Machine Learning Techniques in Adaptive and Personalized Systems for Health and Wellness

2022/07/27 by Oladapo Oyebode, Jonathon Fowles, Jonathon R. Fowles +2 · 3 citations
Computer Science · Health Professions · #Context-Aware Activity Recognition Systems #Machine Learning in Healthcare #Mobile Health and mHealth Applications

paper · doi:10.1080/10447318.2022.2089085

openalex publication_date 2022/07/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30

Abstract

Traditional health systems mostly rely on rules created by experts to offer adaptive interventions to patients. However, with recent advances in artificial intelligence (AI) and machine learning (ML) techniques, health-related systems are becoming more sophisticated with higher accuracy in providing more personalized interventions or treatments to individual patients. In this paper, we present an extensive literature review to explore the current trends in ML-based adaptive systems for health and well-being. We conduct a systematic search for articles published between January 2011 and April 2022 and selected 87 articles that met our inclusion criteria for review. The selected articles target 18 health and wellness domains including disease management, assistive healthcare, medical diagnosis, mental health, physical activity, dietary management, health monitoring, substance use, smoking cessation, homeopathy remedy finding, patient privacy, mobile health (mHealth) apps finder, clinician knowledge representation for neonatal emergency care, dental and oral health, medication management, disease surveillance, medical specialty recommendation, and health awareness. Our review focuses on five key areas across the target domains: data collection strategies, model development process, ML techniques utilized, model evaluation techniques, as well as adaptive or personalization strategies for health and wellness interventions. We also identified various technical and methodological challenges including data volume constraints, data quality issues, data diversity or variability issues, infrastructure-related issues, and suitability of interventions which offer directions for future work in this area. Finally, we offer recommendations for tackling these challenges, leveraging on technological advances such as multimodality, Cloud technology, online learning, edge computing, automatic re-calibration, Bluetooth auto-reconnection, feedback pipeline, federated learning, explainable AI, and co-creation of health and wellness interventions.

Citations

• Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects
• A survey on feature selection methods
• Personalized Nutrition by Prediction of Glycemic Responses
• Automated machine learning: Review of the state-of-the-art and opportunities for healthcare
• The Future of Digital Health with Federated Learning
• Federated Learning: Challenges, Methods, and Future Directions
• The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI

Cited by

• Personal Care Utility (PCU): Building the Health Infrastructure for Everyday Insight and Guidance
• A Laplace diffusion-based transformer model for heart rate forecasting within daily activity context
• Advancements in machine learning and deep learning for early detection and management of mental health disorder

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