Model detects depression from transcribed interviews using affective language.
problem Detecting depression from transcribed clinical interviews.
method Hierarchical Attention Network with affective conditioning.
result Model achieves state-of-the-art F1 scores in depression detection.
Study proposes a decision tree for more accurate depression recognition in speech.
problem Subjective bias in traditional depression diagnosis methods.
method A novel speech segment fusion method based on decision tree.
result The proposed decision tree model improves depression classification performance.
Paper proposes CBPT for early depression detection on Twitter.
problem Early detection of depression on social media.
method Cost-sensitive Boosting Pruning Trees (CBPT) for depression detection.
result CBPT achieves strong classification results on multiple datasets.
This paper calculates second order price sensitivities for markets affected by financial crises.
problem Accurate risk management in financial derivative markets, especially during financial crises.
method Derives explicit formulas for second order price sensitivities under a depressed market model.
result Improved hedging strategies during financial crunches are possible with the derived formulas.
Machine learning models predict depression risk based on various factors.
problem Identifying individuals at greatest risk for depression.
method Random Effects/Expectation Maximization (RE-EM) trees and Mixed Effects Random Forest (MERF) algorithms.
result Machine learning models accurately predict depression severity and identify key predictors.
Transfer entropy shows abnormal brain connectivity in depression.
problem Abnormal brain connectivity in depression.
method Transfer entropy analysis on EEG recordings.
result Aberrated dynamics and direction of information between brain centers in depression.
Paper proposes automated depression screening using deep CNNs.
problem Subjective and inconsistent depression diagnosis.
method Convolutional Neural Networks (CNN) and multipart interactive training.
result ResNet architectures achieved 77% accuracy in depression detection.
Study uses machine learning to detect depression in Twitter users.
problem Detecting depression in Twitter users using social media data.
method Machine learning techniques applied to Twitter activity and tweet features.
result More features improve accuracy in detecting depressed users.
Study finds PLI functional connectivity feature superior for depression recognition.
problem Effective detection of depression remains a public health challenge.
method Resting state EEG data collected from MDD and normal controls; various feature types and selection methods evaluated.
result PLI functional connectivity feature superior to linear and nonlinear features; highest classification accuracy 82.31%.
Study uses EEG features HFD and SampEn to detect depression with high accuracy.
problem Diagnosing depression reliably and accurately.
method Applied Higuchi Fractal Dimension and Sample Entropy on EEG signals using seven machine learning algorithms.
result Good classification possible even with small EEG data, achieving high accuracy.
Review of machine learning methods for detecting depression from resting EEG.
problem Detecting depression from resting-state EEG recordings.
method Machine learning combined with nonlinear analysis on EEG data.
result Improved reliability of depression detection models.
Digital risk scores predict depression and anxiety over 10 years.
problem Identifying individuals at risk of depression and anxiety.
method Developed a 10-year predictive algorithm using UKB cohort, selecting predictors via Cox proportional hazards model and DeepSurv.
result Highly discriminating models for depression and anxiety were developed.
Predicting depression on Twitter using social media posts.
problem Identifying depression in online personas.
method Crowdsourced Twitter users, Bag of Words approach, statistical classifiers.
result 81% accuracy rate in depression risk estimation.
Review of machine learning methods for detecting depression from resting EEG.
problem Improving depression diagnosis from EEG data.
method Analysis of machine learning approaches in detecting depression from resting-state EEG.
result Discussion of various machine learning models for depression detection.
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.
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.
Research uses smartphone sensors to predict depressive episodes in patients.
problem Predicting depressive episodes in patients without prior warning.
method Tracked smartphone sensors to identify abnormal patterns in sleep and communication.
result Algorithms can detect unusual moments before depressive episodes occur.
AVEC 2019 challenges AI in detecting depression and cross-cultural emotions.
problem Detecting depression and cross-cultural emotions from audiovisual data.
method Comparison of machine learning methods under standardized conditions.
result Baseline system performance on state-of-mind, depression, and cross-cultural tasks.
The study compares machine learning models for depression detection and highlights the importance of feature selection.
problem The choice of features in machine learning models for depression detection is crucial.
method Comparison of seven machine learning models on depression detection tasks.
result Optimal feature selection is essential for accurate and clinically acceptable classification solutions.
