Tissue outcome prediction in hyperacute ischemic stroke: Comparison of machine learning models

2021 ◽  
pp. 0271678X2110243
Author(s):  
Joseph Benzakoun ◽  
Sylvain Charron ◽  
Guillaume Turc ◽  
Wagih Ben Hassen ◽  
Laurence Legrand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Random Forests ◽  
Outcome Prediction ◽  
Infarct Volume ◽  
Gradient Boosting ◽  
Equal Sign ◽  
Clinical Method ◽  
Thresholding Method ◽  
Weighted Sequence

Machine Learning (ML) has been proposed for tissue fate prediction after acute ischemic stroke (AIS), with the aim to help treatment decision and patient management. We compared three different ML models to the clinical method based on diffusion-perfusion thresholding for the voxel-based prediction of final infarct, using a large MRI dataset obtained in a cohort of AIS patients prior to recanalization treatment. Baseline MRI (MRI0), including diffusion-weighted sequence (DWI) and Tmax maps from perfusion-weighted sequence, and 24-hr follow-up MRI (MRI24h) were retrospectively collected in consecutive 394 patients AIS patients (median age = 70 years; final infarct volume = 28mL). Manually segmented DWI24h lesion was considered the final infarct. Gradient Boosting, Random Forests and U-Net were trained using DWI, apparent diffusion coefficient (ADC) and Tmax maps on MRI0 as inputs to predict final infarct. Tissue outcome predictions were compared to final infarct using Dice score. Gradient Boosting had significantly better predictive performance (median [IQR] Dice Score as for median age, maybe you can replace the comma with an equal sign for consistency 0.53 [0.29–0.68]) than U-Net (0.48 [0.18–0.68]), Random Forests (0.51 [0.27–0.66]), and clinical thresholding method (0.45 [0.25–0.62]) ( P < 0.001). In this benchmark of ML models for tissue outcome prediction in AIS, Gradient Boosting outperformed other ML models and clinical thresholding method and is thus promising for future decision-making.

scholarly journals Machine Learning-Based Three-Month Outcome Prediction in Acute Ischemic Stroke: A Single Cerebrovascular-Specialty Hospital Study in South Korea

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1909
Author(s):  
Dougho Park ◽  
Eunhwan Jeong ◽  
Haejong Kim ◽  
Hae Wook Pyun ◽  
Haemin Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Functional Outcome ◽  
Outcome Prediction ◽  
Prediction Models ◽  
Gradient Boosting ◽  
Support Vector ◽  
Extreme Gradient Boosting

Background: Functional outcomes after acute ischemic stroke are of great concern to patients and their families, as well as physicians and surgeons who make the clinical decisions. We developed machine learning (ML)-based functional outcome prediction models in acute ischemic stroke. Methods: This retrospective study used a prospective cohort database. A total of 1066 patients with acute ischemic stroke between January 2019 and March 2021 were included. Variables such as demographic factors, stroke-related factors, laboratory findings, and comorbidities were utilized at the time of admission. Five ML algorithms were applied to predict a favorable functional outcome (modified Rankin Scale 0 or 1) at 3 months after stroke onset. Results: Regularized logistic regression showed the best performance with an area under the receiver operating characteristic curve (AUC) of 0.86. Support vector machines represented the second-highest AUC of 0.85 with the highest F1-score of 0.86, and finally, all ML models applied achieved an AUC > 0.8. The National Institute of Health Stroke Scale at admission and age were consistently the top two important variables for generalized logistic regression, random forest, and extreme gradient boosting models. Conclusions: ML-based functional outcome prediction models for acute ischemic stroke were validated and proven to be readily applicable and useful.

scholarly journals Using a Multiclass Machine Learning Model to Predict the Outcome of Acute Ischemic Stroke Requiring Reperfusion Therapy

Diagnostics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 80
Author(s):  
I-Min Chiu ◽  
Wun-Huei Zeng ◽  
Chi-Yung Cheng ◽  
Shih-Hsuan Chen ◽  
Chun-Hung Richard Lin
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Outcome Prediction ◽  
Reperfusion Therapy ◽  
Gradient Boosting ◽  
Stroke Patients ◽  
Extreme Gradient Boosting ◽  
Machine Learning Models

