Study identifies key health behaviors linked to adolescent suicide attempts.
problem Understanding health behaviors associated with increased risk of adolescent suicide attempts.
method Cross-sectional data analysis using machine learning algorithms and logistic regression.
result Non-parametric Bayesian tree ensemble model outperforms other models, achieving 80.0% accuracy in goodness-of-fit and 78.2% in predictive accuracy.
Though suicide is a major public health problem in the US, machine learning methods are not commonly used to predict an individual's risk of attempting/committing suicide. In the present work, starting with an anonymized collection of electronic health records for 522,056 unique, California-resident adolescents, we dev…
Study identifies risk factors for subsequent suicide attempts in youth.
problem Uncertainty in suicide attempt identification from medical claims data.
method Integrative Cox cure model with regularization for survival analysis with uncertain events.
result Identifies risk factors for subsequent suicide attempts and distinguishes susceptibility from timing.
Study uses DNA methylation data to predict suicidal and non-suicidal deaths.
problem Predicting suicidal and non-suicidal deaths from DNA methylation data.
method Support Vector Machines (SVM) with dimensionality reduction using PCA and t-SNE.
result t-SNE outperforms PCA in reducing data dimensionality.
Study finds fMRI can predict suicidal ideation, but analysis questions results.
problem Predicting suicidal ideation using fMRI data.
method Naive Bayes classifier trained on fMRI responses to words related to mortality.
result Classification accuracy of 91% for predicting suicidal ideation, but analysis calls into question the accuracy of the findings.
Improved forecasting of suicide attempts using LSGPs for patients with little data.
problem Challenges in predicting suicide attempts due to their rarity and patient heterogeneity.
method Introduced Latent Similarity Gaussian Processes (LSGPs) to capture patient heterogeneity.
result LSGPs outperform baseline models, even without kernel-design, and offer new insights into patient similarity.
Model predicts cannabis use disorder risk for adolescents and young adults.
problem Predicting cannabis use disorder progression in adolescents and young adults.
method Bayesian machine learning model trained on longitudinal data.
result Model provides personalized risk assessment with AUC of 0.68-0.75 and E/O ratio of 0.95-1.
New method uses surrogate outcomes and single-record data to improve suicide risk modeling.
problem Lack of historical information in single-record patients hinders modeling rare medical events.
method Hybrid framework combining supervised and unsupervised learning to integrate concurrent and single-record data.
result Single-record data and concurrent diagnoses provide valuable information for improving suicide risk modeling.
New analysis shows rational actors will deploy AGI despite negative social value due to catastrophic risk.
problem Rational actors will deploy AGI despite negative social value due to shared catastrophic risk.
method Continuous-time preemption game with shared catastrophic externalities, showing suicide region and welfare distortion.
result The suicide region widens as catastrophic risk grows, and two mechanisms can close it.
Neural SDEs model suicide risk with compact state space constraints.
problem Modeling suicide risk with irregular, noisy, and partially observed data.
method Developed neural SDEs confined to compact state spaces, addressing domain constraints and numerical stability.
result Improved forecasts and optimization dynamics over standard models on EMA datasets.
BiLiNGAM model reveals brain emotion circuit development in adolescents.
problem Understanding brain emotion circuit development during adolescence.
method Bayesian incorporated linear non-Gaussian acyclic model (BiLiNGAM) for multiple DAGs estimation.
result BiLiNGAM reveals unique developmental hub structures and group-specific patterns in emotion-related intra- and inter-modular connectivity.
Accurate prediction of suicide risk in mental health patients remains an open problem. Existing methods including clinician judgments have acceptable sensitivity, but yield many false positives. Exploiting administrative data has a great potential, but the data has high dimensionality and redundancies in the recording …
This article presents a novel method for predicting suicidal ideation from Electronic Health Records (EHR) and Ecological Momentary Assessment (EMA) data using deep sequential models. Both EHR longitudinal data and EMA question forms are defined by asynchronous, variable length, randomly-sampled data sequences. In our …
This paper proposes a novel profile likelihood method for estimating the covariance parameters in exploratory factor analysis of high-dimensional Gaussian datasets with fewer observations than number of variables. An implicitly restarted Lanczos algorithm and a limited-memory quasi-Newton method are implemented to deve…
Trans-GLMC tackles source heterogeneity in transfer learning for structured clusters.
problem Source heterogeneity makes it hard to use multiple related auxiliary sources effectively.
method Trans-GLMC constructs clusters of sources, then combines global fusion, within-cluster refinement, and target debiasing.
result Improves facility-specific prediction and identifies interpretable communities of hospitals with mutual transferability.
