@Article{M-10141,
AUTHOR = {sabbir, Md Ariful Islam},
TITLE = {Data-Driven Healthcare: Exploring Biomedical Text Mining Through NLP Models},
JOURNAL = {Scientific Research Journal of Science, Engineering and Technology},
VOLUME = {2},
YEAR = {2024},
NUMBER = {2},
ARTICLE-NUMBER = {M-10141},
URL = {https://isrdo.org/journal/SRJSET/currentissue/data-driven-healthcare-exploring-biomedical-text-mining-through-nlp-models-1},
ISSN = {2584-0584},
ABSTRACT = {    In recent years, the
expanding volume of biological literature, clinical notes, and electronic
health records (EHRs) has presented both a barrier and an opportunity for
healthcare improvement. Biological text mining, which employs natural language
processing (NLP) methods, is a viable alternative for extracting useful
insights from unstructured biological data. This paper analyzes the relevance
of NLP models in facilitating data-driven healthcare, with an emphasis on basic
tasks such as named entity recognition (NER), relationship extraction (RE), and
text classification. We show how domain-specific NLP models such as BioBERT,
SciBERT, and ClinicalBERT have been built to cope with the intrinsic complexity
of biological language, such as confusing terminology, acronyms, and technical
jargon.Biomedical text mining has various healthcare applications, including
drug discovery and reuse, clinical decision support, and pharmacovigilance. NLP
models allow more informed decision-making, boost patient outcomes, and speed
up personalized medicine research by automating the extraction of relevant
patterns from large-scale biological texts. This paper also highlights the key
challenges faced in biomedical text mining, such as data heterogeneity,
imbalanced datasets, and the demand for explainable AI. Finally, we address
future techniques for biological text mining that incorporate the integration
of multimodal data, enhanced semantic understanding, and improved model
interpretability. Finally, this research illustrates how NLP-driven text mining
may turn unstructured data into relevant information in the healthcare
industry.},
DOI = {}
}