Search Results for author:
Found 24 papers, 9 papers with code
Fairness-Driven LLM-based Causal Discovery with Active Learning and Dynamic Scoring
Causal discovery (CD) plays a pivotal role in numerous scientific fields by clarifying the causal relationships that underlie phenomena observed in diverse disciplines.
Exploration of LLMs, EEG, and behavioral data to measure and support attention and sleep
We explore the application of large language models (LLMs), pre-trained models with massive textual data for detecting and improving these altered states.
ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Text
The utilization of deep learning on electrocardiogram (ECG) analysis has brought the advanced accuracy and efficiency of cardiac healthcare diagnostics.
AdaWaveNet: Adaptive Wavelet Network for Time Series Analysis
Time series data analysis is a critical component in various domains such as finance, healthcare, and meteorology.
Enhancing Fairness and Performance in Machine Learning Models: A Multi-Task Learning Approach with Monte-Carlo Dropout and Pareto Optimality
Bias originates from both data and algorithmic design, often exacerbated by traditional fairness methods that fail to address the subtle impacts of protected attributes.
Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models
Diffusion models have emerged as a robust framework for various generative tasks, including tabular data synthesis.
SleepNet: Attention-Enhanced Robust Sleep Prediction using Dynamic Social Networks
Monitoring and predicting sleep behavior with ubiquitous sensors may therefore assist in both sleep management and tracking of related health conditions.
ECG-SL: Electrocardiogram(ECG) Segment Learning, a deep learning method for ECG signal
In this work, we propose a novel ECG-Segment based Learning (ECG-SL) framework to explicitly model the periodic nature of ECG signals.
Empirical Study of Mix-based Data Augmentation Methods in Physiological Time Series Data
In this study, we systematically review the mix-based augmentations, including mixup, cutmix, and manifold mixup, on six physiological datasets, evaluating their performance across different sensory data and classification tasks.
PiRL: Participant-Invariant Representation Learning for Healthcare Using Maximum Mean Discrepancy and Triplet Loss
Due to individual heterogeneity, person-specific models are usually achieving better performance than generic (one-size-fits-all) models in data-driven health applications.
PiRL: Participant-Invariant Representation Learning for Healthcare
Due to individual heterogeneity, performance gaps are observed between generic (one-size-fits-all) models and person-specific models in data-driven health applications.
LEAVES: Learning Views for Time-Series Data in Contrastive Learning
But the view-learning method is not well developed for time-series data.
Empirical Evaluation of Data Augmentations for Biobehavioral Time Series Data with Deep Learning
As an effective technique to increase the data variability and thus train deep models with better generalization, data augmentation (DA) is a critical step for the success of deep learning models on biobehavioral time series data.
Bias Reducing Multitask Learning on Mental Health Prediction
However, there is still a lack of standard in evaluating bias in such machine learning models in the field, which leads to challenges in providing reliable predictions and in addressing disparities.
Exploiting Social Graph Networks for Emotion Prediction
To this end, we leverage phone data to construct social networks and develop a machine learning architecture that aggregates information from multiple users of the graph network and integrates it with the temporal dynamics of data to predict emotion for all the users.
Psychotic Relapse Prediction in Schizophrenia Patients using A Mobile Sensing-based Supervised Deep Learning Model
The CrossCheck dataset consisting of continuous mobile sensing data obtained from 63 schizophrenia patients, each monitored for up to a year, was used for our evaluations.
Semi-Supervised Learning and Data Augmentation in Wearable-based Momentary Stress Detection in the Wild
We first applied data augmentation techniques on the physiological and behavioral data to improve the robustness of supervised stress detection models.
More to Less (M2L): Enhanced Health Recognition in the Wild with Reduced Modality of Wearable Sensors
For example, although combining bio-signals from multiple sensors (i. e., a chest pad sensor and a wrist wearable sensor) has been proved effective for improved performance, wearing multiple devices might be impractical in the free-living context.
Modality Fusion Network and Personalized Attention in Momentary Stress Detection in the Wild
Compared to the baseline method using the samples with complete modalities, the performance of the MFN improved by 1. 6% in f1-scores.
Patient-independent Schizophrenia Relapse Prediction Using Mobile Sensor based Daily Behavioral Rhythm Changes
In this work, we investigated a machine learning based schizophrenia relapse prediction model using mobile sensing data to characterize behavioral features.
Forecasting Health and Wellbeing for Shift Workers Using Job-role Based Deep Neural Network
According to the differences in self-reported health and wellbeing labels between nurses and doctors, and the correlations among their labels, we proposed a job-role based multitask and multilabel deep learning model, where we modeled physiological and behavioral data for nurses and doctors simultaneously to predict participants' next day's multidimensional self-reported health and wellbeing status.
Routine Clustering of Mobile Sensor Data Facilitates Psychotic Relapse Prediction in Schizophrenia Patients
The clustering model based features, together with other features characterizing the mobile sensing data, resulted in an F2 score of 0. 24 for the relapse prediction task in a leave-one-patient-out evaluation setting.
Sensor-Based Estimation of Dim Light Melatonin Onset (DLMO) Using Features of Two Time Scales
The results using data from 207 undergraduates show that our two-step model with two time-scale features has statistically significantly lower root-mean-square errors than models that use either daily sampled data or frequently sampled data.
Multimodal Autoencoder: A Deep Learning Approach to Filling In Missing Sensor Data and Enabling Better Mood Prediction
To accomplish forecasting of mood in real-world situations, affective computing systems need to collect and learn from multimodal data collected over weeks or months of daily use.
