Book chapter · 2024
Smart Health: Advancements in Machine Learning and the Internet of Things Solutions
In Machine Learning and IoT Applications for Health Informatics, pp. 31–51, CRC Press · Published
Summary
Vajjhala and Eappen (2024) examined how machine learning (ML) and the Internet of Things (IoT), together with healthcare analytics, reshape healthcare delivery, in a review chapter without new empirical data. They identify benefits of combining ML and IoT in healthcare, such as enhanced patient care, early disease detection, operational efficiency and personalised treatment plans. They also set out the challenges of integrating ML and IoT with healthcare analytics, recommend solutions, and emphasise the importance of data privacy, model interpretability, bias mitigation and secure connectivity in deploying healthcare technology.
Practical implication. Those deploying machine learning and Internet of Things solutions in healthcare should address data privacy, model interpretability, bias mitigation and secure connectivity, which the chapter emphasises as central to deployment.
- The Problem
- Machine learning and the Internet of Things promise better healthcare, but their integration with healthcare analytics raises unresolved technical, ethical and security problems.
- The Methodology
- Review chapter on the convergence of healthcare analytics, machine learning and IoT: benefits, integration challenges and recommended solutions, ethical and security considerations, and emerging advances.
- The Core Finding
- The chapter shows that combining machine learning and IoT supports enhanced patient care, early disease detection, operational efficiency and personalised treatment, and that data privacy, model interpretability, bias mitigation and secure connectivity are prerequisites for deployment.
- The Citation
- Vajjhala, N. R., & Eappen, P. (2024). Smart Health: Advancements in Machine Learning and the Internet of Things Solutions. In P. Samui, S. S. Roy, W. Zhang, & Y.-H. Taguchi (Eds.), Machine Learning and IoT Applications for Health Informatics (pp. 31–51). CRC Press. https://doi.org/10.1201/9781003424987-3
The summary, key findings, methods, implications and relevance notes on this page are this website’s own description of the chapter, written from the published abstract and text. The official abstract and citation details are given further down.
Key findings
- Finding 1. The synergy of machine learning and IoT in healthcare is associated with enhanced patient care, early disease detection, operational efficiency and personalised treatment plans.
- Finding 2. Integrating ML and IoT with healthcare analytics raises problems and challenges for which the chapter recommends solutions.
- Finding 3. Ethical, practical and security considerations — data privacy, model interpretability, bias mitigation and secure connectivity — are emphasised as central to deploying healthcare technology.
- Finding 4. Ongoing technological advancements could further augment healthcare analytics, with a need for patient-centric approaches and attention to global health disparities.
Source: Vajjhala & Eappen (2024), In Machine Learning and IoT Applications for Health Informatics, pp. 31–51, CRC Press. DOI: 10.1201/9781003424987-3
The chapter in detail
What question does this chapter answer?
How do machine learning and the Internet of Things, working together with healthcare analytics, reshape healthcare delivery, and what technical, ethical and security challenges must be addressed to deploy them?
What did the chapter find, in detail?
The chapter examines the intersection of healthcare analytics, machine learning and IoT, focusing on benefits such as enhanced patient care, early disease detection, operational efficiency and personalised treatment plans. It sets out the problems and challenges of integrating ML and IoT with healthcare analytics and recommends solutions, addressing ethical, practical and security considerations — data privacy, model interpretability, bias mitigation and secure connectivity — and presents ongoing technological advances with an emphasis on patient-centric approaches and global health disparities.
Why does it matter?
ML and IoT are being deployed in healthcare faster than the governance around them matures. The chapter combines the technical promise with the practical and ethical conditions for safe, equitable deployment.
Methods and data
| Question | How do ML and IoT reshape healthcare analytics and delivery, and what are the challenges? |
|---|---|
| Design | Review / overview chapter |
| Benefits covered | Enhanced patient care, early disease detection, operational efficiency, personalised treatment plans |
| Challenges covered | Integration of ML and IoT with healthcare analytics; data privacy; model interpretability; bias mitigation; secure connectivity |
| Outlook | Ongoing technological advancements; patient-centric approaches; global health disparities |
| Citation | Vajjhala & Eappen (2024) · DOI 10.1201/9781003424987-3 |
Abstract
This chapter examines the transformative intersection of healthcare analytics, Machine Learning (ML), and the Internet of Things (IoT), exploring how these state-of-the-art technologies reshape healthcare delivery. This chapter focuses on the benefits brought about by the synergy of ML and IoT in the healthcare sector, such as enhanced patient care, early disease detection, operational efficiency, and personalized treatment plans. We explore the problems and challenges of integrating ML and IoT with healthcare analytics and recommend solutions in this chapter. The chapter also addresses ethical, practical, and security considerations, emphasizing the importance of data privacy, model interpretability, bias mitigation, and secure connectivity in deploying healthcare technology. Furthermore, this chapter also presents the ongoing technological advancements and their potential to augment healthcare analytics further, emphasizing the need for patient-centric approaches and addressing global health disparities. This chapter explores healthcare analytics’ current landscape and prospects, considering the integration of ML and IoT solutions.
