Elastic Net Research Articles

Healthy Lifestyle, Metabolic Signature, and Risk of Cardiovascular Diseases: A Population-Based Study.

Although healthy lifestyle has been linked with a reduced risk of cardiovascular diseases (CVDs), the potential metabolic mechanism underlying this association remains unknown. We included 161,018 CVD-free participants from the UK Biobank. Elastic net regression was utilized to generate a healthy lifestyle-related metabolic signature. The Cox proportional hazards model was applied to investigate associations of lifestyle-related metabolic signature with incident CVDs, and mediation analysis was conducted to evaluate the potential mediating role of metabolic profile on the healthy lifestyle-CVD association. Mendelian randomization (MR) analysis was conducted to detect the causality. During 13 years of follow-up, 17,030 participants developed incident CVDs. A healthy lifestyle-related metabolic signature comprising 123 metabolites was established, and it was inversely associated with CVDs. The hazard ratio (HR) was 0.83 (95% confidence interval [CI]: 0.81, 0.84) for CVD, 0.83 (95% CI: 0.81, 0.84) for ischemic heart disease (IHD), 0.86 (95% CI: 0.83, 0.90) for stroke, 0.86 (95% CI: 0.82, 0.89) for myocardial infarction (MI), and 0.75 (95% CI: 0.72, 0.77) for heart failure (HF) per standard deviation increase in the metabolic signature. The metabolic signature accounted for 20% of the association between healthy lifestyle score and CVD. Moreover, MR showed a potential causal association between the metabolic signature and stroke. Our study revealed a potential link between a healthy lifestyle, metabolic signatures, and CVD. This connection suggests that identifying an individual's metabolic status and implementing lifestyle modifications may provide novel insights into the prevention of CVD.

Combined effects of benzene, toluene, xylene, ethylbenzene, and styrene exposure on hearing loss mediated by oxidative stress at realistic low levels

The link between benzene, toluene, ethylbenzene, xylene, and styrene (BTEXS) exposure and hearing loss (HL) is not well-established. This study investigated 1694 petrochemical workers in southern China to examine the effects of BTEXS urinary metabolites on auditory function, considering oxidative stress (OS) as a potential cause. Using generalized linear models, elastic net regression, and quantile g-computation, we evaluated the single and combined effects of BTEXS, OS indicators, and HL. Subgroup analysis explored interactions between BTEXS and cumulative noise exposure (CNE), while mediation analysis assessed OS's role in BTEXS-related HL. Positive associations were found between hippuric acid (HA) and HL (OR = 1.20, P < 0.05) and high-frequency hearing loss (HFHL) (OR = 1.22, P < 0.05). The ENET model linked 3&4-methylhippuric acid (3&4-MHA) with increased HFHL risk. The qgcomp model showed a 23% increased HL risk and a 20% increased HFHL risk per quartile increase in BTEXS exposure. Toluene metabolites (SBMA and HA) were significant contributors to HL, HFHL, and speech-frequency hearing loss (SFHL). Higher BTEXS SBMA, MA and HA levels exacerbated HL risk in workers exposed to CNE. Interaction analysis revealed synergistic effects between tt-MA and other metabolites on HFHL risk. Total SOD (TSOD) significantly mediated the BTEXS-HL relationship. These findings highlight a dose-effect association between BTEXS exposure and HL due to oxidative damage, with toluene metabolites being critical pollutants. BTEXS exposure also synergistically increased HL risk with noise.

Targeted Drug Screening Leveraging Senescence-Induced T-Cell Exhaustion Signatures in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the sixth most prevalent cancer and a leading cause of cancer-related mortality globally, with most patients diagnosed at advanced stages and facing limited early treatment options. This study aimed to identify characteristic genes associated with T-cell exhaustion due to senescence in hepatocellular carcinoma patients, elucidating the interplay between senescence and T-cell exhaustion. We constructed prognostic models based on five signature genes (ENO1, STMN1, PRDX1, RAN, and RANBP1) linked to T-cell exhaustion, utilizing elastic net regression. The findings indicate that increased expression of ENO1 in T cells may contribute to T-cell exhaustion and Treg infiltration in hepatocellular carcinoma. Furthermore, molecular docking was employed to screen small molecule compounds that target the anti-tumor effects of these exhaustion-related genes. This study provides crucial insights into the diagnosis and treatment of hepatocellular carcinoma, establishing a strong foundation for the development of predictive biomarkers and therapeutic targets for affected patients.

