Modeling individual cardiovascular responses from wearable sensor data.
problem Capturing and understanding cardiovascular responses to physical activity and sleep changes.
method Attentional convolutional neural network to learn signatures from minute-level sensor data.
result Generated signatures generalize and outperform baseline models in predicting cardiovascular variables.
Study reviews machine learning techniques for stress monitoring.
problem Improving accuracy of stress monitoring devices.
method Reviewed machine learning techniques for various stress indicators.
result Choosing the right classifier depends on multiple factors.
CNNs improve ECG noise detection for better heart condition monitoring.
problem Signal noise in mobile ECGs hinders accurate diagnosis.
method Developed and trained a 16-layer CNN on a noise-annotated dataset.
result 16-layer CNN achieves 0.977 AUC on ECG noise detection.
ECG signal learning predicts cardiovascular death risk.
problem Scarce positive ECG event examples and class imbalance.
method Multiple instance learning framework for raw ECG signals.
result Learned risk score outperforms existing metrics.
Neural network predicts cardiovascular events from EHRs with high accuracy.
problem Predicting onset of cardiovascular diseases from electronic health records.
method Multi-task gated recurrent units with attention mechanism.
result Model outperforms clinical risk scores in predicting stroke and myocardial infarction.
Study proposes a multimodal model for cardiovascular risk prediction using EHRs.
problem Lack of comprehensive risk prediction from EHRs due to unstructured text.
method Proposes a multimodal BiLSTM model integrating structured and unstructured EHR data.
result Proposed BiLSTM model outperforms other DNN architectures in cardiovascular risk prediction.
Paper models treatment effects by clustering patients with distinct survival characteristics.
problem Estimating treatment efficacy in clinical settings with censored outcomes.
method Latent variable approach to model heterogeneous treatment effects.
result The latent structure can mediate base survival rates and reveal actionable phenotypes.
SBI improves uncertainty analysis of cardiovascular biomarkers.
problem Mapping waveforms back to plausible physiological parameters.
method Simulation-based inference (SBI) for statistical inference.
result Posterior distributions provide a multi-dimensional representation of uncertainty.
Researchers develop methods to reduce simulation costs for cardiovascular modeling.
problem High computational cost of high-fidelity simulations in cardiovascular modeling.
method Use low-fidelity approximations, neural networks, and normalizing flows to construct surrogates.
result Validated methods reduce computational cost while maintaining accuracy.
Risk-stratify improves risk stratification for cardiovascular disease.
problem Accurately stratify patients for cardiovascular disease prognosis.
method Two-phase algorithm: tree partitioning followed by graph decomposition.
result Significant reduction in false discovery rate (33%) compared to state-of-the-art methods.
FalconBC improves patient-specific cardiovascular modeling by estimating boundary conditions efficiently.
problem Efficiently estimating boundary conditions in patient-specific cardiovascular models, especially in open-loop models and anatomies with lesions.
method A general amortized inference framework based on probabilistic flow that treats clinical targets and anatomies as conditioning variables.
result Demonstrated on two patient-specific models, FalconBC improves efficiency and accuracy in estimating boundary conditions.
Models for predicting the risk of cardiovascular events based on individual patient characteristics are important tools for managing patient care. Most current and commonly used risk prediction models have been built from carefully selected epidemiological cohorts. However, the homogeneity and limited size of such coho…
We train and validate a semi-supervised, multi-task LSTM on 57,675 person-weeks of data from off-the-shelf wearable heart rate sensors, showing high accuracy at detecting multiple medical conditions, including diabetes (0.8451), high cholesterol (0.7441), high blood pressure (0.8086), and sleep apnea (0.8298). We compa…
Study creates a multimodal learning framework for CVD risk prediction.
problem Predicting cardiovascular disease risk in diverse populations.
method Combines cross modal transformers, graph neural networks, and causal representation learning.
result Model predicts personalized CVD risk with causal invariance across subpopulations.
Neural network predicts falls in elderly people up to 10 minutes in advance.
problem Falls prevention in elderly people, especially in aging societies.
method Gated Recurrent Unit (GRU) based neural networks model using heart rate and mean blood pressure signals.
result Predicted syncope occurrence approximately 10 minutes before manual markers.
Generative ODE model learns unknown variables in medical systems.
problem Estimating unknown variables in complex medical systems.
method Variational autoencoder incorporating known ODE functions.
result Modeling known-unknowns improves system parameter discovery and extrapolation.
