Locally Linear Embedding improves psychiatric diagnosis accuracy from fMRI data.
problem Improving psychiatric diagnosis accuracy from fMRI data.
method Locally Linear Embedding of BOLD time-series data to optimise feature selection using LOOCV.
result Embedded fMRI gave highly diagnostic performances (> 80%) on eleven publicly-available datasets.
A framework uses preprocessing to improve psychiatric questionnaire predictions while maintaining interpretability.
problem Weak predictive accuracy and limited interpretability of psychiatric questionnaires.
method Two-stage method: stable preprocessing followed by a linear mapping.
result REFINE outperforms other interpretable approaches in psychiatric and non-psychiatric prediction tasks.
This thesis evaluates text-based vs audio-based classification of mental health interviews.
problem Classifying psychiatric illness using text-based methods.
method Design and evaluate a text classification network on mental health interviews, using belabBERT.
result Text-based classification is a strong alternative to audio-based methods.
DEBIAS learns causal effects from psychiatric longitudinal data by optimizing outcome weights.
problem Causal inference challenges in psychiatric longitudinal data due to symptom heterogeneity and latent confounding.
method DEBIAS algorithm that optimizes outcome weights to maximize durable treatment effects and minimize confounding.
result DEBIAS consistently outperforms state-of-the-art methods in recovering causal effects for clinically interpretable composite outcomes.
Accurate diagnosis of psychiatric disorders plays a critical role in improving the quality of life for patients and potentially supports the development of new treatments. Many studies have been conducted on machine learning techniques that seek brain imaging data for specific biomarkers of disorders. These studies hav…
The analysis of comorbidity is an open and complex research field in the branch of psychiatry, where clinical experience and several studies suggest that the relation among the psychiatric disorders may have etiological and treatment implications. In this paper, we are interested in applying latent feature modeling to …
UCSL combines clustering with supervised learning to discover interpretable subtypes.
problem Discovering interpretable subtypes in datasets relevant to supervised tasks.
method UCSL (Unsupervised Clustering driven by Supervised Learning) framework integrating clustering and supervised learning.
result UCSL achieves +1.9 points in balanced accuracy for psychiatric diseases clustering.
CI-GNN uses GNNs to diagnose psychiatric disorders by identifying causally relevant brain regions.
problem Leveraging GNNs for psychiatric diagnosis requires interpretable models to understand decision-making.
method CI-GNN integrates Granger causality into GNNs to identify causally relevant subgraphs.
result CI-GNN provides more reliable and concise explanations of psychiatric diagnoses.
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.
ED-Filter improves eating disorder classification on Twitter.
problem High dimensionality and extensive feature sets in Twitter data for ED classification.
method Informed branch and bound search technique with hybrid greedy-based deep learning.
result Significant improvements in classification accuracy and efficiency.
Simple 1-D CNNs classify Autism from rsfMRI.
problem Classifying Autism from high-dimensional rsfMRI data.
method Sub-sampled 1-D convolutional network.
result 1-D CNNs perform as well as state-of-the-art methods.
NEURO-DRAM improves neuroimaging classification accuracy.
problem Improper use of traditional computer vision models in neuroimaging.
method 3D recurrent visual attention model trained with reinforcement learning.
result NEURO-DRAM achieves state-of-the-art accuracy in Alzheimer's disease prediction.
The functional significance of resting state networks and their abnormal manifestations in psychiatric disorders are firmly established, as is the importance of the cortical rhythms in mediating these networks. Resting state networks are known to undergo substantial reorganization from childhood to adulthood, but wheth…
A new method improves target selection for manipulating complex systems like the brain.
problem Improper incorporation of low-variance outcomes into latent space of predictive models.
method Developed a novel objective based on supervised variational autoencoders (SVAEs) for PPCA (Probabilistic Principal Component Analysis).
result gPCR (Generative Principal Component Regression) dramatically improves target selection in manipulation compared to standard PCR and SVAEs.
Dynamic topic model improves mental health note analysis for children.
problem Lack of longitudinal topic models for psychiatric clinical notes.
method Developed a dynamic topic model with consistent topics and individualized temporal dependencies.
result Achieved a 38% increase in topic coherence.
