Development of machine learning-based models for predicting risk factors in acute cerebral infarction patients: a clinical retrospective study

In our work, the ACI risk prediction model developed by the logistic regression exhibited excellent performance. This could contribute to the identification of risk variables for ACI patients and enables clinicians timely and effective interventions.

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Publication Details

Journal
BMC Neurology
Published
2024-08-31
DOI
https://doi.org/10.1186/s12883-024-03818-6
Citations
4
Primary Topic
Acute Ischemic Stroke Management
Type
article
Field-Weighted Citation Impact
1.55
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article

Development of machine learning-based models for predicting risk factors in acute cerebral infarction patients: a clinical retrospective study

Renlin Hu, Mingzhu Chen, Jiaqi Liu, Zhou Hong et al.
4 citations
BMC Neurology
Acute Ischemic Stroke Management
1.55
article

Development of machine learning-based models for predicting risk factors in acute cerebral infarction patients: a clinical retrospective study

Renlin Hu, Mingzhu Chen, Jiaqi Liu, Zhou Hong, Shilan Xiong, Changqing Yang, Z. Mao
article en
4 citations

Abstract

In our work, the ACI risk prediction model developed by the logistic regression exhibited excellent performance. This could contribute to the identification of risk variables for ACI patients and enables clinicians timely and effective interventions.

BMC NeurologyVol. 24(1)
Nantong University (CN), Wuhan University (CN), Affiliated Hospital of Nantong University (CN), Yancheng Third People's Hospital (CN), Fifth Hospital In Wuhan (CN), Jingzhou Central Hospital (CN)
Openalex Percentile: Top 16%
Acute Ischemic Stroke Management
1.55
DOIOpen Access PDFOpenAlex
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Development of machine learning-based models for predicting risk factors in acute cerebral infarction patients: a clinical retrospective study — Renlin Hu, Mingzhu Chen, et al. · BMC Neurology (2024) | TGRS Research Map | TGRS