Abstract 1122‐000106: Comparing Machine Learning Algorithms and Regression Models for Predicting Functional Outcome in the STRATIS Registry

2021 ◽  
Vol 1 (S1) ◽  
Author(s):  
Mouhammad A Jumaa ◽  
Zeinab Zoghi ◽  
Syed Zaidi ◽  
Nils Mueller‐Kronast ◽  
Osama Zaidat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Functional Outcome ◽  
Learning Algorithms ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Systematic Evaluation ◽  
Machine Learning Model

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.

Detection and defense of cyberattacks on the machine learning control of robotic systems

2021 ◽  
pp. 154851292110438
Author(s):  
George W Clark ◽  
Todd R Andel ◽  
J Todd McDonald ◽  
Tom Johnsten ◽  
Tom Thomas
Keyword(s):  
Machine Learning ◽  
Autonomous Vehicles ◽  
Defense Mechanisms ◽  
Autonomous Vehicle ◽  
Learning Algorithms ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Robotic Systems ◽  
Machine Learning Model ◽  
Attack Surface

Robotic systems are no longer simply built and designed to perform sequential repetitive tasks primarily in a static manufacturing environment. Systems such as autonomous vehicles make use of intricate machine learning algorithms to adapt their behavior to dynamic conditions in their operating environment. These machine learning algorithms provide an additional attack surface for an adversary to exploit in order to perform a cyberattack. Since an attack on robotic systems such as autonomous vehicles have the potential to cause great damage and harm to humans, it is essential that detection and defenses of these attacks be explored. This paper discusses the plausibility of direct and indirect cyberattacks on a machine learning model through the use of a virtual autonomous vehicle operating in a simulation environment using a machine learning model for control. Using this vehicle, this paper proposes various methods of detection of cyberattacks on its machine learning model and discusses possible defense mechanisms to prevent such attacks.

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

2018 ◽  
Vol 9 ◽  
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

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

Stroke ◽  
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.

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

Abstract WP343: Machine Learning Predicts Sex Difference in Acute Ischemic Stroke

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Yun-Ju Lai ◽  
Frank W Blixt ◽  
Louise D McCullough
Keyword(s):  
Machine Learning ◽  
Sex Differences ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Immune Cell ◽  
Learning Algorithms ◽  
Cytokine Expression ◽  
Machine Learning Algorithms ◽  
Computer Algorithms ◽  
Cell Network

Introduction: Stroke is a common cause of physical disability. Women generally suffer from more severe strokes, have poorer stroke outcomes, and higher mortality than that of men. Cytokines play an important role in post-stroke inflammation. Prior studies have examined differences in individual cytokine levels in patients with acute ischemic stroke (AIS), but comprehensive cytokine expression profiling across different sex and clinical characteristics are lacking. Hypothesis: Stroke is a sexually dimorphic disease with well-known sex differences in immune cell prevalence, cytokine expression, and outcome. A comprehensive cytokine and immune cell network may help identify sex-specific immune response and further provide guidance for stroke research in females. Methods: Patients with AIS were recruited from 2011-2015 at a Comprehensive Stroke Center. Multiplex analysis (Luminex 200 IS) was used to measure serum levels of 30 common cytokines. Data were analyzed with SPSS 26.0 (IBM) and machine learning algorithms. Spearman’s correlation, Mann-Whitney U test, and two-way ANOVA analyses were used to determine the relationships among the variables. The network between cytokines and immune cell types was predicted by CIBERSORT and modified ssGSEA in R package. Results: We examined sex differences in serum cytokine profiles on stroke severity and immune cells profiles using 144 patients with AIS. Among 30 cytokines, IFN-A2, IFNγ, IL-1RA, IL-6, IL-8, IP-10, RANTES, TNFα, and VEGF were found to have statistically significant differences between male and female. Additionally, female survivors with higher admission NIHSS exhibited higher levels of IFN-A2, IFNγ, IL-6, and IL-8 (F=2.722, p=.011; F=2.245, p=.034; F=7.626, p<.001; F=4.599, p<.001, respectively). A cytokine-immune cell network was created using computer algorithms resulting in identification of an upregulation of Th22 in the female. Sex-specific expression of Th22 cells was then validated in human PBMC. Conclusion: Our study suggests sex is an important factor which determines clinical outcome. Reducing Th22 may improve stroke recovery in females. Analyzing clinical data using machine learning algorithms can identify prognostic indicators of stroke.

scholarly journals Machine learning algorithms vs. thresholding to segment ischemic regions in patients with acute ischemic stroke

2021 ◽  
pp. 1-1
Author(s):  
Luca Tomasetti ◽  
Liv Jorunn Hllesli ◽  
Kjersti Engan ◽  
Kathinka Dhli Kurz ◽  
Martin Wilhelm Kurz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

scholarly journals Comparative analysis, applications, and interpretation of electronic health record-based stroke phenotyping methods

2020 ◽  
Author(s):  
Phyllis Thangaraj ◽  
Benjamin R Kummer ◽  
Tal Lorberbaum ◽  
Mitchell S.V. Elkind ◽  
Nicholas P Tatonetti
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Learning Algorithms ◽  
Case Control ◽  
Machine Learning Algorithms ◽  
Care Hospital ◽  
Diagnosis Codes ◽  
Wide Range ◽  
Electronic Health

Abstract Background: Accurate identification of acute ischemic stroke (AIS) patient cohorts is essential for a wide range of clinical investigations. Automated phenotyping methods that leverage electronic health records (EHRs) represent a fundamentally new approach cohort identification without current laborious and ungeneralizable generation of phenotyping algorithms. We systematically compared and evaluated the ability of machine learning algorithms and case-control combinations to phenotype acute ischemic stroke patients using data from an EHR.Materials and Methods: Using structured patient data from the EHR at a tertiary-care hospital system, we built and evaluated machine learning models to identify patients with AIS based on 75 different case-control and classifier combinations. We then estimated the prevalence of AIS patients across the EHR. Finally, we externally validated the ability of the models to detect AIS patients without AIS diagnosis codes using the UK Biobank.Results: Across all models, we found that the mean AUROC for detecting AIS was 0.963±0.0520 and average precision score 0.790±0.196 with minimal feature processing. Classifiers trained with cases with AIS diagnosis codes and controls with no cerebrovascular disease codes had the best average F1 score (0.832±0.0383). In the external validation, we found that the top probabilities from a model-predicted AIS cohort were significantly enriched for AIS patients without AIS diagnosis codes (60-150 fold over expected). Conclusions: Our findings support machine learning algorithms as a generalizable way to accurately identify AIS patients without using process-intensive manual feature curation. When a set of AIS patients is unavailable, diagnosis codes may be used to train classifier models.

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