This book provides a comprehensive explanation of precision (i.e., personalized) healthcare and explores how it can be advanced through Artificial Intelligence (AI) and other data-driven technologies. From improving the diagnosis, treatment, and monitoring of many medical conditions to the effective implementation of precise patient care, this book will help you understand datasets produced from digital health technologies and IoT and teach you how to employ analytical methods such as convolutional neural networks and Deep Learning to analyze that data. Open-source toolkits support Machine Learning by providing accessible and ready-to-use code for common algorithms. Most are available for Python, the programming language favored for developing Machine Learning algorithms. Scikit-learn is a Python module containing image processing and Machine Learning techniques built on SciPy and enables algorithms for clustering, classification, and regression, such as naïve Bayes, decision trees, random forests, k-means, and support vector machines. NLTK, or Natural Language Toolkit, is a collection of libraries used in natural language processing (NLP).
Author(s): Arjun Panesar
Publisher: Apress
Year: 2023
Language: english
Pages: 183
Table of Contents
About the Author
About the Technical Reviewer
Acknowledgments
Introduction
Chapter 1: Introduction
From Personalized Medicine to Precision Health
Why Precision Health? Why Now?
Shifting Paradigms from Volume to Value
Social Determinants of Health
Why Diversity Is Essential Within Precision Health
Summary
Chapter 2: What Is Precision Health?
The Five Ps of Precision Health
Prediction and Prevention
Personalization of Treatment
Participation
Population
Considerations of Precision Health
Cost
Genes Are Just the Beginning
Health Equality
Unfulfilled Power of Data
Engagement
High Touch Means High Tech
Phenomics
Digital Transformation
Applying Precision Health: The P5H Precision Healthcare Continuum
Health Stages
Stage A
Stage B
Stage C
Stage D
Optimization Across Stages
Intervention Levels
Level 1
Level 2
Level 3
Level 4
Summary
Chapter 3: Data and the Digital Phenotype
Data Forms and Types
Forms
Types
Sources of Data
Sensors
Digital Phenotyping
Digital Twin
Data Challenges
Measurement and Completeness
Lack of Data on Social Determinants of Health
Privacy and Security
Cost
Disconnected from Data
Limited Adoption of Common Data Models
Expanding Beyond Qualitative Data
A Paradigm for Acting on Data
Turning Data into Information, Knowledge, and Wisdom
Summary
Chapter 4: Artificial Intelligence and Machine Learning in Precision Health
The Three Types of AI
Artificial Narrow Intelligence
Artificial General Intelligence
Artificial Superintelligence
A Brief Introduction to Machine Learning
Framework for Machine Learning
Software and Toolkits
Explainable AI
Applications of AI Assisted Precision Health in Practice
Clinical Decision Support
Behavioral Change Interventions and Lifestyle Medicine
New Treatments, Definitions of Disease, and Points of Intervention
Digital Twins
Health Promoting Chatbots
Voice Recognition
Summary
Chapter 5: Risks and Ethical Challenges of Precision Health
Responsible Development and Ethical AI Principles
Epistemic Principles
Interpretability
Reliability and Safety
General Ethical AI Principles
Bias, Inclusivity, and Fairness
Transparency and Accountability
Lawfulness
Data Privacy and Security
Human Agency
Beneficence
Redesigning Care and the Patient-Clinician Relationship
Health Inequalities
Theology
Preparing the Profession
Summary
Chapter 6: Future of Precision Healthcare
Precision Care from Birth to Death
Nanotechnology
DNA Manipulation and Gene Therapy
Smart Sensors
Bioprinting
Brain Computer Interfacing
Smart Habitats
Digital Health Education
Literacy
Changing Roles
Quality
Ability
Accessibility and Equity
New Forms of Training
Collaboration Between Academia and Industry
Summary
Chapter 7: Precision Healthcare in Practice
Delivery of Specialist Multidisciplinary Weight Management to Hospital-Based Patients Through a Digital Tool
Objective
Methods
How Does Personalization Appear?
Results
Discussion
Conclusion
Building on Our Evidence
Understanding People’s Attitudes Toward Data for the Optimization of a Specialist Podiatry Service for People with Long-Term Health Conditions
Objective
Methods
Results
Discussion
Conclusion
Impact of the Findings
Evaluation of a Digital Intervention for the Self-Management of Type 2 Diabetes and Prediabetes
Objectives
Methods
Results
Discussion
Conclusion
Impact of the Findings
Voice-Based Symptom Monitoring and AI-Based Rehabilitation for Patients with Long COVID
Background
Objective
Implementation Plan
Risks
Evaluation
Potential Impact
Developing a Digital Tool to Support Daily Behavioral Change for Children and Young People to Support Healthier Lives
Objective
Methods
Milestones
Evaluation
Impact of the Project
Summary
Index
