AIMC Topic: Data Interpretation, Statistical
Clear Filters Showing 61 to 70 of 233 articles
Untargeted Metabolomics for Metabolic Diagnostic Screening with Automated Data Interpretation Using a Knowledge-Based Algorithm.
International journal of molecular sciences
Feb 1, 2020
Untargeted metabolomics may become a standard approach to address diagnostic requests, but, at present, data interpretation is very labor-intensive. To facilitate its implementation in metabolic diagnostic screening, we developed a method for automat...
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation.
BMC genomics
Jan 2, 2020
BACKGROUND: To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learn...
Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review.
Psychiatry research
Dec 9, 2019
Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is th...
Vulnerability Indexing to Saltwater Intrusion from Models at Two Levels using Artificial Intelligence Multiple Model (AIMM).
Journal of environmental management
Dec 3, 2019
Unplanned groundwater exploitation in coastal aquifers results in water decline and consequently triggers saltwater intrusion (SWI). This study formulates a novel modeling strategy based on GALDIT method using Artificial Intelligence (AI) models for ...
Machine learning algorithm validation with a limited sample size.
PloS one
Nov 7, 2019
Advances in neuroimaging, genomic, motion tracking, eye-tracking and many other technology-based data collection methods have led to a torrent of high dimensional datasets, which commonly have a small number of samples because of the intrinsic high c...
Deep learning, reusable and problem-based architectures for detection of consolidation on chest X-ray images.
Computer methods and programs in biomedicine
Oct 31, 2019
BACKGROUND AND OBJECTIVE: In most patients presenting with respiratory symptoms, the findings of chest radiography play a key role in the diagnosis, management, and follow-up of the disease. Consolidation is a common term in radiology, which indicate...
Estimating treatment effects with machine learning.
Health services research
Oct 10, 2019
OBJECTIVE: To demonstrate the performance of methodologies that include machine learning (ML) algorithms to estimate average treatment effects under the assumption of exogeneity (selection on observables).
Post-hoc modification of linear models: Combining machine learning with domain information to make solid inferences from noisy data.
NeuroImage
Sep 26, 2019
Linear machine learning models "learn" a data transformation by being exposed to examples of input with the desired output, forming the basis for a variety of powerful techniques for analyzing neuroimaging data. However, their ability to learn the de...
Toxicity Prediction Method Based on Multi-Channel Convolutional Neural Network.
Molecules (Basel, Switzerland)
Sep 17, 2019
Molecular toxicity prediction is one of the key studies in drug design. In this paper, a deep learning network based on a two-dimension grid of molecules is proposed to predict toxicity. At first, the van der Waals force and hydrogen bond were calcul...
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