Prediction of Functional Outcome in Stroke Patients with Proximal Middle Cerebral Artery Occlusions Using Machine Learning Models

被引:10
|
作者
Ozkara, Burak B. [1 ]
Karabacak, Mert [2 ]
Hamam, Omar [3 ]
Wang, Richard [3 ]
Kotha, Apoorva [3 ]
Khalili, Neda [3 ]
Hoseinyazdi, Meisam [3 ]
Chen, Melissa M. [1 ]
Wintermark, Max [1 ]
Yedavalli, Vivek S. [3 ]
机构
[1] MD Anderson Canc Ctr, Dept Neuroradiol, Houston, TX 77030 USA
[2] Mt Sinai Hlth Syst, Dept Neurosurg, New York, NY 10029 USA
[3] Johns Hopkins Univ Hosp, Russell H Morgan Dept Radiol & Radiol Sci, Baltimore, MD 21287 USA
关键词
ischemic stroke; machine learning; medical decision making; middle cerebral artery; artificial intelligence; ACUTE ISCHEMIC-STROKE; MECHANICAL THROMBECTOMY; VESSEL OCCLUSION; SCORE;
D O I
10.3390/jcm12030839
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
At present, clinicians are expected to manage a large volume of complex clinical, laboratory, and imaging data, necessitating sophisticated analytic approaches. Machine learning-based models can use this vast amount of data to create forecasting models. We aimed to predict short- and medium-term functional outcomes in acute ischemic stroke (AIS) patients with proximal middle cerebral artery (MCA) occlusions using machine learning models with clinical, laboratory, and quantitative imaging data as inputs. Included were consecutive AIS patients with MCA M1 and proximal M2 occlusions. The XGBoost, LightGBM, CatBoost, and Random Forest were used to predict the outcome. Minimum redundancy maximum relevancy was used for selecting features. The primary outcomes were the National Institutes of Health Stroke Scale (NIHSS) shift and the modified Rankin Score (mRS) at 90 days. The algorithm with the highest area under the receiver operating characteristic curve (AUROC) for predicting the favorable and unfavorable outcome groups at 90 days was LightGBM. Random Forest had the highest AUROC when predicting the favorable and unfavorable groups based on the NIHSS shift. Using clinical, laboratory, and imaging parameters in conjunction with machine learning, we accurately predicted the functional outcome of AIS patients with proximal MCA occlusions.
引用
收藏
页数:16
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