Machine Learning in Stroke Medicine: Opportunities and Challenges for Risk Prediction and Prevention

Abstract

Abstract Stroke 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.

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