Prediction of functional outcome in ischemic stroke patients is useful for clinical decisions. Previous studies mostly elaborate on the prediction of favorable outcomes. Miserable outcomes, which are usually defined as modified Rankin Scale (mRS) 5–6, should be considered as well before further invasive intervention. By using a machine learning algorithm, we aimed to develop a multiclass classification model for outcome prediction in acute ischemic stroke patients requiring reperfusion therapy. This was a retrospective study performed at a stroke medical center in Taiwan. Patients with acute ischemic stroke who visited between January 2016 and December 2019 and who were candidates for reperfusion therapy were included. Clinical outcomes were classified as favorable outcome, intermediate outcome, and miserable outcome. We developed four different multiclass machine learning models (Logistic Regression, Supportive Vector Machine, Random Forest, and Extreme Gradient Boosting) to predict clinical outcomes and compared their performance to the DRAGON score. A sample of 590 patients was included in this study. Of them, 180 (30.5%) had favorable outcomes and 152 (25.8%) had miserable outcomes. All selected machine learning models outperformed the DRAGON score on accuracy of outcome prediction (Logistic Regression: 0.70, Supportive Vector Machine: 0.67, Random Forest: 0.69, and Extreme Gradient Boosting: 0.67, vs. DRAGON: 0.51, p < 0.001). Among all selected models, Logistic Regression also had a better performance than the DRAGON score on positive predictive value, sensitivity, and specificity. Compared with the DRAGON score, the multiclass machine learning approach showed better performance on the prediction of the 3-month functional outcome of acute ischemic stroke patients requiring reperfusion therapy.

scholarly journals Predicting 1-Hour Thrombolysis Effect of r-tPA in Patients With Acute Ischemic Stroke Using Machine Learning Algorithm

2022 ◽  
Vol 12 ◽  
Author(s):  
Bin Zhu ◽  
Jianlei Zhao ◽  
Mingnan Cao ◽  
Wanliang Du ◽  
Liuqing Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Learning Algorithm ◽  
Degradation Products ◽  
Early Stage ◽  
Area Under The Curve ◽  
Recursive Feature Elimination ◽  
Gradient Boosting ◽  
Data Sets

Background: Thrombolysis with r-tPA is recommended for patients after acute ischemic stroke (AIS) within 4.5 h of symptom onset. However, only a few patients benefit from this therapeutic regimen. Thus, we aimed to develop an interpretable machine learning (ML)–based model to predict the thrombolysis effect of r-tPA at the super-early stage.Methods: A total of 353 patients with AIS were divided into training and test data sets. We then used six ML algorithms and a recursive feature elimination (RFE) method to explore the relationship among the clinical variables along with the NIH stroke scale score 1 h after thrombolysis treatment. Shapley additive explanations and local interpretable model–agnostic explanation algorithms were applied to interpret the ML models and determine the importance of the selected features.Results: Altogether, 353 patients with an average age of 63.0 (56.0–71.0) years were enrolled in the study. Of these patients, 156 showed a favorable thrombolysis effect and 197 showed an unfavorable effect. A total of 14 variables were enrolled in the modeling, and 6 ML algorithms were used to predict the thrombolysis effect. After RFE screening, seven variables under the gradient boosting decision tree (GBDT) model (area under the curve = 0.81, specificity = 0.61, sensitivity = 0.9, and F1 score = 0.79) demonstrated the best performance. Of the seven variables, activated partial thromboplastin clotting time (time), B-type natriuretic peptide, and fibrin degradation products were the three most important clinical characteristics that might influence r-tPA efficiency.Conclusion: This study demonstrated that the GBDT model with the seven variables could better predict the early thrombolysis effect of r-tPA.

scholarly journals Automated prediction of final infarct volume in patients with large-vessel occlusion acute ischemic stroke

2021 ◽  
Vol 51 (1) ◽  
pp. E13
Author(s):  
Rania Abdelkhaleq ◽  
Youngran Kim ◽  
Swapnil Khose ◽  
Peter Kan ◽  
Sergio Salazar-Marioni ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Infarct Volume ◽  
Large Vessel Occlusion ◽  
Infarct Core ◽  
Vessel Occlusion ◽  
Automated Method ◽  
Machine Learning Model ◽  
Time Of Presentation

