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,…
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48 results for “Mental illnesses”
Key features of mental illnesses are reflected in speech. Our research focuses on designing a multimodal deep learning structure that automatically extracts salient features from recorded speech samples for predicting various mental disorders including depression, bipolar, and schizophrenia. We adopt a variety of pre-t…
Autoencoders identify brain networks linked to stress and genotype.
problem Designing effective brain stimulation protocols for mental illnesses.
method Supervised autoencoders to model multi-region brain activity.
result Autoencoders reveal a stress-related brain network associated with a bipolar disorder genotype.
The study predicts how discussions in mental disorder Reddit communities affect users' emotional states.
problem Improving mental health conditions through social support analysis.
method Text embedding techniques and RNNs for predicting emotional tone shifts.
result Users' emotional states can improve due to social support, as evidenced by positive comments following negative posts.
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.
Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network t…
Bayesian Inference of Psychometric Variables From Brain and Behavior in Implicit Association Testscs.LG
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.
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.
This research improves interpretability in sequential explanations using mental models.
problem Improving interpretability in sequential explanations between two parties.
method A reinforcement learning framework that selects explanations based on the explainee's mental model.
result Mental model-based policies increase interpretability over random selection in multiple sequential explanations.
Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatmentscs.LG
Framework integrates mental disorder measurements for personalized treatment.
problem Optimizing treatment for mental disorders with latent mental states and heterogeneity.
method Measurement theory and multi-layer neural network for complex treatment effects.
result Learned treatment policies outperform alternatives on heterogeneous treatment effects.
Semiparametric STAR model improves mental health data analysis.
problem Overdispersed, zero-inflated, bounded count data in self-reported mental health surveys.
method STAR transformation and rounding of latent Gaussian model, nonparametric transformation estimation, EM algorithm for maximum likelihood.
result Substantial improvements in goodness-of-fit compared to existing models.
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.
New dataset tests mental rotation from single images, improving model understanding of 3D scenes.
problem Understanding how a scene looks from a different viewpoint using a single image.
method Created CLEVR-MRT dataset, explored neural architectures for volumetric scene representations.
result Demonstrated the effectiveness of volumetric representations in answering mental rotation questions.
A framework for goal-based investing with penalties for fund transfers.
problem Investors' mental accounting and multiple investment goals.
method Continuous-time portfolio selection with mental costs and penalties.
result The value function is the unique solution to a complex system of equations.
Study identifies mental stress in firefighters using heart rate variability data.
problem Unsupervised identification of mental stress in firefighters from heart rate variability data.
method Exploration and comparison of three unsupervised methods: K-Means, convolutional autoencoders, and LSTM autoencoders.
result Convolutional and LSTM autoencoders successfully stratify stressed versus normal samples using HRV markers.
Automated EEG analysis gauges mental workload in task evaluation.
problem Evaluating mental workload in user tasks, especially complex ones.
method Experimental setup, EEG data analysis, feature extraction, machine learning.
result Machine learning outperforms traditional methods in mental workload estimation.
This work adapts RDT for mental program construction, showing benefits and costs.
problem Applying RDT to mental programs with trade-offs between description length, error, and computational costs.
method Proposed a three-way trade-off and used simulations and partial information decomposition.
result Constructing a shared program library provides global benefits but is sensitive to curricula.
The Importance of Constraint Smoothness for Parameter Estimation in Computational Cognitive Modelingq-bio.QM
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…
Assessment of mental workload in real-world conditions is key to ensure the performance of workers executing tasks that demand sustained attention. Previous literature has employed electroencephalography (EEG) to this end despite having observed that EEG correlates of mental workload vary across subjects and physical s…
Bipolar disorder (BPD) is a chronic mental illness characterized by extreme mood and energy changes from mania to depression. These changes drive behaviors that often lead to devastating personal or social consequences. BPD is managed clinically with regular interactions with care providers, who assess mood, energy lev…
Towards automated symptoms assessment in mental healthphysics.med-ph
Activity and motion analysis has the potential to be used as a diagnostic tool for mental disorders. However, to-date, little work has been performed in turning stratification measures of activity into useful symptom markers. The research presented in this thesis has focused on the identification of objective activity …
Precision medicine has received attention both in and outside the clinic. We focus on the latter, by exploiting the relationship between individuals' social interactions and their mental health to develop a predictive model of one's likelihood to be depressed or anxious from rich dynamic social network data. To our kno…
Depression and anxiety are critical public health issues affecting millions of people around the world. To identify individuals who are vulnerable to depression and anxiety, predictive models have been built that typically utilize data from one source. Unlike these traditional models, in this study, we leverage a rich …
In cognitive psychology, automatic and self-reinforcing irrational thought patterns are known as cognitive distortions. Left unchecked, patients exhibiting these types of thoughts can become stuck in negative feedback loops of unhealthy thinking, leading to inaccurate perceptions of reality commonly associated with anx…
Planar neural networks learn image transformations from sequences.
problem Learning image transformations for mental simulation.
method Using planar neural networks, the study investigates various factors affecting the learning of image transformations.
result The approach can effectively learn and transfer image transformations, including translation, rotation, and scaling.
