Sarika Kondra, Wu Xu and Vijay V Raghavan*

University of Louisiana, Lafayette, USA

*Corresponding Author: Vijay V Raghavan, University of Louisiana, Lafayette, USA.

Received: April 15, 2024; Published: May 09, 2024

Abstract

Electronic Health Records (EHRs) have become the backbone of modern healthcare, providing a comprehensive record of a patient's medical journey. However, a significant portion of this data resides in clinical notes, which predominantly consist ofare rich in unstructured text. While valuable for consumption by medical professionals, this format presents challenges for traditional data analysis methods.

Natural Language Processing (NLP) offers a powerful solution to structure the information presented and unlock the potential of clinical notes. This paper explores the application of NLP tasks within the healthcare domain, specifically focusing on EHR data. We delve into the NLP pipeline, which allows us to differentiate between essential upstream tasks like tokenization and downstream tasks like named entity recognition (NER) and relation extraction. We showcase how NLP can extract crucial clinical information through these tasks and also emphasize the importance of de-identification for maintaining patient privacy.

A major challenge in NLP for healthcare is the limited availability of labeled clinical data. We discuss this bottleneck and explore potential solutions like active learning and transfer learning. Finally, the paper highlights the transformative potential of NLP in healthcare data processing and paves the way for future advancements in this dynamic field.

 Keywords: Natural Language Processing; Electronic Health Records; Clinical Applications

References

1. Martin-Sanchez Fernando and Karin Verspoor. "Big data in medicine is driving big changes”. Yearbook of Medical Informatics01 (2014): 14-20.
2. Spasic Irena and Goran Nenadic. “Clinical text data in machine learning: systematic review”. Jmir Medical Informatics3 (2020): e17984.
3. Settles Burr. "Active learning literature survey”. (2009).
4. Peng Yifan., et al. "Transfer learning in biomedical natural language processing: an evaluation of BERT and ELMo on ten benchmarking datasets”. arXiv preprint arXiv:1906.05474 (2019).
5. Snorkel AI. "Snorkel: A System for Crowdsourcing Weak Supervision”.
6. Shen F., et al. "Unsupervised representation learning for electronic health records: A review and outlook”. Briefings in Bioinformatics2(2020): 544-553.
7. Campbell David A and Stephen B Johnson. "Comparing syntactic complexity in medical and non-medical corpora”. Proceedings of the AMIA Symposium. American Medical Informatics Association (2001).
8. Zhang Yuhao., et al. "Biomedical and clinical English model packages for the Stanza Python NLP library”. Journal of the American Medical Informatics Association9 (2021): 1892-1899.
9. Medical Transcription Samples: www.mtsamples.com.
10. World Health Organization. ICD-10: international statistical classification of diseases and related health problems : tenth revision, 2^nd World Health Organization (2004).
11. Dotson Peggy. "CPT® codes: what are they, why are they necessary, and how are they developed?”. (2013): 583-587.
12. Devlin Jacob., et al. "Bert: Pre-training of deep bidirectional transformers for language understanding”. arXiv preprint arXiv:1810.04805 (2018).

Citation

Citation: Vijay V Raghavan., et al. “Unlocking the Power of Clinical Notes: Natural Language Processing in Healthcare”.Acta Scientific Medical Sciences 8.5 (2024): 37-44.

Copyright

Copyright: © 2024 Vijay V Raghavan., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.