AI and Data Science in Precision Medicine, Predictive Analytics, and Medical Practice

Erscheinungsjahr 2026
ISBN: 978-0-443-36554-6
Verlag: Elsevier Science

Buch, Englisch, 450 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 450 g

AI and Data Science in Precision Medicine, Predictive Analytics, and Medical Practice explores the transformative role of AI and data science in enhancing precision medicine, predictive analytics, and medical practice. The book covers diverse topics such as AI-driven personalized medicine, seizure prediction through EEG analysis, and the application of chaos theory in AI-driven healthcare. The volume also delves into medical practice and education, including ethical considerations, AI-driven supply chain management, and clinical documentation using natural language processing.

Furthermore, it examines AI's role in telemedicine, patient engagement, and adherence, offering innovative solutions to improve healthcare delivery and outcomes.

Autoren/Hrsg.

Boubaker, Olfa

Boussarsar, Mohamed

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Biotechnologie

Inhaltsverzeichnis

Introduction to AI and Data Science in Precision Medicine, predictive analytics and medical practice

Part 1. AI for Precision Medicine and predictive analytics
1. AI-driven Personalized Medicine: A Survey
2. AI Revolution in Healthcare: Enhancing Patient Care and Outcomes through Innovative Applications and Future Prospects
3. AI-Powered Seizure Prediction: Advancing Brain Health with EEG Analysis
4. Chaos Theory in AI-Driven Healthcare Systems: Unraveling Complex Data Patterns
5. A Novel Network-Based Methodology to Investigate Obstructive Sleep Apnea
6. Enhancing Peritoneal Dialysis Care in Tunisia: Leveraging Predictive Analytics and AI Enhancing Peritoneal Dialysis Care in Tunisia: Leveraging Predictive Analytics and AI
7. Deep Learning-Based Stroke Risk Prediction System Using Real-Time Digital Biomarker Data
8. Identification of Mental Health Crisis During COVID-19 Using Machine Learning Techniques
9. AI in Psychological Assessment: A Combined Approach of Feature Selection and Machine Learning Techniques for the Real-Time Detection of Interpersonal Trust Issues

Part 2. Medical Practice and Education topics
10. Precision medicine, omics, and treatable traits as a paradigm shift towards promising medical curriculum: A narrative review
11. Ethical Considerations in the Implementation of AI and Data Science within Healthcare Applications: A Systematic Review
12. AI-Driven Blood Supply Chain Management: A Reinforcement Learning Approach
13. Clinical Documentation and Electronic Health Records (EHR) using Natural language processing (NLP) and Optical character recognition

Part 3. Telemedicine, Patient Engagement and Adherence for healthcare monitoring
14. Patient Engagement and Adherence: Developing AI-driven solutions to improve patient engagement, medication adherence, and lifestyle modifications
15. Enhancing Telemedicine and Remote Patient Monitoring with AI and Data Science
16. Intelligent Prospective Study for Elderly Healthcare Assistance in Smart Homes with WSN
17. Conclusion and perspectives in Precision Medicine and predictive analytics

Über Autor(innen)

Boubaker, Olfa
Pr. Olfa Boubaker is a full professor at the National Institute of Applied Sciences and Technology (INSAT) at the University of Carthage, Tunisia, where she specializes in control theory, nonlinear systems, and robotics. She holds a Ph.D. in Electrical Engineering from the National Engineering School of Tunis and a Habilitation Universitaire in Control Engineering from the National Engineering School of Sfax. Prof. Boubaker is the series editor of the book series Medical Robots and Devices: New Developments and Advances."

Boussarsar, Mohamed
Mohamed Boussarsar is Professor of Intensive Care Medicine at the Faculty of Medicine of Sousse, University of Sousse, Tunisia, and Head of the Medical Intensive Care Unit at Farhat Hached University Hospital. His clinical and research interests focus on acute respiratory failure, with a particular emphasis on the optimization of non-invasive respiratory support (NIRS) and invasive mechanical ventilation (IMV). His work integrates a frugality-driven approach, aiming to adapt evidence-based practices and clinical guidelines to the realities of low- and middle-income countries.

Professor Boussarsar has conducted numerous studies to refine diagnostic strategies, predict outcomes, and improve interventions in critical care. He coordinates a long-standing NIRS master program, covering acute and chronic respiratory support, and organizes two prominent annual conferences: @-VAC (mechanical ventilation) and ICCPC (respiratory diseases). He has also led biomedical engineering innovations during the COVID-19 pandemic, including the development of ventilators and HFNC devices.

In 2023, he launched Tunisia's first post-graduate course on AI applied to healthcare, promoting AI-driven precision medicine and translational research. Professor Boussarsar is an active member of several national and international scientific societies and serves as a reviewer and editorial board member for leading journals in intensive care and pulmonology.