Abstract:

The healthcare domain generates vast and heterogeneous data streams, encompassing electronic health records (EHRs), clinical narratives, pathology reports, discharge summaries, and physician notes. A significant amount of this data exists in unstructured textual form, posing challenges for traditional data analysis and automated decision-support systems. Natural Language Processing (NLP), a subfield of Artificial Intelligence (AI), has emerged as a pivotal methodology for structuring, extracting, and interpreting clinically relevant information from such unstructured text. This paper systematically investigates the role of NLP in the healthcare domain, emphasizing its utility in information extraction from clinical notes, clinical information retrieval, medical documentation summarization etc. In order to emphasize the applicability of NLP in healthcare domain, we present a detailed case study illustrating the application of NLP on clinical text to detect early symptoms of depression, highlighting the use of advanced preprocessing strategies and domain-specific lexical normalization. Further, the study demonstrates the integration of traditional machine learning algorithms and transformer-based architectures including BERT and ClinicalBERT for contextual representation learning and classification of depressive symptomatology within clinical notes. The technical implementation involves a hybrid pipeline comprising tokenization, lemmatization, feature extraction via TF-IDF and embeddings, and classification using both conventional (e.g., Logistic Regression, Random Forest) and deep neural models such as LSTM and BERT. This work therefore provides empirical insights and methodological guidelines to researchers and practitioners aiming to advance clinical NLP, and suggests future research directions in healthcare informatics.