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ردیف | عنوان | نوع |
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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 |
Prediction of antiepileptic drug treatment outcomes of patients with newly diagnosed epilepsy by machine learning
پیش بینی نتایج درمان دارویی ضد صرع بیماران مبتلا به صرع تازه کشف شده با یادگیری ماشین-2019 Objective: The objective of this study was to build a supervised machine learning-based classifier, which can accurately
predict the outcomes of antiepileptic drug (AED) treatment of patients with newly diagnosed epilepsy.
Methods: We collected information from 287 patients with newly diagnosed epilepsy between 2009 and
2017 at the Second Affiliated Hospital of Zhejiang University. Patients were prospectively followed up for at
least 3 years. A number of features, including demographic features,medical history, and auxiliary examinations
(electroencephalogram [EEG] and magnetic resonance imaging [MRI]) are selected to distinguish patients with
different remission outcomes. Seizure outcomes classified as remission and never remission. In addition, remission
is further divided into early remission and late remission. Five classical machine learning algorithms,
i.e., Decision Tree, Random Forest, Support Vector Machine, XGBoost, and Logistic Regression, are selected and
trained by our dataset to get classification models.
Results: Our study shows that 1) comparedwith the other four algorithms, the XGBoost algorithmbased machine
learning model achieves the best prediction performance of the AEDtreatment outcomes between remission and
never remission patientswith an F1 score of 0.947 and an area under the curve (AUC) value of 0.979; 2) The best
discriminative factor for remission and never remission patients is higher number of seizures before treatment
(N3); 3) XGBoost-based machine learning model also offers the best prediction between early remission and
later remission patients, with an F1 score of 0.836 and an AUC value of 0.918; 4) multiple seizure type has the
highest dependence to the categories of early and late remission patients.
Significances: Our XGBoost-based machine learning classifier accurately predicts the most probable AED treatment
outcome of a patient after he/she finishes all the standard examinations for the epilepsy disease. The
classifiers prediction result could help disease guide counseling and eventually improve treatment strategies Keywords: Machine learning | Epilepsy | Epilepsy remission | Antiepileptic drug | Outcome prediction |
مقاله انگلیسی |
3 |
Deep Representation Learning for Individualized Treatment Effect Estimation using Electronic Health Records
یادگیری بازنمایی عمیق برای ارزیابی اثر درمانی شخصی با استفاده از سوابق الکترونیکی بهداشت-2019 Utilizing clinical observational data to estimate individualized treatment effects (ITE)
is a challenging task, as confounding inevitably exists in clinical data. Most of the existing
models for ITE estimation tackle this problem by creating unbiased estimators of the
treatment effects. Although valuable, learning a balanced representation is sometimes
directly opposed to the objective of learning an effective and discriminative model for
ITE estimation. We propose a novel hybrid model bridging multi-task deep learning and
K-nearest neighbors (KNN) for ITE estimation. In detail, the proposed model firstly
adopts multi-task deep learning to extract both outcome-predictive and treatment-specific
latent representations from Electronic Health Records (EHR), by jointly performing the
outcome prediction and treatment category classification. Thereafter, we estimate
counterfactual outcomes by KNN based on the learned hidden representations. We
validate the proposed model on a widely used semi-simulated dataset, i.e. IHDP, and a
real-world clinical dataset consisting of 736 heart failure (HF) patients. The performance
of our model remains robust and reaches 1.7 and 0.23 in terms of Precision in the
estimation of heterogeneous effect (PEHE) and average treatment effect (ATE),
respectively, on IHDP dataset, and 0.703 and 0.796 in terms of accuracy and F1 score
respectively, on HF dataset. The results demonstrate that the proposed model achieves
competitive performance over state-of-the-art models. In addition, the results reveal
several findings which are consistent with existing medical domain knowledge, and
discover certain suggestive hypotheses that could be validated through further
investigations in the clinical domain. Keywords: Individualized Treatment Effect Estimation | Counterfactual Inference | Deep Representation Learning | Multi-task Learning | K-Nearest Neighbors |
مقاله انگلیسی |
4 |
Survival outcome prediction in cervical cancer: Cox models vs deep-learning model
پیش بینی نتیجه بقا در سرطان دهانه رحم: مدل های کاکس در مقابل مدل یادگیری عمیق-2019 BACKGROUND: Historically, the Cox proportional hazard regression
model has been the mainstay for survival analyses in oncologic research.
The Cox proportional hazard regression model generally is used based on
an assumption of linear association. However, it is likely that, in reality,
there are many clinicopathologic features that exhibit a nonlinear association
in biomedicine.
