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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, predominantly consist of 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.
Kondra et al. (Sat,) studied this question.