Explanation of Machine Learning Models Revealed Influential Factors of Early Outcomes in Acute Ischemic Stroke: A registry database study

database[Title] 2022-01-31

Summary:

CONCLUSIONS: Machine learning models are feasible in predicting early stroke outcomes. An enriched feature bank could improve model performance. Initial neurological and daily activity levels determined the activity independence at hospital discharge. In addition, physiological and laboratory surveillance aided in predicting in-hospital deterioration. The utilization of SHAP explanatory method successfully transformed machine learning predictions into clinically meaningful results.

Link:

https://pubmed.ncbi.nlm.nih.gov/35072631/?utm_source=Other&utm_medium=rss&utm_campaign=pubmed-2&utm_content=12QQbiNmM99eUQGIX1JjHIKcROC1Vzv4sOS-2S_LNI19uG_Yrk&fc=20220129225649&ff=20220131044917&v=2.17.5

From feeds:

📚BioDBS Bibliography » database[Title]

Tags:

Authors:

Po-Yuan Su, Yi-Chia Wei, Hao Luo, Chi-Hung Liu, Wen-Yi Huang, Kuan-Fu Chen, Ching-Po Lin, Hung-Yu Wei, Tsong-Hai Lee

Date tagged:

01/31/2022, 04:49

Date published:

01/24/2022, 06:00