Association of neuroimaging measures with facial emotional processing in healthy adults: a task fMRI study.

Investigating the neural processing of emotion-related neural circuits underlying emotional facial processing may help in understanding mental disorders. We used two subscales of the Toronto Alexithymia Scale to assess the emotional cognitive of 25 healthy participants. A higher score indicates greater difficulty in emotional perception. In addition, participants completed a n-back task during functional magnetic resonance imaging. Psychophysiological interaction analysis was used to explore the functional connectivity (FC) of neural circuits. Next, we used elastic-net regression analysis for feature selection and conducted correlation analysis between the neuroimaging measures and questionnaire scores. Following a 3-fold cross-validation, 5 neuroimaging measures emerged as significant features. Results of correlation analysis demonstrated that participants with higher TAS scores exhibited increased FC between the amygdala and occipital face area (OFA) during facial stimulus processing, but decreased connectivity during emotional processing. These findings suggested that individuals with poor emotional recognition exhibited increased connectivity among face-related brain regions during facial processing. However, during emotional processing, decreasing neural synchronization among neural circuits involved in emotional processing affects facial expression processing. These findings suggest potential neural marker related to subjective emotional perception, which may contribute to the diagnosis and treatment of emotional dysregulation in individuals with psychiatric conditions.

Development of a metabolomic risk score for exposure to traffic-related air pollution: A multi-cohort study

To synthesize vast amounts of high-throughput biological information, omics-fields like epigenetics have applied risk scores to develop biomarkers for environmental exposures. Extending the risk score analytic tool to the metabolomic data would be highly beneficial. This research aimed to develop and evaluate metabolomic risk score (metRS) approaches reflecting the biological response to traffic-related air pollution (TRAP) exposure (fine particulate matter, black carbon, and nitrogen dioxide). A simulation study compared three metRS methodologies: elastic net regression, which uses penalized regression to select metabolites, and two variations of thresholding, where a p-value cutoff is used to select metabolites. The methods performance was compared to assess 1) ability to correctly select metabolites associated with daily TRAP and 2) ability of the risk score to predict daily TRAP exposure. Power calculations and false discovery rates (FDR) were calculated for each approach. This metRS was applied to two real cohorts, the Center for Health Discovery and Wellbeing (CHDWB, n = 180) and Environment and Reproductive Health (EARTH, n = 200). In simulations, elastic net regression consistently presented inflated FDR for both high and low effect sizes and across all three sample sizes (n = 200; 500; 1000). Power to detect correct metabolites exceeded 0.8 for all three sample sizes in all three methods. In the real data application assessing associations of metabolomics risk scores and TRAP, associations were largely null. While we did not identify strong associations between the risk scores and TRAP in the real data application, metabolites selected by the risk score approaches were enriched in pathways that are well-known for their association with TRAP. These results demonstrate that certain methodologies to construct metabolomics risk scores are statistically robust and valid; however, standardized metabolic profiling and large sample sizes are required.

Characterizing metabolomic signatures related to coffee and tea consumption and their association with incidence and dynamic progression of type 2 diabetes: A multi-state analysis.

Our study aimed to investigate the impact of tea and coffee consumption and related metabolomic signatures on dynamic transitions from diabetes-free status to incident type 2 diabetes (T2D), and subsequently to T2D-related complications and death. We included 438,970 participants in the UK Biobank who were free of diabetes and diabetes complications at baseline. Of these, 212,146 individuals had information on all metabolic biomarkers. We identified tea- and coffee-related metabolomic signatures using elastic net regression models. We examined associations of tea and coffee intake and related metabolomic signatures with the onset and progression of T2D using multi-state regression models. We observed that tea and coffee consumption and related metabolomic signatures were inversely associated with the risk of five T2D transitions. For example, HRs (95% CIs) per SD increase of the tea-related metabolomic signature were 0.87 (0.85, 0.89), 0.97 (0.95, 0.99), 0.91 (0.90, 0.92), 0.92 (0.91, 0.94), and 0.91 (0.90, 0.92) for transitions from diabetes-free state to incident T2D, from diabetes-free state to total death, from incident T2D to T2D complications, from incident T2D to death, and from T2D complications to death. These findings highlight the benefit of tea and coffee intake in reducing the risk of occurrence and progression of T2D.

