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

Introduction : Machine learning algorithms have emerged as powerful predictive tools in the field of acute ischemic stroke. Here, we examine the predictive performance of a machine algorithm compared to logistic regression for predicting functional outcomes in the prospective Systematic Evaluation of Patients Treated With Neurothrombectomy Devices for Acute Ischemic Stroke (STRATIS) Registry. Methods : The STRATIS Registry was a prospective, observational study of the use of the Solitaire device in acute ischemic stroke patients. Patients with posterior circulation stroke or missing 90‐day mRS were excluding from the analysis. A statistical algorithm (logistic regression) and a machine learning algorithm (decision tree) were implemented on the preprocessed dataset using 10‐fold cross‐validation method where 80% of the data were fed into the models to be trained and the remaining 20% were utilized in the test phase to evaluate the performance of the models for prediction of 90‐day mRS score as dichotomous output. Results : Of the 938 STRATIS patients, 702 with 90‐day mRS were included. The machine learning model outperformed the logistic regression model with a 0.92±0.026 Area Under Curve (AUC) score compared to a 0.88±0.028 AUC score obtained by implementing logistic regression. Conclusions : Our machine learning model delivered improved performance in comparison with the statistical model in predicting 90‐day functional outcome. More studies are needed to understand and externally validate the predictive capacity of our machine learning model.

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Searching for improvements in predicting human eye colour from DNA

International Journal of Legal Medicine ◽

10.1007/s00414-021-02645-5 ◽

2021 ◽

Author(s):

Magdalena Kukla-Bartoszek ◽

Paweł Teisseyre ◽

Ewelina Pośpiech ◽

Joanna Karłowska-Pik ◽

Piotr Zieliński ◽

...

Keyword(s):

Machine Learning ◽

Regression Models ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Sequencing Analysis ◽

Learning Approaches ◽

Human Eye ◽

Software Analysis ◽

Whole Exome ◽

Eye Colour

AbstractIncreasing understanding of human genome variability allows for better use of the predictive potential of DNA. An obvious direct application is the prediction of the physical phenotypes. Significant success has been achieved, especially in predicting pigmentation characteristics, but the inference of some phenotypes is still challenging. In search of further improvements in predicting human eye colour, we conducted whole-exome (enriched in regulome) sequencing of 150 Polish samples to discover new markers. For this, we adopted quantitative characterization of eye colour phenotypes using high-resolution photographic images of the iris in combination with DIAT software analysis. An independent set of 849 samples was used for subsequent predictive modelling. Newly identified candidates and 114 additional literature-based selected SNPs, previously associated with pigmentation, and advanced machine learning algorithms were used. Whole-exome sequencing analysis found 27 previously unreported candidate SNP markers for eye colour. The highest overall prediction accuracies were achieved with LASSO-regularized and BIC-based selected regression models. A new candidate variant, rs2253104, located in the ARFIP2 gene and identified with the HyperLasso method, revealed predictive potential and was included in the best-performing regression models. Advanced machine learning approaches showed a significant increase in sensitivity of intermediate eye colour prediction (up to 39%) compared to 0% obtained for the original IrisPlex model. We identified a new potential predictor of eye colour and evaluated several widely used advanced machine learning algorithms in predictive analysis of this trait. Our results provide useful hints for developing future predictive models for eye colour in forensic and anthropological studies.

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Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data

NeuroImage ◽

10.1016/j.neuroimage.2015.06.008 ◽

2015 ◽

Vol 118 ◽

pp. 219-230 ◽

Cited By ~ 64

Author(s):

Brent C. Munsell ◽

Chong-Yaw Wee ◽

Simon S. Keller ◽

Bernd Weber ◽

Christian Elger ◽

...

Keyword(s):

Machine Learning ◽

Treatment Outcome ◽

Outcome Prediction ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Structural Connectome ◽

Patients With Epilepsy ◽

Treatment Outcome Prediction

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

Diagnostics ◽

10.3390/diagnostics11101909 ◽

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.

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Can machine learning augment clinician adjudication of events in cardiovascular trials? A case study of major adverse cardiovascular events (MACE) across CVRM trials

European Heart Journal ◽

10.1093/eurheartj/ehab724.3061 ◽

2021 ◽

Vol 42 (Supplement_1) ◽

Author(s):

H Lea ◽

E Hutchinson ◽

A Meeson ◽

S Nampally ◽

G Dennis ◽

...

Keyword(s):

