Scarcity of health care resources could result in the unavoidable conseq...
Knowledge graphs play a vital role in numerous artificial intelligence t...
Strong secrecy communication over a discrete memoryless state-dependent
...
Mobile crowd sensing (MCS) has emerged as an increasingly popular sensin...
Strong secrecy communication over a discrete memoryless state-dependent
...
Leveraging medical record information in the era of big data and machine...
Dynamic diagnosis is desirable when medical tests are costly or
time-con...
Most datasets suffer from partial or complete missing values, which has
...
Objective: Reflex testing protocols allow clinical laboratories to perfo...
Objective: Clinical knowledge enriched transformer models (e.g.,
Clinica...
Hypergraph neural networks can model multi-way connections among nodes o...
Over 12 billion doses of COVID-19 vaccines have been administered at the...
Acute kidney injury (AKI) is a common clinical syndrome characterized by...
As machine learning and artificial intelligence are more frequently bein...
The recent development of imaging and sequencing technologies enables
sy...
Graph neural network (GNN) is effective to model graphs for distributed
...
Dimensionality reduction techniques are powerful tools for data preproce...
Transformers-based models, such as BERT, have dramatically improved the
...
Open-set recognition generalizes a classification task by classifying te...
Background: The increasing adoption of electronic health records (EHR) a...
Systemic lupus erythematosus (SLE) is a rare autoimmune disorder
charact...
Background Sepsis is one of the most life-threatening circumstances for
...
Hypertension is the leading global cause of cardiovascular disease and
p...
Machine learning in medicine leverages the wealth of healthcare data to
...
Sepsis is an important cause of mortality, especially in intensive care ...
Delirium is a common acute onset brain dysfunction in the emergency sett...
We demonstrate 2.5-GHz-spacing frequency multiplexing capable of aggrega...
Federated learning is a distributed machine learning paradigm where mult...
Persistent homology is a topological feature used in a variety of
applic...
Locally repairable codes (LRCs), which can recover any symbol of a codew...
We develop a regularization method which finds flat minima during the
tr...
Genetic pathways usually encode molecular mechanisms that can inform tar...
Graph Neural Network (GNN) aggregates the neighborhood of each node into...
Joint image-text embedding extracted from medical images and associated
...
This paper investigates covert communication over an additive white Gaus...
In inference, open-set classification is to either classify a sample int...
Anomaly detection is crucial to ensure the security of cyber-physical sy...
Distributed representations of medical concepts have been used to suppor...
Our research focuses on studying and developing methods for reducing the...
Predicting patient mortality is an important and challenging problem in ...
Distributed representations have been used to support downstream tasks i...
This paper considers the achievability and converse bounds on the maxima...
Knowledge graphs are important resources for many artificial intelligenc...
Training accurate deep neural networks (DNNs) in the presence of noisy l...
The problem of missing values in multivariable time series is a key chal...
The problem of missing values in multivariable time series is a key chal...
Acute Kidney Injury (AKI) is a common clinical syndrome characterized by...
Laboratory testing and medication prescription are two of the most impor...
Image representation is a fundamental task in computer vision. However, ...
Covert communication over an additive white Gaussian noise (AWGN) channe...