An improved algorithm with particle swarm optimization-extreme gradient boosting to predict the contents of pyrolytic hydrocarbons in source rocks

Establishing a method to estimate the hydrocarbon-generation potential of source rocks driven by combinations of well logs is of major significance. The pyrolytic hydrocarbon value (S2) is an important pyrolysis parameter for source-rock evaluation; however, has received limited attention in predictive modeling. This study focused on optimizing ML algorithms to improve the accuracy of S2 predictions. A comparative analysis of ML regression algorithms, including extreme gradient boosting (XGBoost), random forest (RF), support vector regression (SVR), elastic net regularization (Elastic Net), convolutional neural network (CNN), and Bayesian ridge regression (BRR), was conducted to identify the most suitable method for predicting S2. Through confusion matrix analysis, a typical dataset for S2 prediction was established, which combined four well logs (spontaneous potential (SP), deep lateral log (LLD), acoustic transit time (AC), and shallow lateral log (LLS)) as the input variables. Data preprocessing steps, namely, normalization, moving average filtering homogenization, reduced dimension method t-distributed stochastic neighbor embedding (t-SNE), and density-based spatial clustering of application with noise (DBSCAN) reduction, were carried out to minimize errors between the abnormal well logs and the corresponding depth of the measured points. Among the evaluated algorithms, XGBoost exhibited excellent performance in predicting S2 values. Particle swarm optimization (PSO) and genetic algorithm (GA) were selected to optimize the hyperparameters of the XGBoost model. The improved algorithm (t-SNE + DBSCAN + PSO_XGBoost) effectively predicted S2 in the source rocks, serving as a powerful tool for hydrocarbon-generation potential evaluation.

Development of novel computational models based on artificial intelligence technique to predict liquids mixtures separation via vacuum membrane distillation

The fundamental objective of this paper is to use Machine Learning (ML) methods for building models on temperature (T) prediction using input features r and z for a membrane separation process. A hybrid model was developed based on computational fluid dynamics (CFD) to simulate the separation process and integrate the results into machine learning models. The CFD simulations were performed to estimate temperature distribution in a vacuum membrane distillation (VMD) process for separation of liquid mixtures. The evaluated ML models include Support Vector Machine (SVM), Elastic Net Regression (ENR), Extremely Randomized Trees (ERT), and Bayesian Ridge Regression (BRR). Performance was improved using Differential Evolution (DE) for hyper-parameter tuning, and model validation was performed using Monte Carlo Cross-Validation. The results clearly indicated the models’ effectiveness in temperature prediction, with SVM outperforming other models in terms of accuracy. The SVM model had a mean R2 value of 0.9969 and a standard deviation of 0.0001, indicating a strong and consistent fit to the membrane data. Furthermore, it exhibited the lowest mean squared error, mean absolute error, and mean absolute percentage error, signifying superior predictive accuracy and reliability. These outcomes highlight the importance of selecting a suitable model and optimizing hyperparameters to guarantee accurate predictions in ML tasks. It demonstrates that using SVM, optimized with DE improves accuracy and consistency for this specific predictive task in membrane separation context.

