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

arXiv: https://arxiv.org/abs/2401.12994 · HTML: https://mlsanity.com/paper/2401.12994/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 18 Jan 2024.

## Builds on (most cited first, 2 of 2)

- [Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review](https://mlsanity.com/paper/1908.05780/) (2019)
- [Natural language processing of MIMIC-III clinical notes for identifying diagnosis and procedures with neural networks](https://mlsanity.com/paper/1912.12397/) (2019)

## Built on by (most cited first, 6 of 6)

- [Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Survey](https://mlsanity.com/paper/2403.01909/) (2024)
- [Ensemble Methodology:Innovations in Credit Default Prediction Using LightGBM, XGBoost, and LocalEnsemble](https://mlsanity.com/paper/2402.17979/) (2024)
- [Enhancing Text Authenticity: A Novel Hybrid Approach for AI-Generated Text Detection](https://mlsanity.com/paper/2406.06558/) (2024)
- [Enhancing Healthcare Recommendation Systems with a Multimodal LLMs-based MOE Architecture](https://mlsanity.com/paper/2412.11557/) (2024)
- [Advancing Prompt Recovery in NLP: A Deep Dive into the Integration of Gemma-2b-it and Phi2 Models](https://mlsanity.com/paper/2407.05233/) (2024)
- [Optimizing Contrail Detection: A Deep Learning Approach with EfficientNet-b4 Encoding](https://mlsanity.com/paper/2404.14441/) (2024)

## Abstract (arXiv)

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.