A novel method automates quality control of fMRI scans, improving accuracy and generalizability.
problem Lack of automated QC for fMRI scans limits clinical neuroscience research.
method Train machine learning classifiers using runtime log features to predict scan quality.
result Classifiers trained on FLAG-QC features outperform previous methods (AUC=0.79 vs AUC=0.56).
Deep learning predicts stress levels from mouse hippocampus activity.
problem Stress level in mice under different environments.
method Deep learning combined with neuron decoding.
result Deep learning model accurately predicts stress levels.
A new reinforcement learning framework for multi-reward processing.
problem Understanding and modeling multi-reward interactions in complex systems.
method Two-stream reward processing with biological associations.
result Agents can react differently to different types of rewards.
A variety of methods have been proposed for interpreting nodes in deep neural networks, which typically involve scoring nodes at lower layers with respect to their effects on the output of higher-layer nodes (where lower and higher layers are closer to the input and output layers, respectively). However, we may be inte…
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,…
This paper addresses the problem of change-point detection on sequences of high-dimensional and heterogeneous observations, which also possess a periodic temporal structure. Due to the dimensionality problem, when the time between change-points is on the order of the dimension of the model parameters, drifts in the und…
Mental disorders such as Autism Spectrum Disorders (ASD) are heterogeneous disorders that are notoriously difficult to diagnose, especially in children. The current psychiatric diagnostic process is based purely on the behavioural observation of symptomology (DSM-5/ICD-10) and may be prone to over-prescribing of drugs …
Study uses interviews to automatically detect BD and BPD with good accuracy.
problem Challenges in distinguishing BD and BPD from clinical interviews.
method Developed a multi-modal dataset and used a linear classifier with selected features from interviews.
result Different sets of features characterize BD and BPD, providing insights into their differences.
BPt is a Python library for ML with neuroimaging data.
problem Analyzing large neuroimaging datasets using machine learning.
method Unified framework of ML tools for tabulated and neuroimaging data.
result Unified ML tools for neuroimaging and tabulated data.
Psychiatric neuroscience is increasingly aware of the need to define psychopathology in terms of abnormal neural computation. The central tool in this endeavour is the fitting of computational models to behavioural data. The most prominent example of this procedure is fitting reinforcement learning (RL) models to decis…
Reciprocating interactions represent a central feature of all human exchanges. They have been the target of various recent experiments, with healthy participants and psychiatric populations engaging as dyads in multi-round exchanges such as a repeated trust task. Behaviour in such exchanges involves complexities relate…
Method for understanding heterogeneous treatment effects in complex causal graphs.
problem Heterogeneity and comorbidity in healthcare problems.
method Developed a new approach to characterize heterogeneous causal effects (HCEs) in graphical contexts, including heterogeneous causal graphs (HCGs) with confounders and mediators.
result Established theoretical forms and properties of HCEs in linear and nonlinear models, and developed interactive structural learning for estimation.
Normative modeling has recently been introduced as a promising approach for modeling variation of neuroimaging measures across individuals in order to derive biomarkers of psychiatric disorders. Current implementations rely on Gaussian process regression, which provides coherent estimates of uncertainty needed for the …
Development of interpretable machine learning models for clinical healthcare applications has the potential of changing the way we understand, treat, and ultimately cure, diseases and disorders in many areas of medicine. These models can serve not only as sources of predictions and estimates, but also as discovery tool…
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.
Unified framework for human-like decision making in various sequential tasks.
problem Real-life decision-making involves diverse strategies leading to similar outcomes.
method Two-stream reward processing mechanism for flexible and unified models.
result Framework unified MAB, CB, and RL with comparable performance.
Paper proposes a new method for population-wise matching of sulcal graphs.
problem Challenges in matching cortical fold variations across individuals.
method Population-wise multi-graph matching of sulcal graphs.
result Effectiveness of multi-graph matching in obtaining consistent labeling of sulcal basins.
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.
