AI Medical Compendium Topic:
Models, Theoretical

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New-Onset Alzheimer's Disease and Normal Subjects 100% Differentiated by P300.

American journal of Alzheimer's disease and other dementias
Previous work has suggested that evoked potential analysis might allow the detection of subjects with new-onset Alzheimer's disease, which would be useful clinically and personally. Here, it is described how subjects with new-onset Alzheimer's diseas...

Machine Learning in Nuclear Medicine: Part 1-Introduction.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
This article, the first in a 2-part series, provides an introduction to machine learning (ML) in a nuclear medicine context. This part addresses the history of ML and describes common algorithms, with illustrations of when they can be helpful in nucl...

Stability analysis of Cohen-Grossberg neural networks of neutral-type: Multiple delays case.

Neural networks : the official journal of the International Neural Network Society
The essential purpose of this work is to conduct an investigation into stability problem for the class of neutral-type Cohen-Grossberg neural networks including multiple time delays in states and multiple neutral delays in time derivative of states. ...

A Novel Approach of Mathematical Theory of Shape and Neuro-Fuzzy Based Diagnostic Analysis of Cervical Cancer.

Pathology oncology research : POR
This study aims to detect the abnormal growth of tissue in cervix region for diagnosis of cervical cancer using Pap test of patients. The proposed methodology classifies cervical cancer for pattern recognition either benign or malignant stages using ...

Neurodevelopmental heterogeneity and computational approaches for understanding autism.

Translational psychiatry
In recent years, the emerging field of computational psychiatry has impelled the use of machine learning models as a means to further understand the pathogenesis of multiple clinical disorders. In this paper, we discuss how autism spectrum disorder (...

Models and Machines: How Deep Learning Will Take Clinical Pharmacology to the Next Level.

CPT: pharmacometrics & systems pharmacology
Recent advances in machine learning (ML) have led to enthusiasm about its use throughout the biopharmaceutical industry. The ML methods can be applied to a wide range of problems and have the potential to revolutionize aspects of drug development. Th...

Effect of incremental feature enrichment on healthcare text classification system: A machine learning paradigm.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Healthcare tweets are particularly challenging due to its sparse layout and its limited character size. Compared to previous method based on "bag of words" (BOW) model, this study uniquely identifies the enrichment protocol ...

Gene fingerprint model for literature based detection of the associations among complex diseases: a case study of COPD.

BMC medical informatics and decision making
BACKGROUND: Disease comorbidity is very common and has significant impact on disease treatment. Revealing the associations among diseases may help to understand the mechanisms of diseases, improve the prevention and treatment of diseases, and support...

An ecologically constrained procedure for sensitivity analysis of Artificial Neural Networks and other empirical models.

PloS one
Sensitivity analysis applied to Artificial Neural Networks (ANNs) as well as to other types of empirical ecological models allows assessing the importance of environmental predictive variables in affecting species distribution or other target variabl...

Disturbance and uncertainty rejection performance for fractional-order complex dynamical networks.

Neural networks : the official journal of the International Neural Network Society
This paper investigates the synchronization issue for a family of time-delayed fractional-order complex dynamical networks (FCDNs) with time delay, unknown bounded uncertainty and disturbance. A novel fractional uncertainty and disturbance estimator ...