Natural Language Processing for Clinical Data Interpretation

Abstract

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.

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