Bluetooth data predicts depression severity, showing 18.8% extra variance.
problem Predicting depressive symptom severity using Bluetooth data.
method Extracted 49 Bluetooth features from NBDC data, used linear mixed-effect and hierarchical Bayesian linear regression models.
result Hierarchical Bayesian model achieved best prediction metrics (R2=0.526, RMSE=3.891).
Detecting early signs of mood episodes in bipolar disorder patients.
problem Early identification of mood episodes in bipolar disorder patients for timely treatment.
method Signature-based model derived from stochastic analysis applied to real-time mood data.
result The signature method can identify the onset of mood episodes in bipolar disorder patients.
After September 2008, the advanced economies severe decline caused demand for emerging economies' exports to drop and the crisis became truly global, much deeper and broader than expected. In these times of global depression, most countries and companies are affected, some more than others. The financial crisis has tur…
Model predicts depression and anxiety from social network dynamics.
problem Predicting mental health from social interactions.
method Developed a predictive model using dynamic social network data.
result Dynamic social network model outperforms static models.
Variable renewables can avoid market value decline with policy changes.
problem Market value decline due to correlated generation from wind and solar.
method Theoretical analysis and simulation examples of market incentives and prices.
result Market value decline is due to policy assumptions, not inherent technology limitations.
Paper proposes an EKF for estimating time-varying market efficiency.
problem Estimating time-varying market efficiency under nonlinear dynamics.
method Extended Kalman Filter (EKF) for time-varying autoregressive models.
result U.S. market generally remained weak-form efficient since mid-1946.
Author2Vec generates user embeddings from social media data.
problem Generating useful user embeddings from noisy social media data.
method End-to-end neural network with BERT sentence representations and unsupervised pre-training.
result Author2Vec outperforms traditional methods in user classification tasks.
Statistical test evaluates if personalizing interventions is cost-effective.
problem Balancing the benefits of personalizing interventions with their potential costs.
method Developed a statistical hypothesis test to assess the performance of personalized interventions.
result The test shows that personalized interventions can outperform standard approaches under certain conditions.
Model shows how biased beliefs can lead to economic polarization.
problem Economic polarization due to biased beliefs in interacting markets.
method Evolutionary model of stock market agents with biased beliefs, real economy described by multiplier-accelerator framework.
result Polarized beliefs can lead to multiple steady states of income and price levels, reflecting optimism or pessimism.
Researchers develop a new SMC sampler for Wishart processes to improve dynamic covariance inference.
problem Challenging inference of dynamic covariance in various scientific fields.
method Introduce Sequential Monte Carlo (SMC) sampler for the Wishart process.
result SMC sampling provides more robust estimates and out-of-sample predictions of dynamic covariance.
Machine learning confound removal biases results, leading to misleading predictions.
problem Common confound removal methods in machine learning lead to misleading predictions.
method Featurewise removal of confound variance by linear regression before applying ML.
result This common deconfounding approach can leak information, amplifying null or moderate effects.
Study integrates diverse data sources to predict mental health conditions.
problem Predict individuals' mental health conditions using a heterogeneous network approach.
method Leverage a heterogeneous information network (HIN) to model social interaction, health data, and survey data. Apply recommender system (RS) and node classification (NC) paradigms to predict mental health states.
result RS and NC methods outperform traditional logistic regression models in predicting mental health conditions.
The paper compares methods for estimating heterogeneous treatment effects using multiple randomized trials.
problem Estimating heterogeneous treatment effects reliably and precisely with a single dataset is challenging.
method Non-parametric approaches for estimating heterogeneous treatment effects using data from multiple trials.
result Methods that directly allow for heterogeneity of the treatment effect across trials perform better than those that do not.
A new algorithm learns optimal personalized treatment plans online with low regret.
problem Learning optimal dynamic treatment regimes in an online setting.
method Developed a novel algorithm balancing exploration and exploitation for rate-optimal regret.
result Guaranteed rate-optimal regret for linear transition and reward models.