This paper aims to analyse adolescents' road accidents in Tuscany. The analysis is based on the Database Edit of Osservatorio di Epidemiologia della Toscana. Complexity and heterogeneity of Edit's data represet an interesting scope to apply Machine Learning methods. In particular, in this paper is proposed an analysis …
In this work, we utilize T1-weighted MR images and StackNet to predict fluid intelligence in adolescents. Our framework includes feature extraction, feature normalization, feature denoising, feature selection, training a StackNet, and predicting fluid intelligence. The extracted feature is the distribution of different…
A methodology for binary classification of EEG records which correspond to different mental states is proposed. This model-free methodology is based on our theory of the ε-complexity of continuous functions which is extended here (see Appendix) to the case of vector functions. This extension permits us to handle mult…
Approach for modeling EHR data with rare features, improving prediction and interpretation.
problem Challenges in modeling rare binary features in EHR data.
method Tree-guided feature selection and logic aggregation for large-scale regression.
result Improved prediction and model interpretation of suicide risk in EHR data.
Equity-Directed Bootstrapping improves model performance across groups in imbalanced datasets.
problem Improving model performance across different groups in imbalanced datasets.
method Equity-Directed Bootstrapping to balance training data with respect to both labels and group identity.
result The equity-directed bootstrap brings test set sensitivities and specificities closer to satisfying the equal odds criterion.
The study of healthy brain development helps to better understand the brain transformation and brain connectivity patterns which happen during childhood to adulthood. This study presents a sparse machine learning solution across whole-brain functional connectivity (FC) measures of three sets of data, derived from resti…
The use of electronic cigarette (e-cigarette) is increasing among adolescents. This is problematic since consuming nicotine at an early age can cause harmful effects in developing teenager's brain and health. Additionally, the use of e-cigarette has a possibility of leading to the use of cigarettes, which is more sever…
Deciding effective and timely preventive measures against complex social problems affecting relatively low income geographies is a difficult challenge. There is a strong need to adopt intelligent automation based solutions with low cost imprints to tackle these problems at larger scales. Starting with the hypothesis th…
Bayesian method infers contextual bandit policies robustly.
problem Inference of contextual bandit policies in small sample sizes.
method Empirical likelihood for Bayesian inference.
result Accurate uncertainty measurements and policy comparison.
Detecting depression early from social media texts.
problem Early diagnosis and prevention of depression.
method Topic analysis and learned confidence scores.
result Achieved good results compared to state of the art.
Bayesian model predicts mental health symptoms from IAT data, improving accuracy over D-score.
problem Limited predictive performance of D-score method for mental health assessment.
method Sparse hierarchical Bayesian model leveraging multi-modal data.
result AUCs of 0.73 (E-IAT) and 0.76 (PSY-IAT) in best modality configurations, significant after FDR correction.
Stochastic block models (SBMs) have been playing an important role in modeling clusters or community structures of network data. But, it is incapable of handling several complex features ubiquitously exhibited in real-world networks, one of which is the power-law degree characteristic. To this end, we propose a new var…
More than two thirds of mental health problems have their onset during childhood or adolescence. Identifying children at risk for mental illness later in life and predicting the type of illness is not easy. We set out to develop a platform to define subtypes of childhood social-emotional development using longitudinal,…
A framework predicts employment status for students considering unconscious biases.
problem Unconscious biases hinder college students' job hunting.
method Developed a framework MAYA using GAN, LSTM, and bias regularization.
result Framework effectively predicts employment status with bias consideration.
New robust method for high-dimensional data analysis in imaging studies.
problem Analyzing high-dimensional data with complex dependence and outliers.
method Robust high-dimensional regression with coefficient thresholding and Huber loss.
result Statistical consistency and computational convergence under high-dimensional settings.
Extends multivariate regression for tensor-variate data, identifying brain regions and facial characteristics.
problem Challenges in fitting regression models with multivariate responses and covariates.
method Low-rank tensor formats on regression coefficients and tensor-variate normal distribution for errors.
result Maximum likelihood estimators for tensor-on-tensor regression via block-relaxation algorithms.
