Enhancing drug discovery and patient care through advanced analytics with the power of NLP and machine learning in pharmaceutical data interpretation
This study delves into the transformative potential of Machine Learning (ML) and Natural Language Processing (NLP) within the pharmaceutical industry, spotlighting their significant impact on enhancing medical research methodologies and optimizing healthcare service delivery. Utilizing a vast datase...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | SLAS Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2472630324001201 |
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| author | Nagalakshmi R Surbhi Bhatia Khan Ananthoju Vijay kumar Mahesh T R Mohammad Alojail Saurabh Raj Sangwan Mo Saraee |
| author_facet | Nagalakshmi R Surbhi Bhatia Khan Ananthoju Vijay kumar Mahesh T R Mohammad Alojail Saurabh Raj Sangwan Mo Saraee |
| author_sort | Nagalakshmi R |
| collection | DOAJ |
| description | This study delves into the transformative potential of Machine Learning (ML) and Natural Language Processing (NLP) within the pharmaceutical industry, spotlighting their significant impact on enhancing medical research methodologies and optimizing healthcare service delivery. Utilizing a vast dataset sourced from a well-established online pharmacy, this research employs sophisticated ML algorithms and cutting-edge NLP techniques to critically analyze medical descriptions and optimize recommendation systems for drug prescriptions and patient care management. Key technological integrations include BERT embeddings, which provide nuanced contextual understanding of complex medical texts, and cosine similarity measures coupled with TF-IDF vectorization to significantly enhance the precision and reliability of text-based medical recommendations. By meticulously adjusting the cosine similarity thresholds from 0.2 to 0.5, our tailored models have consistently achieved a remarkable accuracy rate of 97 %, illustrating their effectiveness in predicting suitable medical treatments and interventions. These results not only highlight the revolutionary capabilities of NLP and ML in harnessing data-driven insights for healthcare but also lay a robust groundwork for future advancements in personalized medicine and bespoke treatment pathways. Comprehensive analysis demonstrates the scalability and adaptability of these technologies in real-world healthcare settings, potentially leading to substantial improvements in patient outcomes and operational efficiencies within the healthcare system. |
| format | Article |
| id | doaj-art-b0011311e5884d7295459b1f0275ff5d |
| institution | Kabale University |
| issn | 2472-6303 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | SLAS Technology |
| spelling | doaj-art-b0011311e5884d7295459b1f0275ff5d2025-02-07T04:48:01ZengElsevierSLAS Technology2472-63032025-04-0131100238Enhancing drug discovery and patient care through advanced analytics with the power of NLP and machine learning in pharmaceutical data interpretationNagalakshmi R0Surbhi Bhatia Khan1Ananthoju Vijay kumar2Mahesh T R3Mohammad Alojail4Saurabh Raj Sangwan5Mo Saraee6Department of Data Science, School of Science Engineering and Environment, University of Salford, Manchester, United KingdomUniversity Centre for Research and Development, Chandigarh University, Mohali, Punjab, India; Centre for Research Impact and Outcome and Chitkara University Institute of Engineering and Technology and Chitkara University, Rajpura, 140401, Punjab, India; Corresponding author.Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bengaluru, 562112, IndiaDepartment of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bengaluru, 562112, IndiaManagement Information System Department, College of Business Administration, King Saud University, Riyadh, Saudi ArabiaSchool of Computer Science & Engineering, Galgotias University, Greater Noida, IndiaSchool of science, engineering and environment, University of Salford, United KingdomThis study delves into the transformative potential of Machine Learning (ML) and Natural Language Processing (NLP) within the pharmaceutical industry, spotlighting their significant impact on enhancing medical research methodologies and optimizing healthcare service delivery. Utilizing a vast dataset sourced from a well-established online pharmacy, this research employs sophisticated ML algorithms and cutting-edge NLP techniques to critically analyze medical descriptions and optimize recommendation systems for drug prescriptions and patient care management. Key technological integrations include BERT embeddings, which provide nuanced contextual understanding of complex medical texts, and cosine similarity measures coupled with TF-IDF vectorization to significantly enhance the precision and reliability of text-based medical recommendations. By meticulously adjusting the cosine similarity thresholds from 0.2 to 0.5, our tailored models have consistently achieved a remarkable accuracy rate of 97 %, illustrating their effectiveness in predicting suitable medical treatments and interventions. These results not only highlight the revolutionary capabilities of NLP and ML in harnessing data-driven insights for healthcare but also lay a robust groundwork for future advancements in personalized medicine and bespoke treatment pathways. Comprehensive analysis demonstrates the scalability and adaptability of these technologies in real-world healthcare settings, potentially leading to substantial improvements in patient outcomes and operational efficiencies within the healthcare system.http://www.sciencedirect.com/science/article/pii/S2472630324001201Natural language processingPharmaceutical analyticsHealthcare technologyPersonalized MedicineText MiningBERT |
| spellingShingle | Nagalakshmi R Surbhi Bhatia Khan Ananthoju Vijay kumar Mahesh T R Mohammad Alojail Saurabh Raj Sangwan Mo Saraee Enhancing drug discovery and patient care through advanced analytics with the power of NLP and machine learning in pharmaceutical data interpretation SLAS Technology Natural language processing Pharmaceutical analytics Healthcare technology Personalized Medicine Text Mining BERT |
| title | Enhancing drug discovery and patient care through advanced analytics with the power of NLP and machine learning in pharmaceutical data interpretation |
| title_full | Enhancing drug discovery and patient care through advanced analytics with the power of NLP and machine learning in pharmaceutical data interpretation |
| title_fullStr | Enhancing drug discovery and patient care through advanced analytics with the power of NLP and machine learning in pharmaceutical data interpretation |
| title_full_unstemmed | Enhancing drug discovery and patient care through advanced analytics with the power of NLP and machine learning in pharmaceutical data interpretation |
| title_short | Enhancing drug discovery and patient care through advanced analytics with the power of NLP and machine learning in pharmaceutical data interpretation |
| title_sort | enhancing drug discovery and patient care through advanced analytics with the power of nlp and machine learning in pharmaceutical data interpretation |
| topic | Natural language processing Pharmaceutical analytics Healthcare technology Personalized Medicine Text Mining BERT |
| url | http://www.sciencedirect.com/science/article/pii/S2472630324001201 |
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