Automated Scoring of Clinical Patient Notes using Advanced NLP and Pseudo Labeling

TL;DR

The paper tackles automated scoring of clinical patient notes, addressing the time and variability inherent in manual assessments. It adopts a DeBERTa-based architecture with Masked Language Modeling pretraining and strategic pseudo labeling to leverage unlabeled data, complemented by padding optimization to accelerate inference. Key contributions include applying MLM in the clinical domain, generating and using pseudo labels from a large unlabeled corpus, and implementing training accelerations that reduce total training time while maintaining or improving accuracy, as evidenced by cross-validation scores around 0.89. The findings demonstrate that this approach can reliably automate clinical note evaluation, offering practical benefits for medical education and certification by increasing efficiency and consistency in scoring.

Abstract

Clinical patient notes are critical for documenting patient interactions, diagnoses, and treatment plans in medical practice. Ensuring accurate evaluation of these notes is essential for medical education and certification. However, manual evaluation is complex and time-consuming, often resulting in variability and resource-intensive assessments. To tackle these challenges, this research introduces an approach leveraging state-of-the-art Natural Language Processing (NLP) techniques, specifically Masked Language Modeling (MLM) pretraining, and pseudo labeling. Our methodology enhances efficiency and effectiveness, significantly reducing training time without compromising performance. Experimental results showcase improved model performance, indicating a potential transformation in clinical note assessment.

Figures (4)

• Figure 1: Architecture of DeBERTa
• Figure 2: Architecture of Transformer
• Figure 3: Schematic Diagram of Pseudo Label Generation.
• Figure 4: Fast Inference through Padding Optimization