Machine Learning ◽

Clinical Trials ◽

Ischemic Stroke ◽

Cardiovascular Events ◽

Learning Algorithms ◽

Model Performance ◽

Machine Learning Algorithms ◽

Classification Models ◽

Using Data

Abstract Background and introduction Accurate identification of clinical outcome events is critical to obtaining reliable results in cardiovascular outcomes trials (CVOTs). Current processes for event adjudication are expensive and hampered by delays. As part of a larger project to more reliably identify outcomes, we evaluated the use of machine learning to automate event adjudication using data from the SOCRATES trial (NCT01994720), a large randomized trial comparing ticagrelor and aspirin in reducing risk of major cardiovascular events after acute ischemic stroke or transient ischemic attack (TIA). Purpose We studied whether machine learning algorithms could replicate the outcome of the expert adjudication process for clinical events of ischemic stroke and TIA. Could classification models be trained on historical CVOT data and demonstrate performance comparable to human adjudicators? Methods Using data from the SOCRATES trial, multiple machine learning algorithms were tested using grid search and cross validation. Models tested included Support Vector Machines, Random Forest and XGBoost. Performance was assessed on a validation subset of the adjudication data not used for training or testing in model development. Metrics used to evaluate model performance were Receiver Operating Characteristic (ROC), Matthews Correlation Coefficient, Precision and Recall. The contribution of features, attributes of data used by the algorithm as it is trained to classify an event, that contributed to a classification were examined using both Mutual Information and Recursive Feature Elimination. Results Classification models were trained on historical CVOT data using adjudicator consensus decision as the ground truth. Best performance was observed on models trained to classify ischemic stroke (ROC 0.95) and TIA (ROC 0.97). Top ranked features that contributed to classification of Ischemic Stroke or TIA corresponded to site investigator decision or variables used to define the event in the trial charter, such as duration of symptoms. Model performance was comparable across the different machine learning algorithms tested with XGBoost demonstrating the best ROC on the validation set for correctly classifying both stroke and TIA. Conclusions Our results indicate that machine learning may augment or even replace clinician adjudication in clinical trials, with potential to gain efficiencies, speed up clinical development, and retain reliability. Our current models demonstrate good performance at binary classification of ischemic stroke and TIA within a single CVOT with high consistency and accuracy between automated and clinician adjudication. Further work will focus on harmonizing features between multiple historical clinical trials and training models to classify several different endpoint events across trials. Our aim is to utilize these clinical trial datasets to optimize the delivery of CVOTs in further cardiovascular drug development. FUNDunding Acknowledgement Type of funding sources: Private company. Main funding source(s): AstraZenca Plc

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Prediction of early neurological deterioration in acute minor ischemic stroke by machine learning algorithms

Clinical Neurology and Neurosurgery ◽

10.1016/j.clineuro.2020.105892 ◽

2020 ◽

Vol 195 ◽

pp. 105892 ◽

Cited By ~ 1

Author(s):

Sang Min Sung ◽

Yoon Jung Kang ◽

Han Jin Cho ◽

Nae Ri Kim ◽

Suk Min Lee ◽

...

Keyword(s):

Machine Learning ◽

Ischemic Stroke ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Neurological Deterioration ◽

Minor Ischemic Stroke ◽

Early Neurological Deterioration

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Predicting Outcome of Endovascular Treatment for Acute Ischemic Stroke: Potential Value of Machine Learning Algorithms

Frontiers in Neurology ◽

10.3389/fneur.2018.00784 ◽

2018 ◽

Vol 9 ◽

Cited By ~ 24

Author(s):

Hendrikus J. A. van Os ◽

Lucas A. Ramos ◽

Adam Hilbert ◽

Matthijs van Leeuwen ◽

Marianne A. A. van Walderveen ◽

...

Keyword(s):

Machine Learning ◽

Ischemic Stroke ◽

Endovascular Treatment ◽

Acute Ischemic Stroke ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Potential Value ◽

Predicting Outcome

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Abstract TP434: Machine Learning for Prediction of Early Neurological Deterioration in Acute Minor Ischemic Stroke

Stroke ◽

10.1161/str.51.suppl_1.tp434 ◽

2020 ◽

Vol 51 (Suppl_1) ◽

Author(s):

Sang Min Sung ◽

Yoon Jung Kang ◽

Sung Hwan Jang ◽

Nae Ri Kim ◽

Suk Min Lee

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Deep Learning ◽

Functional Outcome ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Neurological Deterioration ◽

Minor Stroke ◽

Minor Ischemic Stroke ◽

Early Neurological Deterioration

Introduction: A significant portion of patients with acute minor stroke have poor functional outcome due to early neurological deterioration (END). The purpose of this study is to investigate the applicability of machine learning algorithms to predict END in patients with acute minor stroke. Methods: We collected clinical and neuroimaging information from patients with acute minor stroke with NIHHS score of 3 or less. Early neurological deterioration was defined as any worsening of NIHSS score within three days after admission. Poor functional outcome was defined as a modified Rankin Scale score of 2 or more. We also compared clinical and neuroimaging information between END and No END group. Four machine learning algorithms, i.e., Boosted trees, Bootstrap decision forest, Deep learning, and Logistic Regression, are selected and trained by our dataset to predict early neurological deterioration Results: A total of 739 patients were included in this study. Seventy-eight patients (10.6%) had early neurological deterioration. Among 78 patients with END, 61 (78.2%) had poor functional outcomes at 90 days after stroke onset. On multivariate analysis, NIHSS score on admission (P=0.003), hemorrhagic transformation(P=0.010), and stenosis (P=0.014) or occlusion (P=0.004) of a relevant artery were independently associated with END. Compared with four machine learning algorithms, Boosted trees, Deep learning, and Logistic Regression achieved an excellent prediction of END in patients with acute minor stroke (Boosted trees: accuracy = 0.966, F1 score = 0.8 and an area under the curve value = 0.934, Deep learning :0.966, 0.8, 0. 904, and Logistic Regression : 0.966, 0.8, 0.885). Conclusions: This study suggests that machine learning algorithms which integrate clinical and neuroimaging information accurately predict END in patients with acute minor ischemic stroke. Further studies based on an extensive data set are needed to predict END accurately for treatment strategies and better functional outcome.

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