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Found 13 papers, 0 papers with code
Global Contrastive Training for Multimodal Electronic Health Records with Language Supervision
To harness the interconnected relationships between medical time series and clinical notes, the framework equips a global contrastive loss, aligning a patient's multimodal feature representations with the corresponding discharge summaries.
Federated learning model for predicting major postoperative complications
Federated learning models achieved comparable AUROC performance to central learning models, except for prolonged ICU stay, where the performance of federated learning models was slightly higher than central learning models at UFH GNV center, but slightly lower at UFH JAX center.
A multi-cohort study on prediction of acute brain dysfunction states using selective state space models
Our research fills these gaps in the existing literature by dynamically predicting delirium, coma, and mortality for 12-hour intervals throughout an ICU stay and validating on two public datasets.
Temporal Cross-Attention for Dynamic Embedding and Tokenization of Multimodal Electronic Health Records
The breadth, scale, and temporal granularity of modern electronic health records (EHR) systems offers great potential for estimating personalized and contextual patient health trajectories using sequential deep learning.
Acute kidney injury prediction for non-critical care patients: a retrospective external and internal validation study
We trained local models for each site (UFH Model trained on UFH, UPMC Model trained on UPMC) and a separate model with a development cohort of patients from both sites (UFH-UPMC Model).
APRICOT-Mamba: Acuity Prediction in Intensive Care Unit (ICU): Development and Validation of a Stability, Transitions, and Life-Sustaining Therapies Prediction Model
The acuity state of patients in the intensive care unit (ICU) can quickly change from stable to unstable.
Detecting Visual Cues in the Intensive Care Unit and Association with Patient Clinical Status
We leveraged our AU-ICU dataset with 107, 064 frames collected in the ICU annotated with facial action units (AUs) labels by trained annotators.
Identifying acute illness phenotypes via deep temporal interpolation and clustering network on physiologic signatures
With clustering analysis for vital signs within six hours of admission, patient phenotypes with distinct pathophysiological signatures and outcomes may support early clinical decisions.
Transformer Models for Acute Brain Dysfunction Prediction
In this work, we develop a machine learning system for real-time prediction of ADB using Electronic Health Record (HER) data.
Computable Phenotypes to Characterize Changing Patient Brain Dysfunction in the Intensive Care Unit
We developed algorithms to quantify acute brain dysfunction status including coma, delirium, normal, or death at 12-hour intervals of each ICU admission and to identify acute brain dysfunction phenotypes using continuous acute brain dysfunction status and k-means clustering approach.
Clinical Courses of Acute Kidney Injury in Hospitalized Patients: A Multistate Analysis
At 14 days following Stage 1 AKI, patients with more frail conditions (Charlson comorbidity index greater than or equal to 3 and had prolonged ICU stay) had lower proportion of transitioning to No AKI or discharge states.
Application of Deep Interpolation Network for Clustering of Physiologic Time Series
M Interpretation: In a heterogeneous cohort of hospitalized patients, a deep interpolation network extracted representations from vital sign data measured within six hours of hospital admission.
Computable Phenotypes of Patient Acuity in the Intensive Care Unit
We gathered two single-center, longitudinal electronic health record datasets for 51, 372 adult ICU patients admitted to the University of Florida Health (UFH) Gainesville (GNV) and Jacksonville (JAX).
