Clinical, radiological, and radiomics feature-based explainable machine learning models for prediction of neurological deterioration and 90-day outcomes in mild intracerebral hemorrhage

Clinical, radiological, and radiomics feature-based explainable machine learning models for prediction of neurological deterioration and 90-day outcomes in mild intracerebral hemorrhage

Abstract Background The risks and prognosis of mild intracerebral hemorrhage (ICH) patients were easily overlooked by clinicians. Our goal was to use machine learning (ML) methods to predict mild ICH patients’ neurological deterioration (ND) and 90-day prognosis. Methods This prospective study recru...

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Bibliographic Details
Main Authors: Weixiong Zeng, Jiaying Chen, Linling Shen, Genghong Xia, Jiahui Xie, Shuqiong Zheng, Zilong He, Limei Deng, Yaya Guo, Jingjing Yang, Yijun Lv, Genggeng Qin, Weiguo Chen, Jia Yin, Qiheng Wu
Format: Article
Language:English
Published: BMC 2025-05-01
Series:BMC Medical Imaging
Subjects:
Intracerebral hemorrhage
Neurological deterioration
Modified rankin scale
Machine learning
Radiomics
Computed tomography
Online Access:https://doi.org/10.1186/s12880-025-01717-x
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https://doi.org/10.1186/s12880-025-01717-x

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