Development and Validation of an Interpretable Machine Learning Model for Prediction of the Risk of Clinically Ineffective Reperfusion in Patients Following Thrombectomy for Ischemic Stroke.

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Title: Development and Validation of an Interpretable Machine Learning Model for Prediction of the Risk of Clinically Ineffective Reperfusion in Patients Following Thrombectomy for Ischemic Stroke.
Authors: Hu, Xiaolong (AUTHOR), Qi, Dayong (AUTHOR), Li, Suya (AUTHOR), Ye, Shifei (AUTHOR), Chen, Yue (AUTHOR), Cao, Wei (AUTHOR), Du, Meng (AUTHOR), Zheng, Tianheng (AUTHOR), Li, Peng (AUTHOR), Fang, Yibin (AUTHOR)
Source: Therapeutics & Clinical Risk Management. May2025, Vol. 21, p621-631. 11p.
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  Data: Development and Validation of an Interpretable Machine Learning Model for Prediction of the Risk of Clinically Ineffective Reperfusion in Patients Following Thrombectomy for Ischemic Stroke.
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PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=185783932
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.2147/TCRM.S520362
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      – Code: eng
        Text: English
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        PageCount: 11
        StartPage: 621
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      – TitleFull: Development and Validation of an Interpretable Machine Learning Model for Prediction of the Risk of Clinically Ineffective Reperfusion in Patients Following Thrombectomy for Ischemic Stroke.
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            NameFull: Qi, Dayong
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              M: 05
              Text: May2025
              Type: published
              Y: 2025
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              Value: 21
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            – TitleFull: Therapeutics & Clinical Risk Management
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