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1 |
Quantitative EEG reactivity and machine learning for prognostication in hypoxic-ischemic brain injury
واکنش کمی EEG و یادگیری ماشین برای پیش آگهی در آسیب مغزی هیپوکسیک-ایسکمیک-2019 Objective: Electroencephalogram (EEG) reactivity is a robust predictor of neurological recovery after cardiac
arrest, however interrater-agreement among electroencephalographers is limited. We sought to
evaluate the performance of machine learning methods using EEG reactivity data to predict good longterm
outcomes in hypoxic-ischemic brain injury.
Methods: We retrospectively reviewed clinical and EEG data of comatose cardiac arrest subjects.
Electroencephalogram reactivity was tested within 72 h from cardiac arrest using sound and pain stimuli.
A Quantitative EEG (QEEG) reactivity method evaluated changes in QEEG features (EEG spectra, entropy,
and frequency features) during the 10 s before and after each stimulation. Good outcome was defined as
Cerebral Performance Category of 1–2 at six months. Performance of a random forest classifier was compared
against a penalized general linear model (GLM) and expert electroencephalographer review.
Results: Fifty subjects were included and sixteen (32%) had good outcome. Both QEEG reactivity methods
had comparable performance to expert EEG reactivity assessment for good outcome prediction (mean
AUC 0.8 for random forest vs. 0.69 for GLM vs. 0.69 for expert review, respectively; p non-significant).
Conclusions: Machine-learning models utilizing quantitative EEG reactivity data can predict long-term
outcome after cardiac arrest.
Significance: A quantitative approach to EEG reactivity assessment may support prognostication in cardiac
arrest. Keywords: EEG reactivity | Quantitative EEG | Hypoxic-ischemic encephalopathy | Cardiac arrest | Machine learning |
مقاله انگلیسی |
2 |
Predictive model of cardiac arrest in smokers using machine learning technique based on Heart Rate Variability parameter
مدل پیش بینی ایست قلبی در افراد سیگاری با استفاده از روش یادگیری ماشین بر اساس پارامتر تنوع ضربان قلب-2019 Cardiac arrest is a severe heart anomaly that results in billions of annual casualties. Smoking is a specific
hazard factor for cardiovascular pathology, including coronary heart disease, but data on smoking and
heart death not earlier reviewed. The Heart Rate Variability (HRV) parameters used to predict cardiac
arrest in smokers using machine learning technique in this paper. Machine learning is a method of computing
experience based on automatic learning and enhances performances to increase prognosis. This
study intends to compare the performance of logistical regression, decision tree, and random forest
model to predict cardiac arrest in smokers. In this paper, a machine learning technique implemented
on the dataset received from the data science research group MITU Skillogies Pune, India. To know the
patient has a chance of cardiac arrest or not, developed three predictive models as 19 input feature of
HRV indices and two output classes. These model evaluated based on their accuracy, precision, sensitivity,
specificity, F1 score, and Area under the curve (AUC). The model of logistic regression has achieved an
accuracy of 88.50%, precision of 83.11%, the sensitivity of 91.79%, the specificity of 86.03%, F1 score of
0.87, and AUC of 0.88. The decision tree model has arrived with an accuracy of 92.59%, precision of
97.29%, the sensitivity of 90.11%, the specificity of 97.38%, F1 score of 0.93, and AUC of 0.94. The model
of the random forest has achieved an accuracy of 93.61%, precision of 94.59%, the sensitivity of 92.11%,
the specificity of 95.03%, F1 score of 0.93 and AUC of 0.95. The random forest model achieved the best
accuracy classification, followed by the decision tree, and logistic regression shows the lowest classification
accuracy. Keywords: Cardiac arrest | Heart Rate Variability | Machine learning | Accuracy | Precision | Area under the curve |
مقاله انگلیسی |
3 |
Outcome prediction of out-of-hospital cardiac arrest with presumed cardiac aetiology using an advanced machine learning technique
پیش بینی نتیجه ایست قلبی خارج از بیمارستان با اتیولوژی قلب فرضی با استفاده از یک روش پیشرفته یادگیری ماشین-2019 Background: Outcome prediction for patients with out-of-hospital cardiac arrest (OHCA) has the possibility to detect patients who could have been
potentially saved. Advanced machine learning techniques have recently been developed and employed for clinical studies. In this study, we aimed to
establish a prognostication model for OHCA with presumed cardiac aetiology using an advanced machine learning technique.
Methods and Results: Cohort data from a prospective multi-centre cohort study for OHCA patients transported by an ambulance in the Kanto area of
Japan between January 2012 and March 2013 (SOS-KANTO 2012 study) were analysed in this study. Of 16,452 patients, data for OHCA patients aged
18 years with presumed cardiac aetiology were retrieved, and were divided into two groups (training set: n = 5718, between January 1, 2012 and
December 12, 2012; test set: n = 1608, between January 1, 2013 and March 31, 2013). Of 421 variables observed during prehospital and emergency
department settings, 35 prehospital variables, or 35 prehospital and 18 in-hospital variables, were used for outcome prediction of 1-year survival using a
random forest method. In validation using the test set, prognostication models trained with 35 variables, or 53 variables for 1-year survival showed area
under the receiver operating characteristics curve (AUC) values of 0.943 (95% CI [0.930, 0.955]) and 0.958 (95% CI [0.948, 0.969]), respectively.
Conclusions: The advanced machine learning technique showed favourable prediction capability for 1-year survival of OHCA with presumed cardiac
aetiology. These models can be useful for detecting patients who could have been potentially saved. Keywords: Out-of-hospital cardiac arrest |Resuscitation | Outcome prediction | Machine learning |
مقاله انگلیسی |
4 |
Prediction of good neurological recovery after out-of-hospital cardiac arrest: A machine learning analysis
پیش بینی بهبود عصبی خوب بعد از ایست قلبی خارج از بیمارستان: تجزیه و تحلیل یادگیری ماشین-2019 Background: This study aimed to train, validate and compare predictive models that use machine learning analysis for good neurological recovery in
OHCA patients.
Methods: Adult OHCA patients who had a presumed cardiac etiology and a sustained return of spontaneous circulation between 2013 and 2016 were
analyzed; 80% of the individuals were analyzed for training and 20% were analyzed for validation. We developed using six machine learning algorithms:
logistic regression (LR), extreme gradient boosting (XGB), support vector machine, random forest, elastic net (EN), and neural network. Variables that
could be obtained within 24 hours of the emergency department visit were used. The area under the receiver operation curve (AUROC) was calculated to
assess the discrimination. Calibration was assessed by the Hosmer–Lemeshow test. Reclassification was assessed by using the continuous net
reclassification index (NRI).
Results: A total of 19,860 OHCA patients were included in the analysis. Of the 15,888 patients in the training group, 2228 (14.0%) had a good
neurological recovery; of the 3972 patients in the validation group, 577 (14.5%) had a good neurological recovery. The LR, XGB, and EN models showed
the highest discrimination powers (AUROC (95% CI)) of 0.949 (0.941–0.957) for all), and all three models were well calibrated (Hosmer–Lemeshow test:
p >0.05). The XGB model reclassified patients according to their true risk better than the LR model (NRI: 0.110), but the EN model reclassified patients
worse than the LR model (NRI:
1.239).
Conclusion: The best performing machine learning algorithm was the XGB and LR algorithm . Keywords: Out-of-hospital cardiac arrest | Outcome | Machine learning analysis |
مقاله انگلیسی |