AIMC Topic: Bias

Clear Filters Showing 141 to 150 of 323 articles

Radiomic Analysis: Study Design, Statistical Analysis, and Other Bias Mitigation Strategies.

Radiology
Rapid advances in automated methods for extracting large numbers of quantitative features from medical images have led to tremendous growth of publications reporting on radiomic analyses. Translation of these research studies into clinical practice c...

Chemical property prediction under experimental biases.

Scientific reports
Predicting the chemical properties of compounds is crucial in discovering novel materials and drugs with specific desired characteristics. Recent significant advances in machine learning technologies have enabled automatic predictive modeling from pa...

Feature blindness: A challenge for understanding and modelling visual object recognition.

PLoS computational biology
Humans rely heavily on the shape of objects to recognise them. Recently, it has been argued that Convolutional Neural Networks (CNNs) can also show a shape-bias, provided their learning environment contains this bias. This has led to the proposal tha...

Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review.

BMC medical research methodology
BACKGROUND: Describe and evaluate the methodological conduct of prognostic prediction models developed using machine learning methods in oncology.

Prediction of hypertension using traditional regression and machine learning models: A systematic review and meta-analysis.

PloS one
OBJECTIVE: We aimed to identify existing hypertension risk prediction models developed using traditional regression-based or machine learning approaches and compare their predictive performance.

Implicit data crimes: Machine learning bias arising from misuse of public data.

Proceedings of the National Academy of Sciences of the United States of America
SignificancePublic databases are an important resource for machine learning research, but their growing availability sometimes leads to "off-label" usage, where data published for one task are used for another. This work reveals that such off-label u...

Diagnostic Accuracy of Artificial Intelligence in Glaucoma Screening and Clinical Practice.

Journal of glaucoma
PURPOSE: Artificial intelligence (AI) has been shown as a diagnostic tool for glaucoma detection through imaging modalities. However, these tools are yet to be deployed into clinical practice. This meta-analysis determined overall AI performance for ...

Missing data imputation in clinical trials using recurrent neural network facilitated by clustering and oversampling.

Biometrical journal. Biometrische Zeitschrift
In clinical practice, the composition of missing data may be complex, for example, a mixture of missing at random (MAR) and missing not at random (MNAR) assumptions. Many methods under the assumption of MAR are available. Under the assumption of MNAR...

GGA-MLP: A Greedy Genetic Algorithm to Optimize Weights and Biases in Multilayer Perceptron.

Contrast media & molecular imaging
The task of designing an Artificial Neural Network (ANN) can be thought of as an optimization problem that involves many parameters whose optimal value needs to be computed in order to improve the classification accuracy of an ANN. Two of the major p...

Data and Model Biases in Social Media Analyses: A Case Study of COVID-19 Tweets.

AMIA ... Annual Symposium proceedings. AMIA Symposium
During the coronavirus disease pandemic (COVID-19), social media platforms such as Twitter have become a venue for individuals, health professionals, and government agencies to share COVID-19 information. Twitter has been a popular source of data for...