Abstract as published in Machine Learning and IoT Applications for Health Informatics.
Limitations
- A review chapter without new empirical data; it surveys the current landscape and prospects rather than testing a specific system.
Practical implications
- Those deploying healthcare technology should attend to the ethical, practical and security considerations the chapter addresses, in particular data privacy, model interpretability, bias mitigation and secure connectivity, whose importance it emphasises.
- Those integrating machine learning and the Internet of Things with healthcare analytics can draw on the solutions the chapter recommends for the problems and challenges that this integration raises.
- Those working on technological advancements in healthcare analytics should adopt patient-centric approaches and attend to global health disparities, a need the chapter emphasises.
Drawn from Vajjhala & Eappen (2024), In Machine Learning and IoT Applications for Health Informatics, pp. 31–51, CRC Press.
When this research may be relevant
This chapter may be relevant to literature on smart health and IoT-enabled healthcare, machine learning for healthcare analytics, ethical and security requirements of connected health devices, and reviews of digital health technology for patient-centred care.
Research topics addressed: smart health; healthcare analytics; machine learning in healthcare; Internet of Things; IoT in healthcare; early disease detection; personalised treatment; data privacy; model interpretability; bias mitigation; secure connectivity; global health disparities; healthcare informatics
How to cite
- APA 7
- Vajjhala, N. R., & Eappen, P. (2024). Smart Health: Advancements in Machine Learning and the Internet of Things Solutions. In P. Samui, S. S. Roy, W. Zhang, & Y.-H. Taguchi (Eds.), Machine Learning and IoT Applications for Health Informatics (pp. 31–51). CRC Press. https://doi.org/10.1201/9781003424987-3
- MLA 9
- Vajjhala, Narasimha Rao, and Philip Eappen. “Smart Health: Advancements in Machine Learning and the Internet of Things Solutions.” Machine Learning and IoT Applications for Health Informatics, edited by Pijush Samui et al., CRC Press, 2024, pp. 31–51. https://doi.org/10.1201/9781003424987-3.
- IEEE
- N. R. Vajjhala and P. Eappen, “Smart Health: Advancements in Machine Learning and the Internet of Things Solutions,” in Machine Learning and IoT Applications for Health Informatics, P. Samui, S. S. Roy, W. Zhang, and Y.-H. Taguchi, Eds. CRC Press, 2024, pp. 31–51, doi: 10.1201/9781003424987-3.
BibTeX
@incollection{vajjhala2024smart,
title = {Smart Health: Advancements in Machine Learning and the Internet of Things Solutions},
author = {Vajjhala, Narasimha Rao and Eappen, Philip},
booktitle = {Machine Learning and IoT Applications for Health Informatics},
editor = {Samui, Pijush and Roy, Sanjiban Sekhar and Zhang, Wengang and Taguchi, Y-h.},
pages = {31--51},
year = {2024},
publisher = {CRC Press},
doi = {10.1201/9781003424987-3},
url = {https://doi.org/10.1201/9781003424987-3}
}RIS
TY - CHAP TI - Smart Health: Advancements in Machine Learning and the Internet of Things Solutions AU - Vajjhala, Narasimha Rao AU - Eappen, Philip ED - Samui, Pijush ED - Roy, Sanjiban Sekhar ED - Zhang, Wengang ED - Taguchi, Y-h. T2 - Machine Learning and IoT Applications for Health Informatics PY - 2024 DA - 2024/10/02 SP - 31 EP - 51 PB - CRC Press SN - 9781003424987 DO - 10.1201/9781003424987-3 UR - https://www.narasimharao.net/research/smart-health-machine-learning-internet-of-things/ LA - en ER -
Links and files
- DOI: https://doi.org/10.1201/9781003424987-3
- Publisher’s page: https://www.taylorfrancis.com/chapters/edit/10.1201/9781003424987-3
- This page as text: https://www.narasimharao.net/research/smart-health-machine-learning-internet-of-things/paper.md
- Permanent address of this page: https://www.narasimharao.net/research/smart-health-machine-learning-internet-of-things/
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