Integrating Natural Language Processing in Medical Information Science for Clinical Text Analysis Article Swipe
Dharmsheel Shrivastav
,
Manjula Sanjay Koti Alamma B.H.
,
Swarna Swetha Kolaventi
,
Bichitrananda Patra
,
N. Ramu
,
Divya Sharma
,
Shubhansh Bansal
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.56294/mw2024513
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.56294/mw2024513
The rapid digitization of healthcare data has led to an exponential increase in unstructured clinical text, necessitating the integration of Natural Language Processing (NLP) in Medical Information Science. This research explores deep learning-based NLP techniques for clinical text analysis, focusing on Named Entity Recognition (NER), disease classification, adverse drug reaction detection, and clinical text summarization. The study leverages state-of-the-art transformer models such as BioBERT, ClinicalBERT, and GPT-4 Medical, which demonstrate superior performance in extracting key medical entities, classifying diseases, and summarizing electronic health records (EHRs). Experimental results on benchmark datasets such as MIMIC-III, i2b2, and ClinicalTrials.gov show that ClinicalBERT outperforms traditional ML models by achieving an F1-score of 89.9% in NER tasks, while GPT-4 Medical improves EHR summarization efficiency by 40%. By means of automated medical documentation, clinical decision support, and real-time adverse drug event detection which integrates NLP into healthcare systems diagnostic accuracy, physician efficiency, and patient safety are much improved. NLP-driven medical text analysis has great potential to transform clinical procedures and raise patient outcomes despite obstacles like computing costs, data privacy issues, and model interpretability. Improving domain-specific AI models, maximising real-time processing, and guaranteeing ethical AI deployment in healthcare should be the key priorities of next studies.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.56294/mw2024513
- https://mw.ageditor.ar/index.php/mw/article/download/513/739
- OA Status
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- References
- 14
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4409382166Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.56294/mw2024513Digital Object Identifier
- Title
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Integrating Natural Language Processing in Medical Information Science for Clinical Text AnalysisWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-12-31Full publication date if available
- Authors
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Dharmsheel Shrivastav, Manjula Sanjay Koti Alamma B.H., Swarna Swetha Kolaventi, Bichitrananda Patra, N. Ramu, Divya Sharma, Shubhansh BansalList of authors in order
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https://doi.org/10.56294/mw2024513Publisher landing page
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https://mw.ageditor.ar/index.php/mw/article/download/513/739Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://mw.ageditor.ar/index.php/mw/article/download/513/739Direct OA link when available
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Computer science, Natural language processing, Medical science, Data science, Information retrieval, Artificial intelligence, Medicine, Medical educationTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.interpretability. | 177 |
| abstract_inverted_index.ClinicalTrials.gov | 95 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.32194158 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |