Deep network clusters hospital patients' vital signs.
problem Sparse and irregularly collected vital sign data.
method Deep interpolation network for latent representation extraction.
result Extracted 7 distinct clusters from vital sign data.
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48 results for “vital sign data”
Aiming for Relevancecs.LG
Novel metrics improve machine learning models for ICU patient care.
problem Predicting vital sign trajectories for early detection of adverse events.
method Developed novel performance metrics aligned with clinical contexts, validated on simulated and real datasets, and optimized neural networks using these metrics.
result Neural networks trained with these metrics excel in predicting clinically significant events.
Ward2ICU dataset protects patient privacy while generating synthetic ICU transitions data.
problem Protecting patient privacy while creating synthetic ICU transition data.
method Wasserstein Generative Adversarial Network (GAN) to generate synthetic data, class label balancing.
result Quality of synthetic data generation assessed through binary classification task.
Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learningcs.LG
Detects physiological patterns to hemodynamic stress using unsupervised deep learning.
problem Identify and characterize physiological responses to hemorrhage in raw vital sign data.
method Transform vital sign time series into latent space using unsupervised deep learning, identify clusters, and evaluate latent embeddings.
result Clusters in latent embeddings correspond to physiological response patterns matching physicians' intuition.
We study multiple rule-based and machine learning (ML) models for sepsis detection. We report the first neural network detection and prediction results on three categories of sepsis. We have used the retrospective Medical Information Mart for Intensive Care (MIMIC)-III dataset, restricted to intensive care unit (ICU) p…
GRU-D detects age-specific missing patterns in vital signs.
problem Temporal missingness in clinical time series data.
method Gated recurrent unit with decay mechanisms (GRU-D) trained on MIMIC-IV vital signs.
result GRU-D achieves AUROC 0.780 and AUPRC 0.810 on bootstrapped data.
In latent Gaussian trees the pairwise correlation signs between the variables are intrinsically unrecoverable. Such information is vital since it completely determines the direction in which two variables are associated. In this work, we resort to information theoretical approaches to achieve two fundamental goals: Fir…
Identification of physiological shock in intensive care units via Bayesian regime switching modelsstat.AP
Bayesian model detects internal bleeding in ICU patients.
problem Late detection of internal bleeding in ICU patients.
method Bayesian regime switching model analyzing vitals and labs.
result Probabilistic assessment of patient's physiological state.
FineHand learns hand shapes for better ASL recognition.
problem Difficult ASL recognition due to fast, articulate gestures.
method Combines manual and deep learning for hand shape embeddings, uses RNN for sequential gestures.
result Improved video gesture recognition accuracy on GMU-ASL51 benchmark.
Patient subtyping based on temporal observations can lead to significantly nuanced subtyping that acknowledges the dynamic characteristics of diseases. Existing methods for subtyping trajectories treat the evolution of clinical observations as a homogeneous process or employ data available at regular intervals. In real…
Critically ill patients in regular wards are vulnerable to unanticipated clinical dete- rioration which requires timely transfer to the intensive care unit (ICU). To allow for risk scoring and patient monitoring in such a setting, we develop a novel Semi- Markov Switching Linear Gaussian Model (SSLGM) for the inpatient…
New method generates diverse EHR data types while maintaining privacy.
problem Privacy risks in sharing EHRs across large scales.
method Refined GAN model, feature constraints, and utility measures.
result New model preserves EHR statistics, correlations, and constraints.
With the improvement of medical data capturing, vast amount of continuous patient monitoring data, e.g., electrocardiogram (ECG), real-time vital signs and medications, become available for clinical decision support at intensive care units (ICUs). However, it becomes increasingly challenging to model such data, due to …
Study improves mortality prediction in hospital patients using comprehensive feature engineering.
problem Accurate prediction of all-cause in-hospital mortality in healthcare.
method Comprehensive feature engineering approach using vital signs, laboratory results, and demographic data.
result Random Forest model achieved highest performance with AUC of 0.94, significantly outperforming other models.
Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank studycs.LG
Study develops a dynamic risk model for COVID-19 mortality using UK Biobank data.
problem Developing tools to monitor high-risk patients during the COVID-19 pandemic.
method Data-driven random forest classification model using baseline characteristics and symptoms.
result Model predicts COVID-19 mortality with excellent performance (AUC: 0.91), identifying novel predictors.
This work aims to create a large-scale model for critical care time series data.
problem Lack of large-scale datasets and distribution shifts in critical care time series data.
method Harmonized dataset creation and transfer learning research.
result Established a foundation for large-scale multi-variate time series models in critical care.
Model predicts patient outcomes from EHR data by limiting feature interactions.
problem Predicting patient outcomes from noisy, missing EHR data.
method Recurrent neural network with feature interaction restriction.
result Improvement in AU-ROC for mortality prediction (1.1% improvement, p<0.01).
Personalized Risk Scoring for Critical Care Patients using Mixtures of Gaussian Process Expertscs.LG
We develop a personalized real time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs. Heterogeneity of the patients population is captured via a hierarchical latent class model. The proposed algorithm aims t…
Kernel TCK_IM tackles missing data in EHR time series, improving analysis.
problem Missing data complicates analysis of EHR time series.
method TCK_IM kernel using ensemble learning of mixed mode Bayesian mixture models.
result TCK_IM kernel effectively exploits missing data without imputation.
In critical care, intensivists are required to continuously monitor high dimensional vital signs and lab measurements to detect and diagnose acute patient conditions. This has always been a challenging task. In this study, we propose a novel self-correcting deep learning prediction approach to address this challenge. W…
Novel GNN for signed and directed networks using magnetic signed Laplacian.
problem Efficiently modeling signed and directed networks for tasks like clustering and link prediction.
method Introduced a magnetic signed Laplacian for directed signed graphs, used it to construct a spectral GNN.
result Demonstrated effective performance on tasks involving signed and directional information.
Traffic sign recognition is an important component of many advanced driving assistance systems, and it is required for full autonomous driving. Computational performance is usually the bottleneck in using large scale neural networks for this purpose. SqueezeNet is a good candidate for efficient image classification of …
Study develops sign recognition system for DHH users.
problem Accessibility of voice-controlled devices for Deaf and Hard-of-Hearing users.
method Multimodal data (RGB video and skeletal data) for sign language recognition using deep learning.
result Validation on GMUASL51 dataset of 12 users and 13107 samples across 51 signs.
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.
New method identifies sepsis-related patient features in EMR data.
problem Identify sepsis-related patient features in EMR data.
method Linear multivariate Hawkes process model with ReLU link function, coupled with gradient-based method.
result Identifies several interpretable GC chains that precede sepsis.
Study on signed graphs with random signs, focusing on community detection.
problem Community detection in signed stochastic block models.
method Strong concentration inequalities for adjacency and Laplacian matrices, applied to signed Laplacian matrix.
result The sign of the first eigenvector of the Laplacian matrix defines a weakly consistent estimator for balanced community detection.
PyTorch Geometric Signed Directed fills the gap for GNNs on signed and directed graphs.
problem Lack of unified software packages for GNNs on signed and directed networks.
method Developed a software package with GNN models, synthetic and real-world data, and evaluation metrics.
result Demonstrates the effectiveness of the implemented methods through experiments.
Method predicts which high-dimensional correlation signs will change in the future.
problem Predicting which correlation matrix coefficients will change signs in high-dimensional data.
method Stability of correlation signs depends on three-by-three relationships, inspired by Heider social cohesion theory.
result The method accurately predicts the stability of correlation signs in high-dimensional data.
Develops method for learning signed graphs from smooth signals.
problem Learning signed graphs from observed data, especially in contexts with both positive and negative interactions.
method Uses net Laplacian as graph shift operator and minimizes total variation of observed signals with ADMM.
result Theoretical proofs of convergence and estimation error bound provided.
SELO model predicts link signs better than SDGNN using subgraph encoding and linear optimization.
problem Inferring the sign of links in signed networks with limited sign data.
method Subgraph Encoding via Linear Optimization (SELO) approach to learn edge embeddings.
result SELO model outperforms state-of-the-art methods on multiple real-world signed networks.
Systemic Risk Surveillanceecon.EM
Online surveillance detects systemic risk in financial markets.
problem Detecting and monitoring systemic risk in financial markets.
method Online monitoring procedures for multiple series, controlling for false rejections.
result Procedures allow timely detection of financial distress.
We present a method for learning the parameters of a Bayesian network with prior knowledge about the signs of influences between variables. Our method accommodates not just the standard signs, but provides for context-specific signs as well. We show how the various signs translate into order constraints on the network …
New method estimates tensors from noisy data with missing entries.
problem Tensor estimation from noisy observations with missing entries.
method Sign series representation for tensor completion, addressing low- and high-rank signals.
result Excess risk bounds, estimation error rates, and sample complexities established.
Proposes a privacy-preserving sign selection method for distributed systems.
problem Sign selection in distributed differentially private settings.
method Iterative peeling of stability function combined with exponential mechanism.
result Recovery of support and signs with optimal signal-to-noise ratio.
New tests for high-dimensional data improve on existing methods.
problem Testing mean vectors in high-dimensional data.
method Generalized multivariate sign transformation, using different norm functions.
result Tests using generalized signs have higher power than existing tests.
Objective: Predict patient-specific vitals deemed medically acceptable for discharge from a pediatric intensive care unit (ICU). Design: The means of each patient's hr, sbp and dbp measurements between their medical and physical discharge from the ICU were computed as a proxy for their physiologically acceptable state …
New approach models individual vitality for better mortality predictions.
problem Limited ability of existing mortality models to capture individual complexity.
method Developed a four-component framework for individual vitality dynamics.
result Demonstrates improved analytical and practical outcomes in life insurance and lifetime decision-making.
Objective: To compare different deep learning architectures for predicting the risk of readmission within 30 days of discharge from the intensive care unit (ICU). The interpretability of attention-based models is leveraged to describe patients-at-risk. Methods: Several deep learning architectures making use of attentio…
The paper introduces DP algorithms using random projections and sign random projections for improved privacy in machine learning.
problem Improving differential privacy in machine learning applications.
method Developed algorithms based on random projections and sign random projections, focusing on individual differential privacy (iDP) and standard differential privacy (DP).
result DP-SignOPORP and iDP-SignRP achieve superior performance in differential privacy, especially for small epsilon values.
L21 SNF compresses mixed-sign data robustly.
problem Compression of mixed-sign data with high fidelity.
method Regularized L21 Semi-NonNegative Matrix Factorization (L21 SNF).
result Rigorous proof of convergence and use-case advantages demonstrated.
The paper discovers patterns in Maass forms' coefficients related to Fricke signs.
problem Identifying Fricke signs in Maass forms with unknown signs.
method Averaging Fourier coefficients, Linear Discriminant Analysis (LDA), neural networks.
result 96% accuracy in predicting Fricke signs for forms with even parity, 94% for odd parity.
Signed Laplacian Deep Learning with Adversarial Augmentation for Improved Mammography Diagnosiseess.IV
Computer-aided breast cancer diagnosis in mammography is limited by inadequate data and the similarity between benign and cancerous masses. To address this, we propose a signed graph regularized deep neural network with adversarial augmentation, named \textsc{DiagNet}. Firstly, we use adversarial learning to generate p…
Regularized spectral methods improve clustering in signed graphs, especially for sparse data.
problem Clustering signed graphs with positive and negative edges.
method Developed regularized versions of SPONGE and Signed Laplacian methods for clustering signed graphs, especially for sparse data.
result Theoretical guarantees and empirical performance improvements for clustering signed graphs, especially in sparse regimes.
Paper introduces stable vectorization for multiparameter PH using signed barcodes.
problem Lack of stable vectorization methods for multiparameter persistent homology.
method Signed barcodes as measures for stable vectorization of MPH.
result Stable feature vectors from signed barcodes improve performance in data science.
Large SVARsecon.EM
New algorithm speeds up SVAR inference for large datasets.
problem Inference in sign-identified SVARs for big data.
method Elliptical slice within Gibbs sampler for computational efficiency.
result Algorithm delivers posterior distribution and is well-defined.
Unified sign-based compression for federated learning with faster convergence.
problem High communication cost in federated learning with large-scale models.
method Unified noisy perturbation scheme for sign-based compression.
result Achieves faster convergence rate than existing sign-based methods.
Develops a new algorithm to calibrate signed datasets to specified marginals.
problem Calibrating signed datasets to specified marginals.
method Extends Schrödinger-Fortet-Sinkhorn paradigm to sign-indefinite multi-dimensional arrays.
result Proposes an optimization problem to update a sign-indefinite prior to match given marginals.
Proposes OCGNN for detecting anomalies in graph data.
problem Detecting anomalies in graph-structured data.
method One Class Graph Neural Network (OCGNN) combining Graph Neural Networks and one-class classification.
result Significant improvements in anomaly detection compared to baselines.