Correlation analysis of long-span cable-stayed bridge strain response with environmental and operational variations based on feature selection

The structural response of large-span bridges is significantly influenced by environmental and operational conditions. This study employs a data-driven approach, which utilizes various techniques such as multiple linear regression, best subset regression, stepwise regression, Lasso regression, Ridge regression, and ElasticNet regression to analyze in detail the effects of environmental variables (temperature variables including temperature principal components, lateral temperature gradients, and vertical temperature gradients), and traffic loading on bridge strain response. Based on the analysis of 21 months of health monitoring data from a newly constructed cable-stayed bridge, Lasso regression is shown to perform best in predicting strains on the bridge deck and bottom slab. Furthermore, sensitivity analysis identifies several key influencing factors and quantifies their relative contributions to the strain. Although the 10-min average of the strain due to traffic loading has a minimal impact, the root mean square of the strain shows a significant correlation with the cumulative gross vehicle weight during busy traffic periods. The methodology proposed in this study helps to understand the strain response patterns of large-span bridges from a data perspective, and provides an effective approach to establishing a strain baseline model that eliminates environmental and operational variations.

A novel unsupervised machine learning algorithm for automatic Alfvénic activity detection in the TJ-II stellarator

Abstract A novel sparse encoding algorithm is developed to detect and study plasma instabilities automatically. This algorithm, called Elastic Random Mode Decomposition, is applied to the Mirnov coil signals of a dataset of 1291 discharges of the TJ-II stellarator, enabling the identification of the Alfvénic activity. In the presented approach, each signal is encoded as a collection of basic waveforms called atoms, drawn from a signal dictionary. Then the modes are identified using clustering and correlations with other plasma signals. The performance of the proposed algorithm is dramatically increased by using elastic net regularization and taking advantage of GPU architectures. Therefore the signal size and the number of dictionary elements are no longer limiting factors for encoding complex signals. Once the modes are retrieved from the shots, standard clustering and dimensionality reduction techniques are applied to obtain a 2D map featuring of the physical mode characteristics of this subset of TJ-II shots. The clustering features consider the relationship with the plasma current Ip, the diamagnetic energy W, and inverse square root of electronic density 1/√n, profiling different subtypes of Alfvénic activity. The proposed algorithm can potentially create large databases of labeled modes with unprecedented detail.

Development and external validation of a prediction model for interstitial lung disease in systemic lupus erythematosus patients: A cross-sectional study

ObjectiveThe aim of this study is to develop and validate a nomogram that can assist clinicians in identifying female systemic lupus erythematosus (SLE) patients of reproductive age complicated with interstitial lung disease (ILD). MethodsClinical, laboratory data of SLE patients were first collected. Meteorological data were then gathered according to the geographical locations of the SLE patients. Diagnostic results, univariate logistic regression, elastic net regression, and multivariate logistic regression were used to screen for risk factors for female SLE patients of reproductive age complicated with ILD. A nomogram was constructed using these risk factors and was internally and externally validated through methods such as calculating the concordance index, plotting calibration curves, drawing receiver operating characteristic curves, and clinical decision curves. ResultsA total of 4798 SLE patients were included in this study, with 2488 patients in the development set and 2310 patients in the external validation set. The patients in the development set were randomly divided into a training set (N = 1742) and an internal testing set (N = 746) at a ratio of 7:3. Eight independent risk factors for ILD were identified, including APOB, APOA1, ALP, PLT, HCT, EOS-R, LYM-R, and age. The nomogram model was developed, and the areas under the receiver operating characteristic curve was 0.811 (0.748, 0.875), 0.820 (0.727,0.913), and 0.889 (0.869, 0.909) for the three sets, respectively. ConclusionWe established a nomogram model using easily accessible clinical and laboratory data to predict the probability of female SLE patients of reproductive age developing ILD.

A flexible and efficient algorithm for high dimensional support vector regression

In high dimensional statistical learning, variable selection and handling highly correlated phenomena are two crucial topics. Elastic-net regularization can automatically perform variable selection and tends to either simultaneously select or remove highly correlated variables. Consequently, it has been widely applied in machine learning. In this paper, we incorporate elastic-net regularization into the support vector regression model, introducing the Elastic-net Support Vector Regression (En-SVR) model. Due to the inclusion of elastic-net regularization, the En-SVR model possesses the capability of variable selection, addressing high dimensional and highly correlated statistical learning problems. However, the optimization problem for the En-SVR model is rather complex, and common methods for solving the En-SVR model are challenging. Nevertheless, we observe that the optimization problem for the En-SVR model can be reformulated as a convex optimization problem where the objective function is separable into multiple blocks and connected by an inequality constraint. Therefore, we employ a novel and efficient Alternating Direction Method of Multipliers (ADMM) algorithm to solve the En-SVR model, and provide a complexity analysis as well as convergence analysis for the algorithm. Furthermore, extensive numerical experiments validate the outstanding performance of the En-SVR model in high dimensional statistical learning and the efficiency of this novel ADMM algorithm.