OBJECTIVE: The purpose of this study was to compare the deeplearning
neural network model and the Cox proportional hazard regression
model in the prediction of survival in women with cervical cancer.
STUDY DESIGN: This was a retrospective pilot study of consecutive
cases of newly diagnosed stage IeIV cervical cancer from 2000e2014.
A total of 40 features that included patient demographics, vital signs,
laboratory test results, tumor characteristics, and treatment types were
assessed for analysis and grouped into 3 feature sets. The deeplearning
neural network model was compared with the Cox proportional
hazard regression model and 3 other survival analysis models for
progression-free survival and overall survival. Mean absolute error and
concordance index were used to assess the performance of these 5
models.
RESULTS: There were 768 women included in the analysis. The median
age was 49 years, and the majority were Hispanic (71.7%). The majority of
tumors were squamous (75.3%) and stage I (48.7%). The median followup
time was 40.2 months; there were 241 events for recurrence and
progression and 170 deaths during the follow-up period. The deeplearning
model showed promising results in the prediction of
progression-free survival when compared with the Cox proportional hazard
regression model (mean absolute error, 29.3 vs 316.2). The deep-learning
model also outperformed all the other models, including the Cox proportional
hazard regression model, for overall survival (mean absolute
error, Cox proportional hazard regression vs deep-learning, 43.6 vs 30.7).
The performance of the deep-learning model further improved when more
features were included (concordance index for progression-free survival:
0.695 for 20 features, 0.787 for 36 features, and 0.795 for 40 features).
There were 10 features for progression-free survival and 3 features for
overall survival that demonstrated significance only in the deep-learning
model, but not in the Cox proportional hazard regression model. There
were no features for progression-free survival and 3 features for overall
survival that demonstrated significance only in the Cox proportional hazard
regression model, but not in the deep-learning model.
CONCLUSION: Our study suggests that the deep-learning neural
network model may be a useful analytic tool for survival prediction in
women with cervical cancer because it exhibited superior performance
compared with the Cox proportional hazard regression model. This novel
analytic approach may provide clinicians with meaningful survival information
that potentially could be integrated into treatment decision-making
and planning. Further validation studies are necessary to support this pilot
study. Key words: Cox proportional hazard | cervical cancer | deep learning | survival prediction |
مقاله انگلیسی |
5 |
The risks of risk: Regulating the use of machine learning for psychosis prediction
ریسک ریسک ها: تنظیم استفاده از یادگیری ماشین برای پیش بینی روان پریشی-2019 Recent advances in Machine Learning (ML) have the potential to revolutionise psychosis prediction and psychiatric
assessment. This article has two objectives. First, it clarifies which aspects of English Law are relevant in
order to regulate the use of ML in clinical research on psychosis prediction. It is argued that its lawful implementation
will depend upon the legal requirements regarding the balance between potential harms and
benefits, particularly with reference to: (i) any additional risks introduced by the use of ML for data analysis and
outcome prediction; and (ii) the inclusion of vulnerable research populations such as minors or incapacitated
adults. Second, this article investigates how clinical prediction via ML might affect the practice of risk assessment
under mental health legislation, with reference to English Law. It is argued that there is a potential for virtuous
applications of clinical prediction in psychiatry. However, reaffirming the distinction between psychosis risk and
risk of harm is paramount. Establishing psychosis risk and assessing a persons risk of harm are discrete practices,
and so should remain when using artificial intelligence for psychiatric assessment. Evaluating whether clinical
prediction via ML might benefit individuals with psychosis will depend on which risk we try to assess and on
what we try to predict, whether this is psychosis transition, a psychotic relapse, self-harm and suicidality, or
harm to others. Keywords: Psychosis | Machine learning | Risk | Prediction | Regulation |
مقاله انگلیسی |
6 |
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 |
7 |
Evaluating machine learning performance in predicting injury severity in agribusiness industries
ارزیابی عملکرد یادگیری ماشینی در پیش بینی شدت جراحات در صنایع کشاورزی-2019 Although machine learning methods have been used as an outcome prediction tool in many fields, their utilization
in predicting incident outcome in occupational safety is relatively new. This study tests the performance
of machine learning techniques in modeling and predicting occupational incidents severity with respect to accessible
information of injured workers in agribusiness industries using workers’ compensation claims. More
than 33,000 incidents within agribusiness industries in the Midwest of the United States for 2008–2016 were
analyzed. The total cost of incidents was extracted and classified from workers’ compensation claims. Supervised
machine learning algorithms for classification (support vector machines with linear, quadratic, and RBF kernels,
Boosted Trees, and Naïve Bayes) were applied. The models can predict injury severity classification based on
