Binomial Logistic Regression and Artificial Neural Network Methods to Classify Opioid-Dependent Subjects and Control Group Using Quantitative EEG Power Measures

Ergüzel, Türker Tekin; Noyan, Cemal Onur; Eryılmaz, Gül; Ünsalver, Barış Önen; Çebi, Merve; Taş, Cumhur; Dılbaz, Nesrin; Tarhan, Nevzat

doi:10.1177/1550059418824450

Cited by 17 publications

(13 citation statements)

References 17 publications

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“…Gini = (2 × AUC − 1) is the equation used to express the Gini coefficient in terms of AUC value. 49 Besides, since the entropy values are distinctive for each group the average values of the entropies are also given in Table 3 where high entropy value is associated with unpredictability, uncertainty and disorder.…”

Section: Resultsmentioning

confidence: 99%

Entropy: A Promising EEG Biomarker Dichotomizing Subjects With Opioid Use Disorder and Healthy Controls

et al. 2020

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Electroencephalography (EEG) signals are known to be nonstationary and often multicomponential signals containing information about the condition of the brain. Since the EEG signal has complex, nonlinear, nonstationary, and highly random behaviour, numerous linear feature extraction methods related to the short-time windowing technique do not satisfy higher classification accuracy. Since biosignals are highly subjective, the symptoms may appear at random in the time scale and very small variations in EEG signals may depict a definite type of brain abnormality it is valuable and vital to extract and analyze the EEG signal parameters using computers. The challenge is to design and develop signal processing algorithms that extract this subtle information and use it for diagnosis, monitoring, and treatment of subjects suffering from psychiatric disorders. For this purpose, finite impulse response–based filtering process was employed rather than traditional time and frequency domain methods. Finite impulse response subbands were analyzed further to obtain feature vectors of different entropy markers and these features were fed into a classifier namely multilayer perceptron. The performances of the classifiers were finally compared considering overall classification accuracies, area under receiver operating characteristic curve scores. Our results underline the potential benefit of the introduced methodology is promising and is to be treated as a clinical interface in dichotomizing substance use disorders subjects and for other medical data analysis studies. The results also indicate that entropy estimators can distinguish normal and opioid use disorder subjects. EEG data and theta frequency band have distinctive capability for almost all types of entropies while nonextensive Tsallis entropy outperforms compared with other types of entropies.

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Section: Resultsmentioning

confidence: 99%

Entropy: A Promising EEG Biomarker Dichotomizing Subjects With Opioid Use Disorder and Healthy Controls

et al. 2020

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“…Previous research using machine learning for addiction outcomes focused mainly on predictive accuracy, although a few studies attempted to identify and interpret specific variables that were associated with the outcomes (3,4,34,35). Acion et al (3) reported that ensemble super learning was superior to other machine learning methods, including penalized regression, SVM, and neural networks for predicting SUD treatment success indicated by treatment discharge status in a Hispanic cohort.…”

Section: Discussionmentioning

confidence: 99%

Factors associated with opioid cessation: a machine learning approach

Cox

Sherva

Lunetta

et al. 2019

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Background and Aims People with opioid use disorder (OUD) can stop using opioids on their own, with help from groups and with treatment, but there is limited research on the factors that influence opioid cessation. Methods We employed multiple machine learning prediction algorithms (LASSO, random forest, deep neural network, and support vector machine) to assess factors associated with ceasing opioid use in a sample comprised of African Americans (AAs) and European Americans (EAs) who met DSM-5 criteria for mild to severe OUD. Values for several thousand demographic, alcohol and other drug use, general health, and behavioral variables, as well as diagnoses for other psychiatric disorders, were obtained for each participant from a detailed semi-structured interview. Results Support vector machine models performed marginally better on average than those derived using other machine learning methods with maximum prediction accuracies of 75.4% in AAs and 79.4% in EAs. Subsequent stepwise regression analyses that considered the 83 most highly ranked variables across all methods and models identified less recent cocaine use (p<5×10 -8 ), a shorter duration of opioid use (p<5×10 -6 ), and older age (p<5×10 -9 ) as the strongest independent predictors of opioid cessation. Factors related to drug use comprised about half of the significant independent predictors, with other predictors related to non-drug use behaviors, psychiatric disorders, overall health, and demographics. Conclusions These proof-ofconcept findings provide information that can help develop strategies for improving OUD management and the methods we applied provide a framework for personalizing OUD treatment.

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“…For comparison and completeness, we adopt the LRM to classify the patients with censored intermediate event time, since the LRM is a widely used model for the classification issue [ 33 – 35 ]. To be specific, the logistic regression part of the mixture cure model, i.e., π ( x ), is used to calculate the susceptible probability of the patient with censored intermediate event time.…”

Section: Methodsmentioning

confidence: 99%

An improved method for the effect estimation of the intermediate event on the outcome based on the susceptible pre-identification

et al. 2021

BMC Med Res Methodol

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Background In follow-up studies, the occurrence of the intermediate event may influence the risk of the outcome of interest. Existing methods estimate the effect of the intermediate event by including a time-varying covariate in the outcome model. However, the insusceptible fraction to the intermediate event in the study population has not been considered in the literature, leading to effect estimation bias due to the inaccurate dataset. Methods In this paper, we propose a new effect estimation method, in which the susceptible subpopulation is identified firstly so that the estimation could be conducted in the right population. Then, the effect is estimated via the extended Cox regression and landmark methods in the identified susceptible subpopulation. For susceptibility identification, patients with observed intermediate event time are classified as susceptible. Based on the mixture cure model fitted the incidence and time of the intermediate event, the susceptibility of the patient with censored intermediate event time is predicted by the residual intermediate event time imputation. The effect estimation performance of the new method was investigated in various scenarios via Monte-Carlo simulations with the performance of existing methods serving as the comparison. The application of the proposed method to mycosis fungoides data has been reported as an example. Results The simulation results show that the estimation bias of the proposed method is smaller than that of the existing methods, especially in the case of a large insusceptible fraction. The results hold for small sample sizes. Besides, the estimation bias of the new method decreases with the increase of the covariates, especially continuous covariates, in the mixture cure model. The heterogeneity of the effect of covariates on the outcome in the insusceptible and susceptible subpopulation, as well as the landmark time, does not affect the estimation performance of the new method. Conclusions Based on the pre-identification of the susceptible, the proposed new method could improve the effect estimation accuracy of the intermediate event on the outcome when there is an insusceptible fraction to the intermediate event in the study population.

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Binomial Logistic Regression and Artificial Neural Network Methods to Classify Opioid-Dependent Subjects and Control Group Using Quantitative EEG Power Measures

Cited by 17 publications

References 17 publications

Entropy: A Promising EEG Biomarker Dichotomizing Subjects With Opioid Use Disorder and Healthy Controls

Entropy: A Promising EEG Biomarker Dichotomizing Subjects With Opioid Use Disorder and Healthy Controls

Factors associated with opioid cessation: a machine learning approach

An improved method for the effect estimation of the intermediate event on the outcome based on the susceptible pre-identification

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