Interpretable machine learning model to predict rupture of small intracranial aneurysms and facilitate clinical decision-Reference-Cited by-同舟云学术

Interpretable machine learning model to predict rupture of small intracranial aneurysms and facilitate clinical decision

Published:2022-08-23 Issue:11 Volume:43 Page:6371-6379
ISSN:1590-1874
Container-title:Neurological Sciences
language:en
Short-container-title:Neurol Sci

Author:

Xiong WeiGen,Chen TingTing,Li Jun,Xiang Lan,Zhang Cheng,Xiang Liang,Li YingBin,Chu Dong,Wu YueZhang,Jie Qiong,Qiu RunZe,Xu ZeYue,Zou JianJun^ORCID,Fan HongWei,Zhao ZhiHong

Funder

the Special Scientific Research Fund Project of Jiangsu Research Hospital Association - Precisely Drug use - CSPC Pharmaceutical Group Co., Ltd.

National Natural Science Foundation of China

Nanjing Pharmaceutical Association - Changzhou Siyao - Hospital Pharmaceutical Research Fund

Publisher

Springer Science and Business Media LLC

Subject

Psychiatry and Mental health,Neurology (clinical),Dermatology,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s10072-022-06351-x.pdf

Reference31 articles.

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4. Bender MT, Wendt H, Monarch T, Beaty N, Lin LM, Huang J, Coon A, Tamargo RJ, Colby GP (2018) Small aneurysms account for the majority and increasing percentage of aneurysmal subarachnoid hemorrhage: A 25-year, single institution study. Neurosurg 83(4):692–699. https://doi.org/10.1093/neuros/nyx484

5. Greving JP, Wermer MJ, Brown RD Jr, Morita A, Juvela S, Yonekura M, Ishibashi T, Torner JC, Nakayama T, Rinkel GJ, Algra A (2014) Development of the PHASES score for prediction of risk of rupture of intracranial aneurysms: a pooled analysis of six prospective cohort studies. Lancet Neurol 13(1):59–66. https://doi.org/10.1016/S1474-4422(13)70263-1

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