injured body part, body group, nature of injury, nature group, cause of injury, cause group, and age and tenure of
injured workers with the accuracy rate of 92–98%. The results emphasize the significance of quantitative
analysis of empirical injury data in safety science, and contribute to enhanced understanding of injury patterns
using predictive modeling along with safety experts’ perspectives with regulatory or managerial viewpoints. The
predictive models obtained from this study can be used to augment the experience of safety professionals in
agribusiness industries to improve safety intervention efforts. Keywords: Injury severity classification | Injury severity prediction | Machine learning |
مقاله انگلیسی |
8 |
A game-predicting expert system using big data and machine learning
یک سیستم خبره پیش بینی بازی با استفاده از داده های بزرگ و یادگیری ماشین-2019 The National Hockey League (NHL) is a major North American sports organization that earns $3.3 bil- lion in annual revenue, and its stakeholders—team management, advertisers, sports analysts, fans, among others—have vested interest in league competitiveness and team performance. Utilizing player and team data collected from various web sources, we propose an expert system to better predict NHL game out- comes as well as improve recruiting and salary decisions. The system combines principal components analysis, nonparametric statistical analysis, a support vector machine (SVM), and an ensemble machine learning algorithm to predict whether a hockey team will win a game. The ensemble methods improve upon the reference SVM classifier, and the ensemble models’ predictive accuracy for the testing set ex- ceeds 90%. The comparison of several ensemble machine learning approaches specifies opportunities to improve the accuracy of game outcome prediction. The system makes it simple for users to employ the learning methodologies and input data sources, evaluate model results, and address the challenges and concerns inherent in predicting hockey game wins. Keywords: Expert system | Decision-making | Big data | Machine learning | Ice hockey |
مقاله انگلیسی |
9 |
A game-predicting expert system using big data and machine learning
یک سیستم خبره پیش بینی بازی با استفاده از داده های بزرگ و یادگیری ماشین-2019 The National Hockey League (NHL) is a major North American sports organization that earns $3.3 bil- lion in annual revenue, and its stakeholders—team management, advertisers, sports analysts, fans, among others—have vested interest in league competitiveness and team performance. Utilizing player and team data collected from various web sources, we propose an expert system to better predict NHL game out- comes as well as improve recruiting and salary decisions. The system combines principal components analysis, nonparametric statistical analysis, a support vector machine (SVM), and an ensemble machine learning algorithm to predict whether a hockey team will win a game. The ensemble methods improve upon the reference SVM classifier, and the ensemble models’ predictive accuracy for the testing set ex- ceeds 90%. The comparison of several ensemble machine learning approaches specifies opportunities to improve the accuracy of game outcome prediction. The system makes it simple for users to employ the learning methodologies and input data sources, evaluate model results, and address the challenges and concerns inherent in predicting hockey game wins. Keywords: Expert system | Decision-making | Big data | Machine learning | Ice hockey |
مقاله انگلیسی |
10 |
Automating Ischemic Stroke Subtype Classification Using Machine Learning and Natural Language Processing
خودکار سازی طبقه بندی زیرگروه ایسکمیک سکته مغزی با استفاده از یادگیری ماشین و پردازش زبان طبیعی-2019 The manual adjudication of disease classification is time-consuming,
error-prone, and limits scaling to large datasets. In ischemic stroke (IS), subtype
classification is critical for management and outcome prediction. This study sought
to use natural language processing of electronic health records (EHR) combined
with machine learning methods to automate IS subtyping. Methods: Among IS
patients from an observational registry with TOAST subtyping adjudicated by
board-certified vascular neurologists, we analyzed unstructured text-based EHR
data including neurology progress notes and neuroradiology reports using natural
language processing. We performed several feature selection methods to reduce the
high dimensionality of the features and 5-fold cross validation to test generalizability
of our methods and minimize overfitting. We used several machine learning
methods and calculated the kappa values for agreement between each machine
learning approach to manual adjudication. We then performed a blinded testing of
the best algorithm against a held-out subset of 50 cases. Results: Compared to manual
classification, the best machine-based classification achieved a kappa of .25
using radiology reports alone, .57 using progress notes alone, and .57 using combined
data. Kappa values varied by subtype being highest for cardioembolic (.64)
and lowest for cryptogenic cases (.47). In the held-out test subset, machine-based
classification agreed with rater classification in 40 of 50 cases (kappa .72). Conclusions:
Automated machine learning approaches using textual data from the EHR
shows agreement with manual TOAST classification. The automated pipeline, if
externally validated, could enable large-scale stroke epidemiology research. Key Words: Ischemic stroke| cryptogenic | cardioembolism |natural language processing | machine learning |
مقاله انگلیسی |