The paper assesses fairness in risk score models, focusing on epistemic value.
problem Fairness of risk score models in communicating uncertainty.
method Identified key fairness desiderata, developed metrics for quantitative assessment, and applied methodology in two case studies.
result Introduced a novel calibration error metric for meaningful comparisons between groups of different sizes.
Analyzed banking crises across 66 countries, showing interconnections and clustering patterns.
problem Characterizing banking crises and their interconnections across different countries.
method Used dichotomous banking crises time series data from 1800 to 2014, analyzed via heatmap matrices and clustering.
result Countries exhibit pairwise correlation in banking crises, and crises tend to affect countries with financial links.
Research uses activity analysis to identify mental health symptoms.
problem Identifying mental health symptoms using objective activity metrics.
method Proposes a framework for mHealth monitoring of psychiatric patients based on physical activity time series.
result Identifies distinct behavioural phenotypes and measures for mood assessment.
A new framework estimates causal effects for ordinal variables.
problem Existing causal inference methods fail for ordinal data.
method Presumes a latent Gaussian DAG model with constrained covariance matrix.
result Closed-form function for ordinal causal effects in latent space.
While studying response trajectory, often the population of interest may be diverse enough to exist distinct subgroups within it and the longitudinal change in response may not be uniform in these subgroups. That is, the timeslope and/or influence of covariates in longitudinal profile may vary among these different sub…
Model uses Preisach hysteresis to predict gig worker acceptance, reducing costs and improving fill rates.
problem Predicting and optimizing gig worker acceptance in labor markets.
method Preisach hysteresis model applied to neural network and XGBoost classifier for binary transaction outcomes.
result Model reduces total wage bill by 21.3% and increases expected fill rate by 9.7 pp.
Framework identifies brain connectivity alterations for MDD patients using limited rs-fMRI data.
problem Difficult to analyze brain connectivity alterations from limited rs-fMRI data.
method Proposed a multitask Gaussian Bayesian network (MTGBN) framework to learn individual disease-induced alterations.
result Framework efficiently learns Bayesian network structures from limited data, showing improved performance.
Paper proposes a new method for uncertainty estimation in medical data.
problem Difficulty in assigning confidence to deep learning model predictions in healthcare.
method Combines deep Bayesian learning with deep kernel learning for uncertainty estimation.
result Demonstrates improved uncertainty estimation compared to Gaussian processes and deep Bayesian neural networks.
The American economy can be thought of as a highly connected random network in terms of both its technological and informational connections. The cumulative size of economic recessions, the fall in output from peak to trough, is analysed for the US economy 1900-2002. A least squares fit of an exponential relationship b…
Machine learning detects and classifies cognitive distortions in mental health texts.
problem Detect and classify cognitive distortions in mental health texts to improve treatment.
method Machine learning framework using crowdsourced and therapy dataset.
result Model achieved high F1 scores for detecting and classifying cognitive distortions.
New method estimates composite outcomes for precision medicine.
problem Balancing multiple outcomes in precision medicine.
method Optimal treatment rule for composite outcomes.
result Estimates composite outcomes and optimal treatment rule.
EXFormer predicts foreign exchange returns with high accuracy using a multi-scale self-attention mechanism and dynamic variable selection.
problem Accurately forecasting daily exchange rate returns in international finance.
method EXFormer uses a multi-scale trend-aware self-attention mechanism with dynamic variable selection and embedded squeeze-and-excitation blocks.
result EXFormer outperforms other models in forecasting daily exchange rate returns, achieving statistically significant improvements in directional accuracy.
Modeling decision-making dynamics in MDD patients using RL-HMM.
problem Characterize reward learning strategies in MDD patients.
method Proposed RL-HMM framework to analyze reward-based decision-making.
result MDD patients show reduced engagement in RL compared to healthy controls.
Paper discusses why and how negative interest rates occur.
problem Negative interest rates and their implications.
method Analyzes second-order differential dynamics to explain negative rates.
result Negative rates can influence interest rate variance and expectation.
The personalization of treatment via bio-markers and other risk categories has drawn increasing interest among clinical scientists. Personalized treatment strategies can be learned using data from clinical trials, but such trials are very costly to run. This paper explores the use of active learning techniques to desig…