Natural Language Processing in Healthcare Informatics: Challenges and Future Directions

Natural Language Processing in Healthcare Informatics: Challenges and Future Directions is an exploration into the transformative role of NLP and AI technologies in modern healthcare systems. The book delves into foundational concepts, advanced deep learning techniques, and cutting-edge applications of NLP in clinical decision support, electronic health record analysis, medical literature mining, patient-provider communication, and personalised medicine.

Structured across ten detailed chapters, the volume covers both theoretical foundations and practical implementations. Early chapters introduce AI and NLP in healthcare, highlighting applications in telehealth, wearable technologies, robotic surgery, and FDA-approved AI devices. Subsequent chapters examine challenges unique to healthcare NLP, including data quality and standardisation, linguistic variability, computational limitations, and ethical considerations such as bias, transparency, and patient privacy.

The book also provides in-depth insights into deep learning approaches for medical text analysis, preprocessing and annotation strategies, the evaluation of large language models for RNA interactions, and the application of Bi-LSTM architectures for advanced healthcare NLP tasks. Innovative chapters explore the role of knowledge graphs in rare disease prediction, AI and ML for focused ultrasound treatments, mathematical modelling using fuzzy numbers for healthcare NLP trends, and AI/ML integration in drug discovery and personalised medicine.

Key Features:

-Detailed discussions and case studies on the applications of AI and NLP in healthcare, including telehealth, EHR analysis, and medical imaging.
-In-depth coverage of challenges in healthcare NLP encompassing data quality, linguistic variability, computational requirements, and ethical considerations.
-Explores deep learning techniques: RNNs, CNNs, Transformers, Bi-LSTM, and their application to medical text.
-Gives practical guidance on data preprocessing, annotation, and evaluation for healthcare NLP tasks.
-Provides insight into emerging trends by integrating theoretical concepts with real-world implementations, coding examples, and model workflows in Python and MATLAB.