Cognitive brain imaging is accumulating datasets about the neural substrate of many different mental processes. Yet, most studies are based on few subjects and have low statistical power. Analyzing data across studies could bring more statistical power; yet the current brain-imaging analytic framework cannot be used at…
Artificial Intelligence for Diabetes Case Management: The Intersection of Physical and Mental Healthq-bio.QM
Diabetes is a major public health problem in the United States, affecting roughly 30 million people. Diabetes complications, along with the mental health comorbidities that often co-occur with them, are major drivers of high healthcare costs, poor outcomes, and reduced treatment adherence in diabetes. Here, we evaluate…
Reducing the number of false discoveries is presently one of the most pressing issues in the life sciences. It is of especially great importance for many applications in neuroimaging and genomics, where datasets are typically high-dimensional, which means that the number of explanatory variables exceeds the sample size…
There is an increasing interest in exploiting mobile sensing technologies and machine learning techniques for mental health monitoring and intervention. Researchers have effectively used contextual information, such as mobility, communication and mobile phone usage patterns for quantifying individuals' mood and wellbei…
The paper addresses ill-conditioning in large spatial data, proposing solutions for prediction and likelihood estimation.
problem Ill-conditioning of the kernel matrix in large spatial data sets.
method Introduction of various optimality criteria and solutions for managing large spatial data.
result Solutions for managing large spatial data, addressing ill-conditioning and improving prediction and likelihood estimation.
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 …
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 -complexity of continuous functions which is extended here (see Appendix) to the case of vector functions. This extension permits us to handle mult…
AI system predicts acute critical illness from EHRs with explainability.
problem Lack of clinical interpretability in AI predictions for acute critical illness.
method Developed an explainable AI early warning score (xAI-EWS) system.
result System provides clinicians with insights into EHR data explaining predictions.
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.
Autonomous robots need to interact with unknown, unstructured and changing environments, constantly facing novel challenges. Therefore, continuous online adaptation for lifelong-learning and the need of sample-efficient mechanisms to adapt to changes in the environment, the constraints, the tasks, or the robot itself a…
Study integrates causal inference and temporal complexity measures to analyze mental health symptoms.
problem Examining how individual symptom trajectories reveal diagnostic patterns in mental disorders.
method Causal inference, graph analysis, temporal complexity measures, machine learning.
result 91% accuracy in diagnosing symptom dynamics, highlighting disorder-specific causal mechanisms.
New models automate support group formation in online health communities.
problem Challenges in traditional support group formation methods for scalability, static categorization, and insufficient personalization.
method Two novel machine learning models: gDMR and gSTM, integrating user content, demographics, and network data.
result Models outperform baselines in predictive accuracy, semantic coherence, and internal group consistency.
MCGDiff uses SGM to guide SMC for solving ill-posed linear inverse problems.
problem Solving ill-posed linear inverse problems in Bayesian settings.
method Exploiting SGM structure, defining a sequence of intermediate problems, and using SMC methods.
result MCGDiff outperforms competing methods in Bayesian ill-posed inverse problems.
Scalable method completes ill-conditioned matrices from few samples.
problem Matrix completion from few samples for ill-conditioned matrices.
method Iterative algorithm combining IRLS, smoothing Newton, and proximal gradient methods.
result Local quadratic convergence rate and well-conditioned linear systems.
New method captures multimodal disconnectivity in schizophrenia.
problem Misinterpretation of single modality data in schizophrenia research.
method Gaussian graphical model and modularity-based approach on multimodal data.
result Identifies missing links in schizophrenia's default mode network.
In this paper, we present a new task that investigates how people interact with and make judgments about towers of blocks. In Experiment~1, participants in the lab solved a series of problems in which they had to re-configure three blocks from an initial to a final configuration. We recorded whether they used one hand …
This paper presents a study in task-oriented approach to stroke rehabilitation by controlling a haptic device via near-infrared spectroscopy-based brain-computer interface (BCI). The task is to command the haptic device to move in opposing directions of leftward and rightward movement. Our study consists of data acquis…
We show certain symmetry of the dimensions of cohomologies of the funda- mental groups of compact Sasakian manifolds by using the Hodge theory of twisted basic cohomology. As applications, we show that the polycyclic fundamental groups of compact Sasakian manifolds are virtually nilpotent and Sasakian solvmanifolds are…
Paper optimizes estimation of quadratic functionals in nonparametric IV models.
problem Optimal estimation of a nonlinear functional in ill-posed inverse regression.
method Adaptive, minimax estimation using leave-one-out, sieve NPIV estimator with data-driven sieve dimension selection.
result Adaptive estimator achieves minimax optimal rate in various ill-posed cases.
Improves numerical solution of ill-conditioned linear systems for machine learning.
problem Wastefulness and instability in solving ill-conditioned linear systems.
method autonugget combines Richardson extrapolation to determine the solution of the ill-conditioned system, improving accuracy over a single nugget.
result Improves accuracy of numerical solution of ill-conditioned linear systems.
VICE embeds concepts in a vector space using human data.
problem Developing numerical models for mental representations of object concepts.
method Variational Interpretable Concept Embeddings (VICE) using variational inference and triplet odd-one-out task data.
result VICE outperforms SPoSE at predicting human behavior and provides more reproducible object representations.
Paper explores challenges in training PINNs and loss landscape effects.
problem Challenges in training Physics-Informed Neural Networks (PINNs) due to loss landscape issues.
method Examined gradient-based optimizers Adam, L-BFGS, and their combination Adam+L-BFGS, and introduced NysNewton-CG (NNCG).
result Adam+L-BFGS outperforms other optimizers, and NysNewton-CG significantly improves PINN performance.
Study rates of convergence for approximate solutions to linear ill-posed problems in Hilbert scales.
problem Linear ill-posed inverse problems with noisy data.
method Approximate reconstructions from random noisy data using regularization schemes in Hilbert scale.
result Explicitly established error bounds for smooth regression functions.