First impressions count: Therapists’ impression on patients’ motivation and helping alliance predicts psychotherapy dropout

Objective With meta-analytically estimated rates of about 25%, dropout in psychotherapies is a major concern for individuals, clinicians, and the healthcare system at large. To be able to counteract dropout in psychotherapy, accurate insights about its predictors are needed. Method We compared logistic regression models with two machine learning algorithms (elastic net regressions and gradient boosting machines) in the prediction of therapy dropout in two large inpatient samples (N = 1,691 and N = 12,473) using baseline and initial process variables reported by patients and therapists. Results Predictive accuracies of the two machine learning algorithms were similar and higher than for logistic regressions: Therapy dropout could be predicted with an AUC of .73 and .83 for Sample 1 and 2, respectively. The initial evaluation of patients’ motivation and the therapeutic alliance rated by the respective therapist were the most important predictors of dropout. Conclusions Therapy dropout in naturalistic inpatient settings can be predicted to a considerable degree by using baseline indicators and therapists’ first impressions. Feature selection via regularization leads to higher predictive performances whereas non-linear or interaction effects are dispensable. The most promising point of intervention to reduce therapy dropouts seems to be patients’ motivation and the therapeutic alliance.

Deep Learning Model for Predicting Neurodevelopmental Outcome in Very Preterm Infants Using Cerebral Ultrasound

ObjectiveTo develop deep learning (DL) models applied to neonatal cranial ultrasound (CUS) and clinical variables to predict neurodevelopmental impairment (NDI) in very preterm babies (VPI) at 3 years of corrected age. Patients and MethodsA retrospective study of a cohort of VPI (220-306 weeks' gestation) born between 2004 and 2016 in Nova Scotia, Canada. Clinical data at hospital discharge, and CUS images at three time points were used to develop DL models using elastic net (EN) and convolutional neural network (CNN). The models' performance was compared using Precision Recall Area Under the Curve (PR-AUC) and area under Receiver Operation Characteristic curve (ROC-AUC) with their 95% Confidence intervals. ResultsOf 665 eligible VPI, 619 (93%) infants with 4184 CUS images were included. The CNN model combining CUS, and clinical variables showed better performance (PR-AUC 0.75, C.I. 072 – 0.79 and ROC-AUC 0.71, CI 0.67 - 0.74) in the prediction of positive NDI outcome compared to the traditional models based solely on clinical predictors (PR AUC 0.60 (0.52-0.68), and ROC-AUC of 0.72 (0.68-0.75). When analyzed by the CUS plane and acquisition timepoint, the model utilizing the anterior coronal plane at 6 weeks of age provided the highest predictive accuracy: PR-AUC 0.81 (0.77-0.91) and ROC-AUC 0.78 (0.66-87). ConclusionWe developed and internally validated a DL prognostic model to predict NDI in VPI at 3 years utilizing CUS and clinical predictors. Early and accurate identification of infants at risk for NDI enables referral to targeted interventions which improves functional outcomes.

The influence of unconventional monetary policy tools: an euro area perspective

Purpose – This article aims to investigate the influence of unconventional monetary policy tools (UMPTs) employed by the European Central Bank (ECB) on the inflation rate and GDP growth rate within the euro area, motivated by the principles of the Taylor rule. Research methodology – Elastic net regression with ARIMA residuals was used to analyse the relationship between UMPTs and economic indicators, measured by adjusted R-squared. Six samples were constructed, and hypothesis testing was performed using moving block bootstrapping. Residual diagnostics were used for model validation. Findings – The study revealed significant impacts of UMPTs, particularly in combination with interest rates, on inflation rates. However, adjusted R-square values for GDP growth rate were less pronounced, indicating a more complex relationship. Research contributes to understanding the dynamics of monetary policy transmission mechanisms, informing policy institutions, and guiding future research directions. Research limitations – Limitations include the focus on the euro area and the absence of analysis in other major economies. Future research should address these limitations and incorporate additional variables for a more comprehensive analysis. Practical implications – The findings provide insight for policymakers regarding the efficacy of UMPTs in influencing inflation rates, aiding in informed decision-making in monetary policy formulation and implementation. Originality/Value – This study contributes novelty by comprehensively analysing the relationship between UMPTs and economic indicators within the euro area, providing valuable insight into monetary policy institutions, and guiding future research directions.

