AIMC Topic: Machine Learning

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Henry gas solubility optimization double machine learning classifier for neurosurgical patients.

PloS one
This study aims to predict head trauma outcome for Neurosurgical patients in children, adults, and elderly people. As Machine Learning (ML) algorithms are helpful in healthcare field, a comparative study of various ML techniques is developed. Several...

Machine learning-based approach for efficient prediction of toxicity of chemical gases using feature selection.

Journal of hazardous materials
Toxic gases can be fatal as they damage many living tissues, especially the nervous and respiratory systems. They can cause permanent damage for many years by harming environmental tissue and living organisms. They can also cause mass deaths when use...

Confidence-based laboratory test reduction recommendation algorithm.

BMC medical informatics and decision making
BACKGROUND: We propose a new deep learning model to identify unnecessary hemoglobin (Hgb) tests for patients admitted to the hospital, which can help reduce health risks and healthcare costs.

Judging facts, judging norms: Training machine learning models to judge humans requires a modified approach to labeling data.

Science advances
As governments and industry turn to increased use of automated decision systems, it becomes essential to consider how closely such systems can reproduce human judgment. We identify a core potential failure, finding that annotators label objects diffe...

MD-GNN: A mechanism-data-driven graph neural network for molecular properties prediction and new material discovery.

Journal of molecular graphics & modelling
Molecular properties prediction and new material discovery are significant for the pharmaceutical industry, food, chemistry, and other fields. The popular methods are theoretical mechanism calculation and machine learning. There is a deviation betwee...

Machine-learning predictive model of pregnancy-induced hypertension in the first trimester.

Hypertension research : official journal of the Japanese Society of Hypertension
In the first trimester of pregnancy, accurately predicting the occurrence of pregnancy-induced hypertension (PIH) is important for both identifying high-risk women and adopting early intervention. In this study, we used four machine-learning models (...

Protein model quality assessment using rotation-equivariant transformations on point clouds.

Proteins
Machine learning research concerning protein structure has seen a surge in popularity over the last years with promising advances for basic science and drug discovery. Working with macromolecular structure in a machine learning context requires an ad...

Machine and deep learning in inflammatory bowel disease.

Current opinion in gastroenterology
PURPOSE OF REVIEW: The Management of inflammatory bowel disease (IBD) has evolved with the introduction and widespread adoption of biologic agents; however, the advent of artificial intelligence technologies like machine learning and deep learning pr...

Neural network and spline-based regression for the prediction of salivary hypofunction in patients undergoing radiation therapy.

Radiation oncology (London, England)
BACKGROUND: This study leverages a large retrospective cohort of head and neck cancer patients in order to develop machine learning models to predict radiation induced hyposalivation from dose-volume histograms of the parotid glands.

In silico prediction of hERG blockers using machine learning and deep learning approaches.

Journal of applied toxicology : JAT
The human ether-à-go-go-related gene (hERG) is associated with drug cardiotoxicity. If the hERG channel is blocked, it will lead to prolonged QT interval and cause sudden death in severe cases. Therefore, it is important to evaluate the hERG-blocking...