Depression is one of the most common mental health disorders, and a large number of depressed people commit suicide each year. Potential depression sufferers usually do not consult psychological doctors because they feel ashamed or are unaware of any depression, which may result in severe delay of diagnosis and treatme…
Machine learning improves ASD diagnosis accuracy.
problem Early detection and treatment of ASD.
method Machine learning, specifically SMO-SVM and Relief Attributes.
result SMO-SVM classifier outperforms other algorithms in ASD detection.
Integrates neural encoders into GLMMs for multimodal data analysis.
problem Scalable Bayesian inference for GLMMs assumes low-dimensional tabular predictors and does not handle high-dimensional modalities.
method Jointly learns modality-specific neural encoders with GLMM objective, performs variance-corrected stochastic-gradient MCMC.
result Preserves interpretable fixed and random effects while scaling to large longitudinal datasets.
There has been huge interest in studying human brain connectomes inferred from different imaging modalities and exploring their relationship with human traits, such as cognition. Brain connectomes are usually represented as networks, with nodes corresponding to different regions of interest (ROIs) and edges to connecti…
ST-GCN improves rs-fMRI prediction accuracy by modeling spatio-temporal graph connectivity.
problem Existing rs-fMRI methods neglect functional connectivity or temporal dynamics.
method Spatio-temporal graph convolutional network (ST-GCN) trained on BOLD time series.
result ST-GCN predicts gender and age more accurately than common methods.
PerSense assesses personality traits from text for commonsense reasoning.
problem Estimating human personality traits from text for mental health analysis.
method Aggregated Probability Density Functions (PDF) and Machine Learning (ML) models.
result PerSense algorithms achieve comparable results to ground truth data, with high accuracy for personality assessment and commonsense prediction.
Deep-SITAR uses autoencoders to predict growth patterns.
problem Predicting individual growth trajectories from population data.
method Deep learning framework integrating autoencoders and B-spline models.
result Deep-SITAR predicts individual growth without full model re-estimation.
Cost-effective models detect depression from speech.
problem Detecting depression from speech with limited resources.
method Comparison of conventional and deep acoustic features for depression prediction.
result Conventional acoustic features perform as well as deep representations at lower cost.
We introduce a novel approach for predicting the progression of adolescent idiopathic scoliosis from 3D spine models reconstructed from biplanar X-ray images. Recent progress in machine learning have allowed to improve classification and prognosis rates, but lack a probabilistic framework to measure uncertainty in the …
Enhanced framework selects features for unbiased causal inference.
problem Unbiased estimation of causal quantities in causal inference.
method Three-stage computational framework balancing treatment and non-treatment variables.
result Significantly reduces bias and variance in estimating causal quantities.
In applications of supervised learning applied to medical image segmentation, the need for large amounts of labeled data typically goes unquestioned. In particular, in the case of brain anatomy segmentation, hundreds or thousands of weakly-labeled volumes are often used as training data. In this paper, we first observe…
Improved security of smart contracts by classifying them into four categories.
problem Detecting and classifying vulnerabilities in smart contracts efficiently.
method Used AWD-LSTM for multi-class classification, addressing class imbalance.
result Achieved a weighted average Fbeta score of 90.0%.
Bayesian Deep Noise Neural Network (B-DeepNoise) estimates predictive densities and uncertainty.
problem Estimating predictive densities and uncertainty in deep neural networks.
method Extends random noise to all hidden layers, using Gibbs sampling for posterior computation.
result Superior performance in prediction accuracy and uncertainty quantification.
Selective inference improves multi-task neuroimaging analysis.
problem Improving predictive performance and modeling accuracy in neuroimaging studies.
method Proposes a framework for selective inference to jointly identify relevant covariates and conduct valid inference in a sparsity-inducing model.
result Selective inference yields tighter confidence intervals and more accurate signal recovery than single-task methods.
Proposes a novel method to identify complex effects in multi-view datasets.
problem Challenges in analyzing multi-view biomedical datasets with complex interactions.
method Generalized kernel machine approach considering marginal and joint effects of features from different views.
result Effective identification of higher-order composite effects in multi-view datasets.
AUC is unreliable in rare event settings but stable with moderate numbers of events.
problem Misleading performance metrics in rare event settings.
method Simulation study varying dataset sizes and event rates.
result AUC is unreliable in rare event settings but stable with moderate numbers of events.