Acta Scientific Medical Sciences (ASMS)(ISSN: 2582-0931)

Mini Review Volume 8 Issue 6

Unlocking the Power of Clinical Notes: Natural Language Processing in Healthcare

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


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


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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: © 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.


Acceptance rate30%
Acceptance to publication20-30 days
Impact Factor1.403

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