AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Linear Models

Showing 301 to 310 of 559 articles

Clear Filters

Gaussian Process Regression Models for the Prediction of Hydrogen Bond Acceptor Strengths.

Molecular informatics
We present two approaches for the computation of hydrogen bond acceptor strengths, one by machine-learning and one by a composite quantum-mechanical protocol, both based on the well-established pK scale and dataset. The QM calculations after a necess...

Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next-Generation Sequencing Data.

Risk analysis : an official publication of the Society for Risk Analysis
Next-generation sequencing (NGS) data present an untapped potential to improve microbial risk assessment (MRA) through increased specificity and redefinition of the hazard. Most of the MRA models do not account for differences in survivability and vi...

High waist-to-hip ratio levels are associated with insulin resistance markers in normal-weight women.

Diabetes & metabolic syndrome
AIM: To assess the association between high waist-to-hip ratio (WHR) levels and insulin resistance (IR) or hyperinsulinemia after oral glucose tolerance test (OGTT) in a sample of normal-weight women.

Multistability of Switched Neural Networks With Piecewise Linear Activation Functions Under State-Dependent Switching.

IEEE transactions on neural networks and learning systems
This paper is concerned with the multistability of switched neural networks with piecewise linear activation functions under state-dependent switching. Under some reasonable assumptions on the switching threshold and activation functions, by using th...

In Silico Prediction of O⁶-Methylguanine-DNA Methyltransferase Inhibitory Potency of Base Analogs with QSAR and Machine Learning Methods.

Molecules (Basel, Switzerland)
O⁶-methylguanine-DNA methyltransferase (MGMT), a unique DNA repair enzyme, can confer resistance to DNA anticancer alkylating agents that modify the O⁶-position of guanine. Thus, inhibition of MGMT activity in tumors has a great interest for cancer r...

A machine learning ensemble to predict treatment outcomes following an Internet intervention for depression.

Psychological medicine
BACKGROUND: Some Internet interventions are regarded as effective treatments for adult depression, but less is known about who responds to this form of treatment.

Semi-supervised machine learning approaches for predicting the chronology of archaeological sites: A case study of temples from medieval Angkor, Cambodia.

PloS one
Archaeologists often need to date and group artifact types to discern typologies, chronologies, and classifications. For over a century, statisticians have been using classification and clustering techniques to infer patterns in data that can be defi...

Rainfall time series disaggregation in mountainous regions using hybrid wavelet-artificial intelligence methods.

Environmental research
In mountainous regions, rainfall can be extremely variable in space and time. The need to simulate rainfall time series at different scales on one hand and the lack of recording such parameters in small scales because of administrative and economic p...

Urban population exposure to tropospheric ozone: A multi-country forecasting of SOMO35 using artificial neural networks.

Environmental pollution (Barking, Essex : 1987)
Urban population exposure to tropospheric ozone is a serious health concern in Europe countries. Although there are insufficient evidence to derive a level below which ozone has no effect on mortality WHO (World Health Organization) uses SOMO35 (sum ...

Social and nutritional factors shape larval aggregation, foraging, and body mass in a polyphagous fly.

Scientific reports
The majority of insect species have a clearly defined larval stage during development. Larval nutrition is crucial for individuals' growth and development, and larval foraging success often depends on both resource availability and competition for th...