Technology and collaboration enable dramatic increases in the size of psychological and psychiatric data collections, but finding structure in these large data sets with many collected variables is challenging. Decision tree ensembles like random forests (Strobl, Malley, and Tutz, 2009) are a useful tool for finding st…
Study finds non-adherence to schizophrenia meds leads to earlier adverse events.
problem Impact of medication non-adherence on adverse outcomes in schizophrenia patients.
method Survival analysis, causal inference methods (T-learner, S-learner, nearest neighbor matching), different amounts of longitudinal information.
result Non-adherence to schizophrenia meds advances adverse events by 1 to 4 months.
Proposes a method to cluster fMRI data and estimate brain connectivity networks.
problem Clustering fMRI data to identify patient groups based on brain connectivity.
method Random covariance clustering model (RCCM) to cluster subjects and estimate individual and shared FC networks.
result RCCM outperforms other methods in clustering and FC network estimation, demonstrated through simulations and real data.
Substantial evidence indicates that major psychiatric disorders are associated with distributed neural dysconnectivity, leading to strong interest in using neuroimaging methods to accurately predict disorder status. In this work, we are specifically interested in a multivariate approach that uses features derived from …
A new method clusters subjects based on brain networks without vectorizing fMRI data.
problem Distortion of clustering results when simplifying fMRI data structure.
method Wishart mixture models for multiple-view clustering of brain networks.
result Identifies multiple underlying pairs of associations between subject clusters and brain sub-networks.
Study predicts gender from brain FC at multiple scales using deep learning and Bayesian methods.
problem Predicting gender from brain functional connectivity.
method Deep learning and Bayesian deep learning applied to brain FC data from 1003 healthy adults.
result Bayesian deep learning provides accurate predictions and uncertainty information.
Dual-stage sEMG classification improves gesture recognition accuracy.
problem Improving accuracy in hand gesture recognition from sEMG signals.
method Dual-stage classification approach: first stage groups similar activities, second stage classifies within groups.
result Dual-stage classification yields significantly higher accuracy than single-stage approach.
A novel method for classification with rejection using ensemble of cost-sensitive classifiers.
problem Avoid risky misclassification in error-critical applications.
method Learning an ensemble of cost-sensitive classifiers.
result Improved classification accuracy and flexibility in loss selection.
The number of possible methods of generalizing binary classification to multi-class classification increases exponentially with the number of class labels. Often, the best method of doing so will be highly problem dependent. Here we present classification software in which the partitioning of multi-class classification…
Sequence classification is an important data mining task in many real world applications. Over the past few decades, many sequence classification methods have been proposed from different aspects. In particular, the pattern-based method is one of the most important and widely studied sequence classification methods in …
Few-shot image classification is improved by correcting CNNs' texture bias.
problem Few-shot image classification performance is hindered by CNNs' texture bias.
method Corrected CNNs' texture bias using a simpler method than state-of-the-art approaches.
result State-of-the-art performance on miniImageNet task achieved.
New NHCAs improve multi-category classification efficiency.
problem Efficient multi-category classification for real-world problems.
method Twin SVM (TWSVM), Generalized eigenvalue proximal SVM (GEPSVM), Regularized GEPSVM (RegGEPSVM), and Improved GEPSVM (IGEPSVM) with OAA, BT, and TDS approaches.
result TDS-TWSVM outperforms other methods in classification accuracy.
Paper compares XGB and BPNN for music style classification.
problem Efficient music style classification using different methods.
method Feature extraction for timbral texture, rhythmic content, and pitch content; comparative evaluation of XGB and BPNN.
result XGB outperforms BPNN for small datasets in music classification.
Classification outperforms regression in portfolio construction, yielding higher Sharpe ratios.
problem Determining which machine learning approach (classification vs. regression) is more effective for portfolio construction.
method Used stacking ensemble of gradient boosted tree, random forest, and neural network models.
result Classification yields higher Sharpe ratios and economically significant alphas compared to regression.
C-HMCNN(h) improves HMC classification by leveraging class hierarchy.
problem Hierarchical multi-label classification with class hierarchy constraints.
method Exploits class hierarchy to produce coherent predictions for multi-label classification.
result C-HMCNN(h) outperforms state-of-the-art models in HMC classification.