OBJECTIVE In patients with large-vessel occlusion (LVO) acute ischemic stroke (AIS), determinations of infarct size play a key role in the identification of candidates for endovascular stroke therapy (EVT). An accurate, automated method to quantify infarct at the time of presentation using widely available imaging modalities would improve screening for EVT. Here, the authors aimed to compare the performance of three measures of infarct core at presentation, including an automated method using machine learning. METHODS Patients with LVO AIS who underwent successful EVT at four comprehensive stroke centers were identified. Patients were included if they underwent concurrent noncontrast head CT (NCHCT), CT angiography (CTA), and CT perfusion (CTP) with Rapid imaging at the time of presentation, and MRI 24 to 48 hours after reperfusion. NCHCT scans were analyzed using the Alberta Stroke Program Early CT Score (ASPECTS) graded by neuroradiology or neurology expert readers. CTA source images were analyzed using a previously described machine learning model named DeepSymNet (DSN). Final infarct volume (FIV) was determined from diffusion-weighted MRI sequences using manual segmentation. The primary outcome was the performance of the three infarct core measurements (NCHCT-ASPECTS, CTA with DSN, and CTP-Rapid) to predict FIV, which was measured using area under the receiver operating characteristic (ROC) curve (AUC) analysis. RESULTS Among 76 patients with LVO AIS who underwent EVT and met inclusion criteria, the median age was 67 years (IQR 54–76 years), 45% were female, and 37% were White. The median National Institutes of Health Stroke Scale score was 16 (IQR 12–22), and the median NCHCT-ASPECTS on presentation was 8 (IQR 7–8). The median time between when the patient was last known to be well and arrival was 156 minutes (IQR 73–303 minutes), and between NCHCT/CTA/CTP to groin puncture was 73 minutes (IQR 54–81 minutes). The AUC was obtained at three different cutoff points: 10 ml, 30 ml, and 50 ml FIV. At the 50-ml FIV cutoff, the AUC of ASPECTS was 0.74; of CTP core volume, 0.72; and of DSN, 0.82. Differences in AUCs for the three predictors were not significant for the three FIV cutoffs. CONCLUSIONS In a cohort of patients with LVO AIS in whom reperfusion was achieved, determinations of infarct core at presentation by NCHCT-ASPECTS and a machine learning model analyzing CTA source images were equivalent to CTP in predicting FIV. These findings have suggested that the information to accurately predict infarct core in patients with LVO AIS was present in conventional imaging modalities (NCHCT and CTA) and accessible by machine learning methods.

scholarly journals Comparing Machine Learning Models and Hybrid Geostatistical Methods Using Environmental and Soil Covariates for Soil pH Prediction

2020 ◽  
Vol 9 (4) ◽  
pp. 276
Author(s):  
Panagiotis Tziachris ◽  
Vassilis Aschonitis ◽  
Theocharis Chatzistathis ◽  
Maria Papadopoulou ◽  
Ioannis (John) D. Doukas
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Random Forests ◽  
Soil Ph ◽  
Prediction Accuracy ◽  
Gradient Boosting ◽  
Soil Parameters ◽  
Geostatistical Methods ◽  
Significant Difference ◽  
Hyperparameter Selection

In the current paper we assess different machine learning (ML) models and hybrid geostatistical methods in the prediction of soil pH using digital elevation model derivates (environmental covariates) and co-located soil parameters (soil covariates). The study was located in the area of Grevena, Greece, where 266 disturbed soil samples were collected from randomly selected locations and analyzed in the laboratory of the Soil and Water Resources Institute. The different models that were assessed were random forests (RF), random forests kriging (RFK), gradient boosting (GB), gradient boosting kriging (GBK), neural networks (NN), and neural networks kriging (NNK) and finally, multiple linear regression (MLR), ordinary kriging (OK), and regression kriging (RK) that although they are not ML models, they were used for comparison reasons. Both the GB and RF models presented the best results in the study, with NN a close second. The introduction of OK to the ML models’ residuals did not have a major impact. Classical geostatistical or hybrid geostatistical methods without ML (OK, MLR, and RK) exhibited worse prediction accuracy compared to the models that included ML. Furthermore, different implementations (methods and packages) of the same ML models were also assessed. Regarding RF and GB, the different implementations that were applied (ranger-ranger, randomForest-rf, xgboost-xgbTree, xgboost-xgbDART) led to similar results, whereas in NN, the differences between the implementations used (nnet-nnet and nnet-avNNet) were more distinct. Finally, ML models tuned through a random search optimization method were compared with the same ML models with their default values. The results showed that the predictions were improved by the optimization process only where the ML algorithms demanded a large number of hyperparameters that needed tuning and there was a significant difference between the default values and the optimized ones, like in the case of GB and NN, but not in RF. In general, the current study concluded that although RF and GB presented approximately the same prediction accuracy, RF had more consistent results, regardless of different packages, different hyperparameter selection methods, or even the inclusion of OK in the ML models’ residuals.

