AIMC Topic: Bias
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Computational Models Used to Predict Cardiovascular Complications in Chronic Kidney Disease Patients: A Systematic Review.
Medicina (Kaunas, Lithuania)
May 27, 2021
cardiovascular complications (CVC) are the leading cause of death in patients with chronic kidney disease (CKD). Standard cardiovascular disease risk prediction models used in the general population are not validated in patients with CKD. We aim to ...
Authors' Reply to: Minimizing Selection and Classification Biases Comment on "Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing".
Journal of medical Internet research
May 26, 2021
Minimizing Selection and Classification Biases. Comment on "Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing".
Journal of medical Internet research
May 26, 2021
Feasibility of predicting allele specific expression from DNA sequencing using machine learning.
Scientific reports
May 19, 2021
Allele specific expression (ASE) concerns divergent expression quantity of alternative alleles and is measured by RNA sequencing. Multiple studies show that ASE plays a role in hereditary diseases by modulating penetrance or phenotype severity. Howev...
Artificial intelligence in oncology: Path to implementation.
Cancer medicine
May 7, 2021
In recent years, the field of artificial intelligence (AI) in oncology has grown exponentially. AI solutions have been developed to tackle a variety of cancer-related challenges. Medical institutions, hospital systems, and technology companies are de...
Discrepancies in Stroke Distribution and Dataset Origin in Machine Learning for Stroke.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Apr 30, 2021
BACKGROUND: Machine learning algorithms depend on accurate and representative datasets for training in order to become valuable clinical tools that are widely generalizable to a varied population. We aim to conduct a review of machine learning uses i...
Reducing bias to source samples for unsupervised domain adaptation.
Neural networks : the official journal of the International Neural Network Society
Mar 26, 2021
Unsupervised Domain Adaptation (UDA) makes predictions for the target domain data while labels are only available in the source domain. Lots of works in UDA focus on finding a common representation of the two domains via domain alignment, assuming th...
Does "AI" stand for augmenting inequality in the era of covid-19 healthcare?
BMJ (Clinical research ed.)
Mar 15, 2021
Artificial intelligence can help tackle the covid-19 pandemic, but bias and discrimination in its design and deployment risk exacerbating existing health inequity argue
Towards a pragmatist dealing with algorithmic bias in medical machine learning.
Medicine, health care, and philosophy
Mar 13, 2021
Machine Learning (ML) is on the rise in medicine, promising improved diagnostic, therapeutic and prognostic clinical tools. While these technological innovations are bound to transform health care, they also bring new ethical concerns to the forefron...
Convolutional neural network model based on radiological images to support COVID-19 diagnosis: Evaluating database biases.
PloS one
Mar 1, 2021
As SARS-CoV-2 has spread quickly throughout the world, the scientific community has spent major efforts on better understanding the characteristics of the virus and possible means to prevent, diagnose, and treat COVID-19. A valid approach presented i...
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