AIMC Topic: Machine Learning

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Multi-label classification and label dependence in in silico toxicity prediction.

Toxicology in vitro : an international journal published in association with BIBRA
Most computational predictive models are specifically trained for a single toxicity endpoint and lack the ability to learn dependencies between endpoints, such as those targeting similar biological pathways. In this study, we compare the performance ...

Classification Criteria for Intermediate Uveitis, Non-Pars Planitis Type.

American journal of ophthalmology
PURPOSE: To determine classification criteria for intermediate uveitis, non-pars planitis type (IU-NPP, also known as undifferentiated intermediate uveitis).

Demystifying uncertainty in PM10 susceptibility mapping using variable drop-off in extreme-gradient boosting (XGB) and random forest (RF) algorithms.

Environmental science and pollution research international
This study investigates uncertainty in machine learning that can occur when there is significant variance in the prediction importance level of the independent variables, especially when the ROC fails to reflect the unbalanced effect of prediction va...

Prediction of vancomycin dose on high-dimensional data using machine learning techniques.

Expert review of clinical pharmacology
OBJECTIVES: Despite therapeutic vancomycin is regularly monitored, its dose requirements vary considerably between individuals. Various innovative vancomycin dosing strategies have been developed for dose optimization; however, the utilization of ind...

Does Physical Activity Predict Obesity-A Machine Learning and Statistical Method-Based Analysis.

International journal of environmental research and public health
BACKGROUND: Obesity prevalence has become one of the most prominent issues in global public health. Physical activity has been recognized as a key player in the obesity epidemic.

Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data.

Scientific reports
A key challenge to gaining insight into complex systems is inferring nonlinear causal directional relations from observational time-series data. Specifically, estimating causal relationships between interacting components in large systems with only s...

Artificial neural network model for predicting changes in ion channel conductance based on cardiac action potential shapes generated via simulation.

Scientific reports
Many studies have revealed changes in specific protein channels due to physiological causes such as mutation and their effects on action potential duration changes. However, no studies have been conducted to predict the type of protein channel abnorm...

A clinical decision model based on machine learning for ptosis.

BMC ophthalmology
BACKGROUND: To establish a decision model based on two- (2D) and three-dimensional (3D) eye data of patients with ptosis for developing personalized surgery plans.

High precision localization of pulmonary nodules on chest CT utilizing axial slice number labels.

BMC medical imaging
BACKGROUND: Reidentification of prior nodules for temporal comparison is an important but time-consuming step in lung cancer screening. We develop and evaluate an automated nodule detector that utilizes the axial-slice number of nodules found in radi...