AI Medical Compendium Topic:
Models, Statistical

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Machine learning combined with non-targeted LC-HRMS analysis for a risk warning system of chemical hazards in drinking water: A proof of concept.

Talanta
Guaranteeing clean drinking water to the global population is becoming more challenging, because of the cases of water scarcity across the globe, growing population, and increased chemical footprint of this population. Existing targeted strategies fo...

Machine Learning Distinguishes with High Accuracy between Pan-Assay Interference Compounds That Are Promiscuous or Represent Dark Chemical Matter.

Journal of medicinal chemistry
Assay interference compounds give rise to false-positives and cause substantial problems in medicinal chemistry. Nearly 500 compound classes have been designated as pan-assay interference compounds (PAINS), which typically occur as substructures in o...

Prediction of the Formation of Reactive Metabolites by A Novel Classifier Approach Based on Enrichment Factor Optimization (EFO) as Implemented in the VEGA Program.

Molecules (Basel, Switzerland)
The study is aimed at developing linear classifiers to predict the capacity of a given substrate to yield reactive metabolites. While most of the hitherto reported predictive models are based on the occurrence of known structural alerts (e.g., the pr...

Prognostic models in primary biliary cholangitis.

Journal of autoimmunity
Risk prediction modelling is important to better understand the determinants of the course and outcome of PBC and to inform the risk across the disease continuum in PBC enabling risk-stratified follow-up care and personalised therapy. Current prognos...

Using Unlabeled Data to Discover Bivariate Causality with Deep Restricted Boltzmann Machines.

IEEE/ACM transactions on computational biology and bioinformatics
An important question in microbiology is whether treatment causes changes in gut flora, and whether it also affects metabolism. The reconstruction of causal relations purely from non-temporal observational data is challenging. We address the problem ...

A Novel Classification and Identification Scheme of Emitter Signals Based on Ward's Clustering and Probabilistic Neural Networks with Correlation Analysis.

Computational intelligence and neuroscience
The rapid development of modern communication technology makes the identification of emitter signals more complicated. Based on Ward's clustering and probabilistic neural networks method with correlation analysis, an ensemble identification algorithm...

Assessment of Time-Series Machine Learning Methods for Forecasting Hospital Discharge Volume.

JAMA network open
IMPORTANCE: Forecasting the volume of hospital discharges has important implications for resource allocation and represents an opportunity to improve patient safety at periods of elevated risk.

A novel retinal vessel detection approach based on multiple deep convolution neural networks.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Computer aided detection (CAD) offers an efficient way to assist doctors to interpret fundus images. In a CAD system, retinal vessel (RV) detection is a crucial step to identify the retinal disease regions. However, RV detec...

Stacked classifiers for individualized prediction of glycemic control following initiation of metformin therapy in type 2 diabetes.

Computers in biology and medicine
OBJECTIVE: Metformin is the preferred first-line medication for management of type 2 diabetes and prediabetes. However, over a third of patients experience primary or secondary therapeutic failure. We developed machine learning models to predict whic...

Utilizing soft constraints to enhance medical relation extraction from the history of present illness in electronic medical records.

Journal of biomedical informatics
Relation extraction between medical concepts from electronic medical records has pervasive applications as well as significance. However, previous researches utilizing machine learning algorithms judge the semantic types of medical concept pair menti...