Enhancing Ischemic Stroke Management: Leveraging Machine Learning Models for Predicting Patient Recovery After Alteplase Treatment

I'd fire anyone working on useless predicting failure to recover, rather than doing the research that leads to patient recovery!

Send me hate mail on this: oc1dean@gmail.com. I'll print your complete statement with your name and my response in my blog. Or are you afraid to engage with my stroke-addled mind? I would like to know exactly what you think stroke research is for.

 Enhancing Ischemic Stroke Management: Leveraging Machine Learning Models for Predicting Patient Recovery After Alteplase Treatment

Babak Khorsand, Atena vaghf, Vahide Salimi, Maryam Zand, Seyed Abdolreza Ghoreishi

Aim: 

 Ischemic stroke remains a leading global cause of morbidity and mortality, emphasizing the need for timely treatment strategies. This study aimed to develop a machine learning model to predict clinical outcomes in ischemic stroke patients undergoing Alteplase therapy, thereby supporting more personalized care. 

Methods: 

Data from 457 ischemic stroke patients were analyzed, including 50 demographic, clinical, laboratory, and imaging variables. Five machine learning algorithms, k-nearest neighbors (KNN), support vector machines (SVM), Naïve Bayes (NB), decision trees (DT), and random forest (RF), were evaluated for predictive accuracy. The primary evaluation metrics were sensitivity and F-measure, with an additional feature importance analysis to identify high-impact predictors. Results: The Random Forest model showed the highest predictive reliability, outperforming other algorithms in sensitivity and F-measure. Furthermore, by using only the top-ranked features identified from the feature importance analysis, the model maintained comparable performance, suggesting a streamlined yet effective predictive approach. 

Conclusion: 

Our findings highlight the potential of machine learning in optimizing ischemic stroke treatment outcomes. Random Forest, in particular, proved effective as a decision-support tool, offering clinicians valuable insights for more tailored treatment approaches. This model's use in clinical settings could significantly enhance patient outcomes by informing better treatment decisions.

Ischemic Stroke

Neurology

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