Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI
A growing number of artificial intelligence (AI)-based clinical decision support systems are
showing promising performance in preclinical, in silico, evaluation, but few have yet …
showing promising performance in preclinical, in silico, evaluation, but few have yet …
[HTML][HTML] Evaluation and mitigation of racial bias in clinical machine learning models: sco** review
Background Racial bias is a key concern regarding the development, validation, and
implementation of machine learning (ML) models in clinical settings. Despite the potential of …
implementation of machine learning (ML) models in clinical settings. Despite the potential of …
Algorithmic fairness in computational medicine
J Xu, Y ** Review: Sco** review examines racial and ethnic bias in clinical …
In August 2022 the Department of Health and Human Services (HHS) issued a notice of
proposed rulemaking prohibiting covered entities, which include health care providers and …
proposed rulemaking prohibiting covered entities, which include health care providers and …
Evaluation of clinical prediction models (part 1): from development to external validation
Evaluating the performance of a clinical prediction model is crucial to establish its predictive
accuracy in the populations and settings intended for use. In this article, the first in a three …
accuracy in the populations and settings intended for use. In this article, the first in a three …
Your fairness may vary: Pretrained language model fairness in toxic text classification
The popularity of pretrained language models in natural language processing systems calls
for a careful evaluation of such models in down-stream tasks, which have a higher potential …
for a careful evaluation of such models in down-stream tasks, which have a higher potential …
Bias and fairness assessment of a natural language processing opioid misuse classifier: detection and mitigation of electronic health record data disadvantages …
Objectives To assess fairness and bias of a previously validated machine learning opioid
misuse classifier. Materials & Methods Two experiments were conducted with the classifier's …
misuse classifier. Materials & Methods Two experiments were conducted with the classifier's …
Improving fairness in the prediction of heart failure length of stay and mortality by integrating social determinants of health
Background: Machine learning (ML) approaches have been broadly applied to the
prediction of length of stay and mortality in hospitalized patients. ML may also reduce …
prediction of length of stay and mortality in hospitalized patients. ML may also reduce …
[HTML][HTML] Evaluating and mitigating bias in machine learning models for cardiovascular disease prediction
Objective The study aims to investigate whether machine learning-based predictive models
for cardiovascular disease (CVD) risk assessment show equivalent performance across …
for cardiovascular disease (CVD) risk assessment show equivalent performance across …