Natural Language Processing (NLP) transforms the interpretation of medical
data by improving the context of unstructured text. In this chapter, we delve into the
potential of NLP techniques such as tokenization, named entity recognition, and deep
learning models like LSTMs and Transformers in handling clinical data, including
electronic health records and discharge summaries. We further elaborate on using
BIOBERT and BlueBERT as advanced pre-trained models, discussing their challenges
with data confidentiality and the scope of domain adaptation in future work to tune
them for the required task. By emphasizing on zero-shot learning, self-supervised
learning, and multilingual data processing, this chapter illustrates the necessity to
enhance NLP for better patient care and clinical decision making.
Keywords: Clinical data interpretation, Deep learning models, Named entity recognition (NER), Natural language processing (NLP), Pre-trained models.