Framework for imputing missing heart data to simulate brain-heart interactions.
problem Lack of multi-modal patient data representing heart and brain processes.
method Probabilistic framework for joint cardiac data imputation and mechanistic model personalization.
result Accurate imputation of missing cardiac features in incomplete datasets.
Method generates natural ECGs with 25 interpretable features.
problem Lack of labeled ECGs for supervised learning and automatic diagnostics.
method Variational autoencoder for ECG generation and feature extraction.
result Low Maximum Mean Discrepancy (0.00383) indicates good ECG generation quality.
Develops fair ASCVD risk models reducing group disparities.
problem Inconsistent performance of risk stratification models across race and gender groups.
method Adversarial learning and large observational cohort from EHRs.
result Reduced variability in error rates across groups.
Paper proposes Adversarial Oversampling for heart disease detection.
problem Imbalanced classes in heartbeats images classification.
method 2D Convolutional Neural Network with InfoGAN for synthetic oversampling.
result Proposed Adversarial Oversampling improves classifier performance for minority classes.
BoXHED boosts hazard estimation for dynamic health risk scores.
problem Analyzing time-varying health vitals for disease onset prediction.
method Gradient boosting for nonparametric hazard function estimation with time-dependent covariates.
result Novel interaction effects among risk factors identified in cardiovascular disease onset data.
Generative model learns shape drift for quantifying domain uncertainty in hemodynamics.
problem Quantifying domain uncertainty in medical image segmentation for biomarker estimation.
method Conditional stochastic interpolant framework based on LDDMM registration.
result Generative model can create random perturbations of shapes for biomarker estimation.
New framework predicts arterial blood pressure from MRI data using physics-informed neural networks.
problem Clinical applicability of predictive cardiovascular flow models is hindered by computational cost and tedious pre-processing.
method Physics-informed neural networks constrained by conservation of mass and momentum principles.
result Deep neural networks provide physically consistent predictions for arterial blood pressure without conventional simulators.
Radiomics identifies subtle cardiac changes in hypertension.
problem Subtle cardiac alterations in hypertension not captured by conventional imaging.
method Combines feature selection and machine learning for identifying structural and tissue changes.
result Radiomics model detects changes beyond conventional imaging.
Deep learning improves CVD risk prediction from health records.
problem Predicting cardiovascular disease risk from administrative health data.
method Combined survival analysis and deep learning models.
result Deep learning models outperform traditional Cox models in accuracy and explained time-to-event occurrence.
Realizations of stochastic process are often observed temporal data or functional data. There are growing interests in classification of dynamic or functional data. The basic feature of functional data is that the functional data have infinite dimensions and are highly correlated. An essential issue for classifying dyn…
A new method for sampling from posterior distributions in Bayesian inverse problems.
problem Sampling from posterior distributions in Bayesian inverse problems is challenging due to intractable terms.
method Proposes a novel approach that decomposes the transitions, allowing a trade-off between complexity of guidance term and prior transitions.
result Validated through experiments on various inverse problems, including challenging cases with latent diffusion models as priors.
Active stacking improves heart rate estimation accuracy with minimal labeled data.
problem Inconsistent heart rate estimation across subjects due to signal quality and individual differences.
method Active learning and stacking ensemble regression to aggregate base estimators.
result Active stacking significantly outperforms other methods with minimal labeled data.
Generative Adversarial Model improves RV segmentation accuracy in MRI.
problem Accurate segmentation of the right ventricular blood pool from cine MRI sequences.
method Combines FCNN, Gated Recurrent Units (GRU), Generative Adversarial Networks (GAN), and L1 loss function.
result Improves Dice Index and Hausdorff Distance by 0.05 and 3.49 mm respectively.
Study predicts heart failure patient survival using stacked ensemble ML.
problem Predicting survival of heart failure patients.
method Collect and analyze patient data, apply SMOTE, use K-Means, Fuzzy C-Means clustering, Random Forest, XGBoost, Decision Tree, and propose a stacked ensemble model.
result Supervised ML algorithms outperform unsupervised models, achieving high accuracy and F1 score.
Portable, Wearable and Wireless electrocardiogram (ECG) Systems have the potential to be used as point-of-care for cardiovascular disease diagnostic systems. Such wearable and wireless ECG systems require automatic detection of cardiovascular disease. Even in the primary care, automation of ECG diagnostic systems will …
Random shuffle method boosts HF dataset size 10-21 times.
problem Narrow dataset cardinality in HF clinical studies.
method Random shuffle method to enhance dataset size.
result Enhanced HF dataset size 10-21 times.