Abstract P308: Machine Learning Based Models of the 30-Day Readmission for Stroke Patients Using Electronic Health Record Data

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Negar Darabi ◽  
Niyousha Hosseinichimeh ◽  
Anthony Noto ◽  
Ramin Zand ◽  
Vida Abedi
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Care Delivery ◽  
Gradient Boosting ◽  
Support Vector ◽  
Stroke Patients ◽  
Electronic Health Record Data ◽  
Percutaneous Gastrostomy ◽  
Extreme Gradient Boosting ◽  
Electronic Health

Background: At a personalized level, identification of patients at higher risk of 30-day readmission and in need of special clinical attention could lower their chances of readmission. While at a system’s level, reducing hospital readmission improves the overall quality of care delivery and reduces the associated cost burden. Objective: To enhance understanding of the predictors of 30-day readmission after ischemic stroke and identify high-risk individuals. We aimed to compare the performance and the predictive power of machine learning-based methods and identify the best model. Method: The electronic health records (EHR) of acute ischemic stroke patients were extracted from two tertiary centers within the Geisinger Health System between January 1, 2015, and October 7, 2018. A total of 61 variables, including clinical variables, demographical characteristics, discharge status, and type of health insurance were used in this study. Patients were randomly split for model development (80%) and testing (20%). Random forest, gradient boosting machine, extreme gradient boosting (XGBoost), support vector machine, and logistic regression, were developed to predict the 30-day readmission after stroke. The models were evaluated based on the area under the curve (AUC), sensitivity, specificity, and positive predictive value (PPV). Results: A total of 3,184 patients with ischemic stroke (mean age: 71±13.90 years, men: 51.06%) were included in this study. From the 3,184, 301 (9.40%) were readmitted within 30-day. The best performance was obtained when XGBoost was used with ROSE-sampling. The AUC for the test set was 0.74 (95% CI: 0.64-0.78) with PPV of 0.43. The top four predictors of the 30-day readmission model were National Institutes of Health Stroke Scale score above 24, insert an indwelling urinary catheter, hypercoagulable state, and percutaneous gastrostomy. Conclusions: Machine learning model can be designed to predict 30-day readmission after stroke using structured data from EHR. Among the five algorithms analyzed, XGBoost had the best performance.

scholarly journals Machine Learning for Outcome Prediction of Acute Ischemic Stroke Post Intra-Arterial Therapy

PLoS ONE ◽  
2014 ◽  
Vol 9 (2) ◽  
pp. e88225 ◽  
Author(s):  
Hamed Asadi ◽  
Richard Dowling ◽  
Bernard Yan ◽  
Peter Mitchell
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Outcome Prediction

scholarly journals Functional Outcome Prediction in Ischemic Stroke: A Comparison of Machine Learning Algorithms and Regression Models

2020 ◽  
Vol 11 ◽  
Author(s):  
Shakiru A. Alaka ◽  
Bijoy K. Menon ◽  
Anita Brobbey ◽  
Tyler Williamson ◽  
Mayank Goyal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Functional Outcome ◽  
Regression Models ◽  
Outcome Prediction ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

scholarly journals Improving Sports Outcome Prediction Process Using Integrating Adaptive Weighted Features and Machine Learning Techniques

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1563
Author(s):  
Chi-Jie Lu ◽  
Tian-Shyug Lee ◽  
Chien-Chih Wang ◽  
Wei-Jen Chen
Keyword(s):  
Machine Learning ◽  
Outcome Prediction ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Adaptive Weighting ◽  
Stochastic Gradient Boosting ◽  
Basketball Game ◽  
Extreme Gradient Boosting

Developing an effective sports performance analysis process is an attractive issue in sports team management. This study proposed an improved sports outcome prediction process by integrating adaptive weighted features and machine learning algorithms for basketball game score prediction. The feature engineering method is used to construct designed features based on game-lag information and adaptive weighting of variables in the proposed prediction process. These designed features are then applied to the five machine learning methods, including classification and regression trees (CART), random forest (RF), stochastic gradient boosting (SGB), eXtreme gradient boosting (XGBoost), and extreme learning machine (ELM) for constructing effective prediction models. The empirical results from National Basketball Association (NBA) data revealed that the proposed sports outcome prediction process could generate a promising prediction result compared to the competing models without adaptive weighting features. Our results also showed that the machine learning models with four game-lags information and adaptive weighting of power could generate better prediction performance.

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