Natural Language Processing for Neonatal Healthcare: Automating Clinical Decision Support and Diagnostic Insights
DOI:
https://doi.org/10.52783/jns.v14.4187Keywords:
Natural Language Processing (NLP), neonatal healthcare, clinical decision support systems (CDSS), electronic health records (EHR), BioBERT, ClinicalBERT, named entity recognition, classification, diagnostic insightsAbstract
This study investigates the application of Natural Language Processing (NLP) within the specialized domain of neonatal healthcare, addressing the critical need for enhanced clinical decision support. The primary aim is to automate the extraction of diagnostic insights from the substantial volume of unstructured data contained within electronic health records (EHRs) and other clinical documentation. Neonatal care is characterized by the urgency of decision-making and the vulnerability of patients, making the need for timely and accurate information paramount. The research acknowledges the challenges faced by clinicians in neonatal intensive care units (NICUs), who are often required to process large amounts of complex information rapidly. The study explores the potential of NLP to alleviate these challenges by transforming unstructured clinical text into actionable, structured data. The methodology involves the application of pre-trained biomedical language models, specifically BioBERT and ClinicalBERT, to perform key NLP tasks. These tasks include named entity recognition (NER), which is used to identify critical clinical entities within the text, and classification, which aids in categorizing patient risk. The efficacy of these models is evaluated using a dataset of anonymized neonatal clinical records. The results of the study demonstrate the promise of NLP in enhancing the efficiency and accuracy of neonatal diagnostics. The NLP-driven system shows potential for reducing the time required for diagnosis and improving the identification of critical conditions. However, the study also acknowledges existing challenges. These include the need for improved model interpretability, the handling of variability in clinical text, and the effective integration of NLP tools into clinical workflows. In conclusion, the study positions NLP as a valuable tool for advancing neonatal healthcare by extracting meaningful insights from clinical documentation. The findings support the continued development and refinement of NLP applications to address the unique challenges of this critical medical field.
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