We study regularized estimation in high-dimensional longitudinal classification problems, using the lasso and fused lasso regularizers. The constructed coefficient estimates are piecewise constant across the time dimension in the longitudinal problem, with adaptively selected change points (break points). We present an…
Study finds incorporating fairness in healthcare models doesn't improve performance or net benefit.
problem Addressing health inequities in healthcare through algorithmic fairness.
method Empirical case study using models to estimate atherosclerotic cardiovascular disease risk.
result Incorporating fairness considerations into model training objective does not improve model performance or net benefit.
There are non-vanishing price responses across different stocks in correlated financial markets. We further study this issue by performing different averages, which identify active and passive cross-responses. The two average cross-responses show different characteristic dependences on the time lag. The passive cross-r…
Neural nets detect alarming student responses for quick review.
problem Identifying alarming student responses in online assessments.
method Developed neural network models to flag potentially concerning responses.
result Neural nets can flag alarming responses more efficiently than manual review.
G-Net uses deep learning for complex counterfactual outcome prediction.
problem Estimating counterfactual outcomes under dynamic treatment strategies.
method G-Net is a sequential deep learning framework for G-computation.
result G-Net can handle complex temporal data and provide accurate treatment effects.
Previous studies of the stock price response to individual trades focused on single stocks. We empirically investigate the price response of one stock to the trades of other stocks. How large is the impact of one stock on others and vice versa? -- This impact of trades on the price change across stocks appears to be tr…
CardiacGen generates realistic ECG signals for training deep learning models.
problem Creating realistic synthetic ECG signals for training deep learning models.
method Hierarchical deep generative model with multi-objective loss functions.
result Synthetic ECG signals from CardiacGen can be used for data augmentation and improve classifier performance.
Study evaluates different price response definitions for NASDAQ stocks.
problem Understanding the long-lasting effects of trading activity on stock prices.
method Examined two different price response implementations for NASDAQ Trades and Quotes (TAQ) data.
result Results are qualitatively the same for two different time scale definitions, but response can vary by up to a factor of two.
Dual-decoder model generates responses with targeted sentiment.
problem Generating human-like responses with specific sentiment.
method Simple dual-decoder model with two sentiment decoders connected to one encoder.
result Significant performance gain in sentiment accuracy and word diversity.
Interpretable text-response modelling for structured outcomes
problem Predicting structured responses alongside textual data
method Joint non-negative matrix factorisation and binomial regression
result Recovering stable response-relevant textual signals
LaRT models LLMs' response accuracy and CoT length to evaluate reasoning ability and speed.
problem Valid evaluation of Large Language Models (LLMs) via response accuracy and chain-of-thought length.
method Introduces Latency-Response Theory (LaRT) to jointly model response accuracy and CoT length using latent ability and latent speed.
result LaRT yields higher estimation accuracy and shorter confidence intervals for latent traits compared to IRT.
Previous studies of the stock price response to trades focused on the dynamics of single stocks, i.e. they addressed the self-response. We empirically investigate the price response of one stock to the trades of other stocks in a correlated market, i.e. the cross-responses. How large is the impact of one stock on other…
New method handles correlated responses and interaction effects in multi-response regression.
problem Handling correlated responses and interaction effects in multi-response regression.
method MADMMplasso, an ADMM-based approach for multi-response regression with overlapping groups and interaction effects.
result The proposed method outperforms in prediction and variable selection for correlated responses and interaction effects.
Study analyzes price response and spread impact in foreign exchange markets.
problem Understanding deviations from Markovian behavior in foreign exchange markets.
method Detailed large-scale data analysis of price response functions for different years and time scales, using pip bid-ask spread definition.
result Large pip spreads significantly impact price response in foreign exchange markets.
PEER tackles multi-response regression with incomplete outcomes efficiently.
problem Challenges in estimating, predicting, and computing with large-scale multi-response regression and incomplete outcomes.
method PEER converts multi-response regression into parallel univariate-response regressions.
result PEER achieves consistency in estimation, prediction, and variable selection.
New method for explaining dialogue response generation models.
problem Interpreting sequence generation models, especially dialogue response generation.
method Local Explanation of Response Generation (LERG) method.
result LERG improves dialogue response generation explanations compared to existing methods.