Leveraging machine learning and artificial intelligence to improve peripheral artery disease detection, treatment, and outcomes
Peripheral artery disease is an atherosclerotic disorder which, when present, portends poor
patient outcomes. Low diagnosis rates perpetuate poor management, leading to limb loss …
patient outcomes. Low diagnosis rates perpetuate poor management, leading to limb loss …
Model inversion attacks that exploit confidence information and basic countermeasures
Machine-learning (ML) algorithms are increasingly utilized in privacy-sensitive applications
such as predicting lifestyle choices, making medical diagnoses, and facial recognition. In a …
such as predicting lifestyle choices, making medical diagnoses, and facial recognition. In a …
Racial and ethnic disparities in radiology: a call to action
JR Betancourt, A Tan-McGrory, E Flores… - Journal of the American …, 2019 - Elsevier
The US health care system is in the midst of incredible transformation. High-value, high-
quality health care is the ultimate goal. Guided by the Institute of Medicine report “Crossing …
quality health care is the ultimate goal. Guided by the Institute of Medicine report “Crossing …
Early predictions of movie success: The who, what, and when of profitability
We focus on predicting the profitability of a movie to support movie-investment decisions at
early stages of film production. By leveraging data from various sources, and using social …
early stages of film production. By leveraging data from various sources, and using social …
Objective metrics and gradient descent algorithms for adversarial examples in machine learning
Fueled by massive amounts of data, models produced by machine-learning (ML) algorithms
are being used in diverse domains where security is a concern, such as, automotive …
are being used in diverse domains where security is a concern, such as, automotive …
A health decision support system for disease diagnosis based on wearable medical sensors and machine learning ensembles
Even with an annual expenditure of more than $3 trillion, the US healthcare system is far
from optimal. For example, the third leading cause of death in the US is preventable medical …
from optimal. For example, the third leading cause of death in the US is preventable medical …
Constrained optimization of objective functions determined from random forests
In this paper, we examine a data‐driven optimization approach to making optimal decisions
as evaluated by a trained random forest, where these decisions can be constrained by an …
as evaluated by a trained random forest, where these decisions can be constrained by an …
Defending against membership inference attacks with high utility by GAN
L Hu, J Li, G Lin, S Peng, Z Zhang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The success of machine learning (ML) depends on the availability of large-scale datasets.
However, recent studies have shown that models trained on such datasets are vulnerable to …
However, recent studies have shown that models trained on such datasets are vulnerable to …
A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare
In many healthcare applications, datasets for classification may be highly imbalanced due to
the rare occurrence of target events such as disease onset. The SMOTE (Synthetic Minority …
the rare occurrence of target events such as disease onset. The SMOTE (Synthetic Minority …
Socioeconomic and demographic predictors of missed opportunities to provide advanced imaging services
Purpose The extent to which racial and socioeconomic disparities exist in accessing
clinically appropriate, advanced diagnostic imaging has not been well studied. This study …
clinically appropriate, advanced diagnostic imaging has not been well studied. This study …