Abstract

AbstractStroke is one of the leading causes of mortality and disability worldwide, causing individual hardship and high economic cost for society. Reducing the global burden of stroke depends on a multi-pronged mission, and experts agree an important strategy in this mission is prevention. Prevention success can be bolstered through the strategic development and adoption of risk prediction tools. However, there are several limitations to risk prediction models currently available. A solution to some of these limitations may be found in machine learning (ML), a promising tool that can improve our ability to assess risk and ultimately prevent strokes.This chapter surveys the global burden of stroke and describes current practices for reducing stroke incidence and stroke mortality rates. In particular, the chapter reviews how ML applications are applied to stroke risk prediction and prevention and identifies important technological and methodological challenges for using ML in these contexts. The chapter concludes by drawing the readers’ attention to some of the questions and ethical challenges that arise as clinicians widely adopt ML-based applications in practice.

Highlights

  • Precision medicine aims to individualize prevention, diagnostics, and therapeutics by understanding differences in individuals’ genetics, lifestyle, and environment [2]

  • In addition to capturing the perceived benefits and dangers of using these new technologies, the authors assessed patients’ readiness for using them. Their findings indicated that only half of the patients who participated in the study viewed digital tools and artificial intelligence (AI) in healthcare as an opportunity, while 11% even considered them a danger, fearing that these will lead to the replacement of humans

  • Novel machine learning (ML)-driven approaches to stroke risk prediction allow researchers to overcome some of the challenges frequently associated with traditional risk prediction models

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Summary

Introduction

“The essence of practicing medicine has been obtaining as much data about the patient’s health or disease as possible and making decisions based on that. Prevention plays an instrumental role in reducing the global burden of stroke [14], and the strategic adoption and development of AI-driven prediction tools can contribute substantially to this mission [1, 13]. These new tools open welcome opportunities and introduce new questions for us, . The chapter reviews how ML applications are applied to stroke risk prediction and prevention and identifies important technological and methodological challenges for using AI in these contexts. The chapter concludes by drawing the readers’ attention to some of the questions and ethical challenges that arise as clinicians widely adopt ML-based applications in practice

Burden of Stroke
Machine Learning in Stroke Medicine
Stroke Prevention: A Public Health Priority
The Advent of Data-Driven Risk Prediction Models
From Data-Driven Risk Prediction to Stroke Prevention
Technological, Methodological, and Ethical Challenges
Data Sourcing
Application Development
Deployment in Clinical Practice
Findings
Conclusion

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