AI Medical Compendium Topic

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Data Interpretation, Statistical

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Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study.

The Lancet. Digital health
BACKGROUND: Deep learning has the potential to transform health care; however, substantial expertise is required to train such models. We sought to evaluate the utility of automated deep learning software to develop medical image diagnostic classifie...

Comparing a novel machine learning method to the Friedewald formula and Martin-Hopkins equation for low-density lipoprotein estimation.

PloS one
BACKGROUND: Low-density lipoprotein cholesterol (LDL-C) is a target for cardiovascular prevention. Contemporary equations for LDL-C estimation have limited accuracy in certain scenarios (high triglycerides [TG], very low LDL-C).

A non-parametric effect-size measure capturing changes in central tendency and data distribution shape.

PloS one
MOTIVATION: Calculating the magnitude of treatment effects or of differences between two groups is a common task in quantitative science. Standard effect size measures based on differences, such as the commonly used Cohen's, fail to capture the treat...

Robust Estimation of Breast Cancer Incidence Risk in Presence of Incomplete or Inaccurate Information.

Asian Pacific journal of cancer prevention : APJCP
PURPOSE: To evaluate the robustness of multiple machine learning classifiers for breast cancer risk estimation in the presence of incomplete or inaccurate information.

Comparing biological and artificial vision systems: Network measures of functional connectivity.

Neuroscience letters
Advances in Deep Convolutional Neural Networks (DCNN) provide new opportunities for computational neuroscience to pose novel questions regarding the function of biological visual systems. Some attempts have been made to utilize advances in machine le...

Recommendations for Reporting Machine Learning Analyses in Clinical Research.

Circulation. Cardiovascular quality and outcomes
Use of machine learning (ML) in clinical research is growing steadily given the increasing availability of complex clinical data sets. ML presents important advantages in terms of predictive performance and identifying undiscovered subpopulations of ...

Statistical and Machine-Learning Analyses in Nutritional Genomics Studies.

Nutrients
Nutritional compounds may have an influence on different OMICs levels, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and metagenomics. The integration of OMICs data is challenging but may provide new knowledge to explain...

DNN-assisted statistical analysis of a model of local cortical circuits.

Scientific reports
In neuroscience, computational modeling is an effective way to gain insight into cortical mechanisms, yet the construction and analysis of large-scale network models-not to mention the extraction of underlying principles-are themselves challenging ta...

[A brief history of artificial intelligence].

Medecine sciences : M/S
For more than a decade, we have witnessed an acceleration in the development and the adoption of artificial intelligence (AI) technologies. In medicine, it impacts clinical and fundamental research, hospital practices, medical examinations, hospital ...

Data Analysis for Antibody Arrays.

Methods in molecular biology (Clifton, N.J.)
When obtaining high-throughput data from antibody arrays, researchers have to face a couple of questions: How and by what means can they get reasonable results significant to their research from these data? Similar to a gene microarray, the classical...