AIMC Topic: Machine Learning

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The use of machine learning for the prediction of response to follow-up in spine registries.

International journal of medical informatics
BACKGROUND: One of the main challenges in the maintenance of registries is to keep a high follow-up rate and a reliable strategy to limit dropout is currently lacking. Aim of this study was to utilize machine learning (ML) models to highlight the cha...

Deep eutectic solvent-modified polyvinyl alcohol/chitosan thin film membrane for dye adsorption: Machine learning modeling, experimental, and density functional theory calculations.

International journal of biological macromolecules
The polyvinyl alcohol/chitosan (PVA/CS) thin film membrane was modified using a deep eutectic solvent (DES) to enhance its adsorption capability and mechanical strength for the removal of brilliant green (BG) dye. Batch adsorption experiments, machin...

Enhancing lipid identification in LC-HRMS data through machine learning-based retention time prediction.

Journal of chromatography. A
The comprehensive identification of peaks in untargeted lipidomics using LC-MS/MS remains a significant challenge. Confidence in lipid annotation can be greatly improved by integrating a highly accurate machine learning-based retention time predictio...

Developing a simplified measure to predict the risk of autism spectrum disorders: Abbreviating the M-CHAT-R using a machine learning approach in China.

Psychiatry research
BACKGROUND: Early screening for autism spectrum disorder (ASD) is crucial, yet current assessment tools in Chinese primary child care are limited in efficacy.

AQuA-P: A machine learning-based tool for water quality assessment.

Journal of contaminant hydrology
This study addresses the critical challenge of assessing the quality of groundwater and surface water, which are essential resources for various societal needs. The main contribution of this study is the application of machine learning models for eva...

Application of machine learning algorithms in an epidemiologic study of mortality.

Annals of epidemiology
PURPOSE: Epidemiologic studies are important in assessing risk factors of mortality. Machine learning (ML) is efficient in analyzing multidimensional data to unravel dependencies between risk factors and health outcomes.

Evaluating generalizability of oncology trial results to real-world patients using machine learning-based trial emulations.

Nature medicine
Randomized controlled trials (RCTs) evaluating anti-cancer agents often lack generalizability to real-world oncology patients. Although restrictive eligibility criteria contribute to this issue, the role of selection bias related to prognostic risk r...

Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction.

Pharmacotherapy
BACKGROUND: Fluid overload (FO) in the intensive care unit (ICU) is common, serious, and may be preventable. Intravenous medications (including administered volume) are a primary cause for FO but are challenging to evaluate as a FO predictor given th...

Evaluation of Machine Learning/Molecular Mechanics End-State Corrections with Mechanical Embedding to Calculate Relative Protein-Ligand Binding Free Energies.

Journal of chemical theory and computation
The development of machine-learning (ML) potentials offers significant accuracy improvements compared to molecular mechanics (MM) because of the inclusion of quantum-mechanical effects in molecular interactions. However, ML simulations are several ti...

From Spectra to Signatures: Detecting Fentanyl in Human Nails with ATR-FTIR and Machine Learning.

Sensors (Basel, Switzerland)
Human nails have recently become a sample of interest for toxicological purposes. Multiple studies have proven the ability to detect various analytes within the keratin matrix of the nail. The analyte of interest in this study is fentanyl, a highly d...