Predicting criminal offence in adolescents who exhibit antisocial behaviour: a machine learning study using data from a large randomised controlled trial of multisystemic therapy.

Accurate prediction of short-term offending in young people exhibiting antisocial behaviour could support targeted interventions. Here we develop a set of machine learning (ML) models that predict offending status with good accuracy; furthermore, we show interpretable ML analyses can complement models to inform clinical decision-making. This study included 679 individuals aged 11-17 years who displayed moderate-to-severe antisocial behaviour, from a controlled trial of Multisystemic-therapy in England. The outcome was any criminal offence in the 18 months after study baseline. Four types of ML algorithms were trained: logistic regression, elastic net regression, random forest, and gradient boosting machine (GBM). Prediction models were developed (1) using predictors readily available to clinicians (e.g. sociodemographics, previous convictions), and (2) with additional information (e.g. parenting). Model agnostic feature importance values were calculated and the most important predictors identified. Nested cross-validation with 100 iterations of random data splits and 10-fold cross-validation within each iteration was employed, and the average predictive performance was reported. Among the ML models using readily available predictors, the GBM is the strongest model (AUC 0.85, 95% CI 0.85-0.86); the other models have average AUCs of 0.82. This performance was better than using only the total number of previous offences as the predictor (0.67, 0.66-0.68), and the model simply assuming past offending status as the prediction (0.81, 0.80-0.81). Additional predictors slightly increased the performance of logistic regression and random forest models but decreased the performance of elastic net regression and gradient boosting machine-based models. The potential utility of ML approaches for accurately predicting criminal offences in high-risk youth is demonstrated. Interpretable ML-based predictive models could be utilised in youth services or research to help develop and deliver effective interventions.

Development and validation of a lung cancer polygenic risk score incorporating susceptibility variants for risk factors.

Incorporating susceptibility genetic variants of risk factors has been reported to enhance the risk prediction of polygenic risk score (PRS). However, it remains unclear whether this approach is effective for lung cancer. Hence, we aimed to construct a meta polygenic risk score (metaPRS) of lung cancer and assess its prediction of lung cancer risk and implication for risk stratification. Here, a total of 2180 genetic variants were used to develop nine PRSs for lung cancer, three PRSs for different histopathologic subtypes, and 17 PRSs for lung cancer-related risk factors, respectively. These PRSs were then integrated into a metaPRS for lung cancer using the elastic-net Cox regression model in the UK Biobank (N = 442,508). Furthermore, the predictive effects of the metaPRS were assessed in the prostate, lung, colorectal, and ovarian (PLCO) cancer screening trial (N = 108,665). The metaPRS was associated with lung cancer risk with a hazard ratio of 1.33 (95% confidence interval: 1.27-1.39) per standard deviation increased. The metaPRS showed the highest C-index (0.580) compared with the previous nine PRSs (C-index: 0.513-0.564) in PLCO. Besides, smokers in the intermediate risk group predicted by the clinical risk model (1.34%-1.51%) with the intermediate-high genetic risk had a 6-year average absolute lung cancer risk that exceeded the clinical risk model threshold (≥1.51%). The addition of metaPRS to the clinical risk model showed continuous net reclassification improvement (continuous NRI = 6.50%) in PLCO. These findings suggest the metaPRS can improve the predictive efficiency of lung cancer compared with the previous PRSs and refine risk stratification for lung cancer.

