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

Amann, Julia

doi:10.1007/978-3-030-74188-4_5

Cited by 6 publications

(3 citation statements)

References 85 publications

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“…For example, supervised ML has been shown to predict attention-deficit and hyperactivity disorder (ADHD), autism, schizophrenia, and Tourette syndrome and suicidal ideation. ML-based models have been shown to more accurately predict the severity of depression or anxiety ( 52 ), and stroke ( 53 ) and allow clinicians to identify which interventions will be most effective for which patient populations.…”

Section: Ai-enabled Decision Support Technologies For Mental Healthcarementioning

confidence: 99%

“…There also needs to be more separation between studies that measure degree of use of AI technologies, and those that evaluate the efficacy of these technologies; in order to gain the trust and understanding of clinicians, developers need to demonstrate that these technologies work better than existing service delivery ( 17 , 62 ). For example, in the case of stroke risk prediction and prevention, this means that novel ML-based approaches need to compete against established models to win clinicians' and patients' trust ( 53 ).…”

Section: Ai-enabled Decision Support Technologies For Mental Healthcarementioning

confidence: 99%

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Pragmatic AI-augmentation in mental healthcare: Key technologies, potential benefits, and real-world challenges and solutions for frontline clinicians

Kellogg¹,

Sadeh‐Sharvit

2022

Front. Psychiatry

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The integration of artificial intelligence (AI) technologies into mental health holds the promise of increasing patient access, engagement, and quality of care, and of improving clinician quality of work life. However, to date, studies of AI technologies in mental health have focused primarily on challenges that policymakers, clinical leaders, and data and computer scientists face, rather than on challenges that frontline mental health clinicians are likely to face as they attempt to integrate AI-based technologies into their everyday clinical practice. In this Perspective, we describe a framework for “pragmatic AI-augmentation” that addresses these issues by describing three categories of emerging AI-based mental health technologies which frontline clinicians can leverage in their clinical practice—automation, engagement, and clinical decision support technologies. We elaborate the potential benefits offered by these technologies, the likely day-to-day challenges they may raise for mental health clinicians, and some solutions that clinical leaders and technology developers can use to address these challenges, based on emerging experience with the integration of AI technologies into clinician daily practice in other healthcare disciplines.

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Section: Ai-enabled Decision Support Technologies For Mental Healthcarementioning

confidence: 99%

Section: Ai-enabled Decision Support Technologies For Mental Healthcarementioning

confidence: 99%

Pragmatic AI-augmentation in mental healthcare: Key technologies, potential benefits, and real-world challenges and solutions for frontline clinicians

Kellogg¹,

Sadeh‐Sharvit

2022

Front. Psychiatry

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show abstract

“…A main reason for this can be seen in the slow uptake and adoption of AI-powered tools due to regulatory uncertainty, organizational challenges, and attitudinal barriers [1,8,[10][11][12]. Moreover, while the scholarly debate on the potential risks and benefits of medical AI is in full swing [13][14][15][16][17], the views of prospective users and beneficiaries of these novel technologies are often missing from the picture and consequently disregarded when it comes to product design and governance structures [11,[18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Expectations and attitudes towards medical artificial intelligence: A qualitative study in the field of stroke

et al. 2023

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Introduction Artificial intelligence (AI) has the potential to transform clinical decision-making as we know it. Powered by sophisticated machine learning algorithms, clinical decision support systems (CDSS) can generate unprecedented amounts of predictive information about individuals’ health. Yet, despite the potential of these systems to promote proactive decision-making and improve health outcomes, their utility and impact remain poorly understood due to their still rare application in clinical practice. Taking the example of AI-powered CDSS in stroke medicine as a case in point, this paper provides a nuanced account of stroke survivors’, family members’, and healthcare professionals’ expectations and attitudes towards medical AI. Methods We followed a qualitative research design informed by the sociology of expectations, which recognizes the generative role of individuals’ expectations in shaping scientific and technological change. Semi-structured interviews were conducted with stroke survivors, family members, and healthcare professionals specialized in stroke based in Germany and Switzerland. Data was analyzed using a combination of inductive and deductive thematic analysis. Results Based on the participants’ deliberations, we identified four presumed roles that medical AI could play in stroke medicine, including an administrative, assistive, advisory, and autonomous role AI. While most participants held positive attitudes towards medical AI and its potential to increase accuracy, speed, and efficiency in medical decision making, they also cautioned that it is not a stand-alone solution and may even lead to new problems. Participants particularly emphasized the importance of relational aspects and raised questions regarding the impact of AI on roles and responsibilities and patients’ rights to information and decision-making. These findings shed light on the potential impact of medical AI on professional identities, role perceptions, and the doctor-patient relationship. Conclusion Our findings highlight the need for a more differentiated approach to identifying and tackling pertinent ethical and legal issues in the context of medical AI. We advocate for stakeholder and public involvement in the development of AI and AI governance to ensure that medical AI offers solutions to the most pressing challenges patients and clinicians face in clinical care.

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Predictive modelling and identification of key risk factors for stroke using machine learning

Hassan,

Gulzar Ahmad,

Ullah Munir

et al. 2024

Sci Rep

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Strokes are a leading global cause of mortality, underscoring the need for early detection and prevention strategies. However, addressing hidden risk factors and achieving accurate prediction become particularly challenging in the presence of imbalanced and missing data. This study encompasses three imputation techniques to deal with missing data. To tackle data imbalance, it employs the synthetic minority oversampling technique (SMOTE). The study initiates with a baseline model and subsequently employs an extensive range of advanced models. This study thoroughly evaluates the performance of these models by employing k-fold cross-validation on various imbalanced and balanced datasets. The findings reveal that age, body mass index (BMI), average glucose level, heart disease, hypertension, and marital status are the most influential features in predicting strokes. Furthermore, a Dense Stacking Ensemble (DSE) model is built upon previous advanced models after fine-tuning, with the best-performing model as a meta-classifier. The DSE model demonstrated over 96% accuracy across diverse datasets, with an AUC score of 83.94% on imbalanced imputed dataset and 98.92% on balanced one. This research underscores the remarkable performance of the DSE model, compared to the previous research on the same dataset. It highlights the model's potential for early stroke detection to improve patient outcomes.

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

Cited by 6 publications

References 85 publications

Pragmatic AI-augmentation in mental healthcare: Key technologies, potential benefits, and real-world challenges and solutions for frontline clinicians

Pragmatic AI-augmentation in mental healthcare: Key technologies, potential benefits, and real-world challenges and solutions for frontline clinicians

Expectations and attitudes towards medical artificial intelligence: A qualitative study in the field of stroke

Predictive modelling and identification of key risk factors for stroke using machine learning

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