Modeling of pre-transplantation liver viability with spatial-temporal smooth variable selection

Development and validation of comprehensive clinical outcome prediction models for acute ischaemic stroke in anterior circulation based on machine learning

The current prediction models for the clinical outcome of acute ischaemic stroke (AIS) remain insufficient for individualized patient management strategies. We aimed to investigate machine learning (ML) performance in the clinical outcome prediction of AIS in anterior circulation and evaluate the clinical outcome by combining the quantitative evaluation indicators of perfusion features and basic clinical information. Four ML classifiers, support vector machine (SVM), naive Bayes (NB), logistic regression (LR), and random forest (RF) were trained on a cohort of 389 adult patients (training cohort [70 %]; external validation cohort [30 %]) from the Acute Stroke Center Registry of Huashan Hospital. Model performance was compared by a range of learning metrics. Most imaging parameters were strongly correlated with the outcome (range, 0.57 to 0.81), and the correlation between relative cerebral blood flow (rCBF) < 30 % and clinical outcome was the strongest (ρ = 0.81). As the reference parameters increased, the performance of the four models was greatly improved [SVM (AUC: from 0.79 to 0.99, F1-score: from 0.61 to 0.90), RF (AUC: from 0.88 to 0.98, F1-score: from 0.71 to 0.96), LR (AUC: from 0.80 to 0.97, F1-score: from 0.64 to 0.95), and NB (AUC: from 0.74 to 0.97, F1-score: from 0.66 to 0.92)]. The ensemble classifier model with all parameters had the highest F1-score (0.97). All the ML models, jointly considering the basic clinical information and quantitative evaluation indicators of computed tomography perfusion (CTP), showed good performance in the prediction of clinical outcome of AIS in anterior circulation.