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Found 31 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.
Leveraging Computer Vision in the Intensive Care Unit (ICU) for Examining Visitation and Mobility
Likewise, while mobility can be an important indicator of recovery or deterioration in ICU patients, it is only captured sporadically or not captured at all.
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).
The Potential of Wearable Sensors for Assessing Patient Acuity in Intensive Care Unit (ICU)
In this study, we evaluated the impact of integrating mobility data collected from wrist-worn accelerometers with clinical data obtained from EHR for developing an AI-driven acuity assessment score.
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.
Transformers in Healthcare: A Survey
With Artificial Intelligence (AI) increasingly permeating various aspects of society, including healthcare, the adoption of the Transformers neural network architecture is rapidly changing many applications.
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.
Predicting risk of delirium from ambient noise and light information in the ICU
This study shows that ambient light and noise intensities are strong predictors of long-term delirium incidence in the ICU.
AI-Enhanced Intensive Care Unit: Revolutionizing Patient Care with Pervasive Sensing
However, the extent of patient monitoring in the ICU is limited due to time constraints and the workload on healthcare providers.
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.
End-to-End Machine Learning Framework for Facial AU Detection in Intensive Care Units
In this work, we evaluated two vision transformer models, namely ViT and SWIN, for AU detection on our Pain-ICU dataset and also external datasets.
Multi-Task Prediction of Clinical Outcomes in the Intensive Care Unit using Flexible Multimodal Transformers
Recent deep learning research based on Transformer model architectures has demonstrated state-of-the-art performance across a variety of domains and tasks, mostly within the computer vision and natural language processing domains.
Posture Recognition in the Critical Care Settings using Wearable Devices
Low physical activity levels in the intensive care units (ICU) patients have been linked to adverse clinical outcomes.
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.
Dynamic Predictions of Postoperative Complications from Explainable, Uncertainty-Aware, and Multi-Task Deep Neural Networks
In a longitudinal cohort study of 56, 242 patients undergoing 67, 481 inpatient surgical procedures at a university medical center, we compared deep learning models with random forests for predicting nine common postoperative complications using preoperative, intraoperative, and perioperative patient data.
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).
Facial Action Unit Detection on ICU Data for Pain Assessment
Previous studies showed the feasibility of automatic pain assessment by detecting Facial Action Units (AUs).
Automated Detection of Rest Disruptions in Critically Ill Patients
Sleep has been shown to be an indispensable and important component of patients recovery process.
Joint Distribution and Transitions of Pain and Activity in Critically Ill Patients
Pain and physical function are both essential indices of recovery in critically ill patients in the Intensive Care Units (ICU).
Human Activity Recognition using Inertial, Physiological and Environmental Sensors: a Comprehensive Survey
In the last decade, Human Activity Recognition (HAR) has become a vibrant research area, especially due to the spread of electronic devices such as smartphones, smartwatches and video cameras present in our daily lives.
Improved Predictive Models for Acute Kidney Injury with IDEAs: Intraoperative Data Embedded Analytics
We used machine learning and statistical analysis techniques to develop perioperative models to predict the risk of AKI (risk during the first 3 days, 7 days, and until the discharge day) before and after the surgery.
Extended Vertical Lists for Temporal Pattern Mining from Multivariate Time Series
We develop a new method called the Fast Temporal Pattern Mining with Extended Vertical Lists.
The Intelligent ICU Pilot Study: Using Artificial Intelligence Technology for Autonomous Patient Monitoring
In this pilot study, we examined the feasibility of using pervasive sensing technology and artificial intelligence for autonomous and granular monitoring of critically ill patients and their environment in the Intensive Care Unit (ICU).
DeepSOFA: A Continuous Acuity Score for Critically Ill Patients using Clinically Interpretable Deep Learning
Traditional methods for assessing illness severity and predicting in-hospital mortality among critically ill patients require time-consuming, error-prone calculations using static variable thresholds.
Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis
The past decade has seen an explosion in the amount of digital information stored in electronic health records (EHR).
