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Found 16 papers, 1 papers with code
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.
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.
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.
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.
Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs
Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life.
Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health
Human space exploration beyond low Earth orbit will involve missions of significant distance and duration.
Invariant Risk Minimisation for Cross-Organism Inference: Substituting Mouse Data for Human Data in Human Risk Factor Discovery
In this work, we augment small human medical datasets with in-vitro data and animal models.
Universally Rank Consistent Ordinal Regression in Neural Networks
Despite the pervasiveness of ordinal labels in supervised learning, it remains common practice in deep learning to treat such problems as categorical classification using the categorical cross entropy loss.
On Invariance Penalties for Risk Minimization
We discuss the role of its eigenvalues in the relationship between the risk and the invariance penalty, and demonstrate that it is ill-conditioned for said counterexamples.
On Information Gain and Regret Bounds in Gaussian Process Bandits
For the Mat\'ern family of kernels, where the lower bounds on $\gamma_T$, and regret under the frequentist setting, are known, our results close a huge polynomial in $T$ gap between the upper and lower bounds (up to logarithmic in $T$ factors).
Safe Linear Stochastic Bandits
We introduce the safe linear stochastic bandit framework---a generalization of linear stochastic bandits---where, in each stage, the learner is required to select an arm with an expected reward that is no less than a predetermined (safe) threshold with high probability.
An Online Learning Approach to Buying and Selling Demand Response
Assuming that both the parameters of the demand curve and the distribution of the random shocks are initially unknown to the aggregator, we investigate the extent to which the aggregator might dynamically adapt its offered prices and forward contracts to maximize its expected profit over a time window of $T$ days.
Risk-Sensitive Learning and Pricing for Demand Response
Assuming that both the parameters of the demand curve and the distribution of the random shocks are initially unknown to the utility, we investigate the extent to which the utility might dynamically adjust its offered prices to maximize its cumulative risk-sensitive payoff over a finite number of $T$ days.