Modelling and Performance Evaluation of Machine Learning Techniques in Forecasting Cereal Yield for Shri Ganganagar Region, Rajasthan, India

One of agriculture's most difficult issues is predicting crop yield. Crop yield forecasting enables necessary decisions to be made to guarantee food security. The current study looks into the use of statistical and machine learning approaches for wheat and rice yield prediction using long-term weather and yield data of ShriGanganagar, Rajasthan, India. Weather-based models may give accurate crop production estimates, but choosing the right model for agricultural output projections can be difficult. As a result, different models were compared in this study to determine the best model for rice and wheat yield prediction including Multiple linear regression (SMLR), Artificial neural network (ANN), Least absolute shrinkage and selection operator (LASSO), Elastic net (ELNET), Ridge regression, and K-nearest neighbor (K-NN). K-NN outperformed ANN in rice crops and fared better in wheat crops based on the lowest nRMSE value. Ridge regression based on nRMSE was the next best model for Rice yield prediction in the examined region, and KNN was the second best model for Wheat crop.

Machine learning and Bayesian network analyses identifies associations with insomnia in a national sample of 31,285 treatment-seeking college students

BackgroundA better understanding of the relationships between insomnia and anxiety, mood, eating, and alcohol-use disorders is needed given its prevalence among young adults. Supervised machine learning provides the ability to evaluate which mental disorder is most associated with heightened insomnia among U.S. college students. Combined with Bayesian network analysis, probable directional relationships between insomnia and interacting symptoms may be illuminated.MethodsThe current exploratory analyses utilized a national sample of college students across 26 U.S. colleges and universities collected during population-level screening before entering a randomized controlled trial. We used a 4-step statistical approach: (1) at the disorder level, an elastic net regularization model examined the relative importance of the association between insomnia and 7 mental disorders (major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder); (2) This model was evaluated within a hold-out sample. (3) at the symptom level, a completed partially directed acyclic graph (CPDAG) was computed via a Bayesian hill-climbing algorithm to estimate potential directionality among insomnia and its most associated disorder [based on SHAP (SHapley Additive exPlanations) values)]; (4) the CPDAG was then tested for generalizability by assessing (in)equality within a hold-out sample using structural hamming distance (SHD).ResultsOf 31,285 participants, 20,597 were women (65.8%); mean (standard deviation) age was 22.96 (4.52) years. The elastic net model demonstrated clinical significance in predicting insomnia severity in the training sample [R2 = .44 (.01); RMSE = 5.00 (0.08)], with comparable performance in the hold-out sample (R2 = .33; RMSE = 5.47). SHAP values indicated that the presence of any mental disorder was associated with higher insomnia scores, with major depressive disorder as the most important disorder associated with heightened insomnia (mean |SHAP|= 3.18). The training CPDAG and hold-out CPDAG (SHD = 7) suggested depression symptoms presupposed insomnia with depressed mood, fatigue, and self-esteem as key parent nodes.ConclusionThese findings provide insights into the associations between insomnia and mental disorders among college students and warrant further investigation into the potential direction of causality between insomnia and depression.Trial registrationTrial was registered on the National Institute of Health RePORTER website (R01MH115128 || 23/08/2018).

Predicting implementation of response to intervention in math using elastic net logistic regression.

The primary objective of this study was to identify variables that significantly influence the implementation of math Response to Intervention (RTI) at the school level, utilizing the ECLS-K: 2011 dataset. Due to missing values in the original dataset, a Random Forest algorithm was employed for data imputation, generating a total of 10 imputed datasets. Elastic net logistic regression, combined with nested cross-validation, was applied to each imputed dataset, potentially resulting in 10 models with different variables. Variables for the models derived from the imputed datasets were selected using four methods, leading to four candidate models for final selection. These models were assessed based on their performance of prediction accuracy, culminating in the selection of the final model that outperformed the others. Method50 and Methodcoef emerged as the most effective, achieving a balanced accuracy of 0.852. The ultimate model selected relevant variables that effectively predicted RTI. The predictive accuracy of the final model was also demonstrated by the receiver operating characteristic (ROC) plot and the corresponding area under the curve (AUC) value, indicating its ability to accurately forecast math RTI implementation in schools for the following year.