Natural Language Processing for Healthcare

Natural Language Processing for Healthcare: The Rise of Intelligent Assistants addresses the critical gap between cutting-edge AI research and its practical applications in healthcare, offering an accessible guide tailored to the unique challenges of medical environments. It highlights how NLP technologies are revolutionizing patient care, medical documentation, and clinical decision-making while emphasizing ethical, legal, and interoperability considerations. Structured into four sections, the book begins by laying foundational knowledge in NLP and healthcare data, covering concepts such as tokenization, medical ontologies like UMLS and SNOMED CT, machine learning models, including BioBERT and ClinicalBERT, and emerging impacts of large language models like GPT.

The applications section explores real-world implementations of intelligent assistants, such as virtual health chatbots, clinical documentation tools, conversational AI for patient engagement, and voice recognition integrated into electronic health records. Technical chapters provide insights into system architectures, evaluation metrics, data privacy, security, and interoperability standards like FHIR. The final section looks ahead to future directions including multilingual NLP, federated learning for privacy preservation, and the evolving landscape of AI-driven healthcare assistants. This book is an indispensable resource for a broad audience.

• Bridges AI research and healthcare practice with accessible, healthcare-focused NLP insights for clinical and operational use
• Provides practical guidance on designing and deploying intelligent virtual assistants to enhance patient care and engagement
• Addresses ethical, legal, and interoperability challenges unique to healthcare NLP applications
• Explores cutting-edge technologies, including large language models and federated learning in real-world medical contexts
• Equips data scientists and clinicians with tools to analyze unstructured medical data and improve clinical decision-making

Dr. Laxmi Shaw is a Researcher and Faculty at Texas A&M University–Victoria, United States, where her work centres on adversarial machine learning, large language models (LLMs), healthcare analytics, fraud detection, and energy management systems. She was previously a Postdoctoral Scholar at Texas State University and a Senior Postdoctoral Fellow (Volunteer) at the University of Texas at Austin. She has worked on projects with Samsung Research & Development and Carrier Corporation (UTC-HRDC). With over a decade of combined research and industry experience, Dr. Shaw has co-authored 5 books and published more than 40 peer-reviewed papers in journals, international conferences, and edited volumes. Her research spans AI/ML security, EEG signal processing, IoT-enabled anomaly detection, Siamese networks, adversarial robustness in LLMs, and GPU-accelerated healthcare analytics. She is a Senior Member of IEEE and an active reviewer for several journals.She earned her Ph.D. in Electrical Engineering with a specialization in Artificial Intelligence and Machine Learning from the prestigious Indian Institute of Technology (IIT) Kharagpur, India. She also holds a Master of Technology (M.Tech) in Instrumentation and Electronics Engineering from Jadavpur University, and a Bachelor of Engineering (B.E.) in Electronics and Instrumentation Engineering from Sambalpur University, Odisha. She has authored three books and over 35 peer-reviewed papers on AI/ML security, EEG processing, IoT anomaly detection, and GPU-accelerated healthcare analytics. A Senior IEEE member and award-winning researcher, she actively reviews for leading journals and is committed to ethical, explainable, and secure AI, especially in healthcare and adversarial contexts.

Dr. Shubham Mahajan is an academic and researcher, member of IEEE, ACM, and IAENG. He earned a B.Tech from Baba Ghulam Shah Badshah University, an M.Tech from Chandigarh University, and a PhD from Shri Mata Vaishno Devi University. He is currently Assistant Professor at Amity University, Haryana. His research spans artificial intelligence and image processing, including video compression, image segmentation, fuzzy entropy, nature-inspired optimization, data mining, machine learning, robotics, and optical communications. He holds patents internationally and has published widely in high-impact venues; he has edited several Scopus-indexed books. He has received multiple awards for research excellence and travel support from IEEE, among others. He has served as IEEE Campus Ambassador at premier institutes and promotes international collaborations. He participates in technical program committees and editorial boards for conferences and journals, shaping discourse in AI and image processing.

Dr. Kamal Upreti is an Associate Professor of Computer Science at CHRIST (Deemed to be University), Ghaziabad. He holds , a Ph.D. in Computer Science & Engineering, and a postdoctoral fellowship at National Taipei University of Business, Taiwan, funded by MHRD.

With teaching, research, and industry exposure, he has produced numerous patents and publications. His interests span modern physics, data analytics, cybersecurity, ML, healthcare, embedded systems, and cloud computing. Notable projects include Hydrastore in Japan, IPDS in India, and an ICMR-funded cardiovascular-prediction project with GB Pant and AIIMS Delhi.

Dr. Upreti serves as session chair, keynote speaker, trainer, and faculty developer, and has been honored as Best Teacher, Best Researcher, and an M.Tech Gold Medalist.