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

• URL: http://arxiv.org/abs/2401.12994v1
• Date: Thu, 18 Jan 2024 05:17:18 GMT
• Title: Automated Scoring of Clinical Patient Notes using Advanced NLP and Pseudo Labeling
• Authors: Jingyu Xu, Yifeng Jiang, Bin Yuan, Shulin Li, Tianbo Song
• Abstract summary: This research introduces an approach leveraging state-of-the-art Natural Language Processing (NLP) techniques. Our methodology enhances efficiency and effectiveness, significantly reducing training time without compromising performance.
• Score: 2.711804338865226
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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