Studies in health technology and informatics
May 18, 2023
As the use of artificial intelligence within healthcare is on the rise, an increased attention has been directed towards ethical considerations. Defining fairness in machine learning is a well explored topic with an extensive literature. However, suc...
Studies in health technology and informatics
May 18, 2023
In this communication, we demonstrate that the bias observed in domain general training sets with health-related content is not improved in domain specific health-communication corpora, contra.
The journal of applied laboratory medicine
Jan 4, 2023
BACKGROUND: Methods of machine learning provide opportunities to use real-world data to solve complex problems. Applications of these methods in laboratory medicine promise to increase diagnostic accuracy and streamline laboratory operations leading ...
The bias-variance tradeoff is a theoretical concept that suggests machine learning algorithms are susceptible to two kinds of error, with some algorithms tending to suffer from one more than the other. In this letter, we claim that the bias-variance ...
Solving the inverse problem is a major challenge in contemporary nano-optics. However, frequently not just a possible solution needs to be found but rather the solution that accommodates constraints imposed by the problem at hand. To select the most ...
Journal of the American Medical Informatics Association : JAMIA
Jun 14, 2022
OBJECTIVE: Artificial intelligence (AI) models may propagate harmful biases in performance and hence negatively affect the underserved. We aimed to assess the degree to which data quality of electronic health records (EHRs) affected by inequities rel...
Studies in health technology and informatics
May 25, 2022
Multimorbidity, having a diagnosis of two or more chronic conditions, increases as people age. It is a predictor used in clinical decision-making, but underdiagnosis in underserved populations produces bias in the data that support algorithms used in...