Improved AUC and AUC-PR metrics over baseline models.

Combination of refinement techniques yields the best prediction performance.

Achieved over 10% AUC improvement, reaching 96.04%.

Abstract

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.