Recently, researchers have started applying convolutional neural networks (CNNs) with one-
dimensional convolutions to clinical tasks involving time-series data. This is due, in part, to …

As machine learning (ML) models gain traction in clinical applications, understanding the
impact of clinician and societal biases on ML models is increasingly important. While biases …

Rate-invariant or reparameterization-invariant matching between functions and shapes of
curves, respectively, is an important problem in computer vision and medical imaging. Often …

There has been recent interest by payers, health care systems, and researchers in the
development of machine learning and artificial intelligence models that predict an …

Machine learning applications to real-world settings are often tasked with making
predictions on data generated by multiple sources. There are many methods for …

One of the ways that machine learning algorithms can help control the spread of an
infectious disease is by building models that predict who is likely to become infected making …

Spinal cord injury (SCI) patients undergo intensive inpatient treatment for multiple months,
and suffer from a variety of secondary complications. The electronic health records (EHR) of …

We explore relationships between machine learning (ML) and causal inference. We focus
on improvements in each by borrowing ideas from one another. ML has been successfully …

Clostridioides difficile infection (CDI) is recognized as a leading cause of healthcare-
associated infections in the United States. CDIs lead to poor health outcomes and impose a …

Asymptomatic carriers of an infection make it more challenging to understand the
characteristics of that infection (eg, parameters such as 𝑅0) and to design, implement, and …