AIMC Topic: Machine Learning

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The Prediction of Body Mass Index from Negative Affectivity through Machine Learning: A Confirmatory Study.

Sensors (Basel, Switzerland)
This study investigates on the relationship between affect-related psychological variables and Body Mass Index (BMI). We have utilized a novel method based on machine learning (ML) algorithms that forecast unobserved BMI values based on psychological...

Graph of graphs analysis for multiplexed data with application to imaging mass cytometry.

PLoS computational biology
Imaging Mass Cytometry (IMC) combines laser ablation and mass spectrometry to quantitate metal-conjugated primary antibodies incubated in intact tumor tissue slides. This strategy allows spatially-resolved multiplexing of dozens of simultaneous prote...

Using machine learning approaches for multi-omics data analysis: A review.

Biotechnology advances
With the development of modern high-throughput omic measurement platforms, it has become essential for biomedical studies to undertake an integrative (combined) approach to fully utilise these data to gain insights into biological systems. Data from ...

Machine learning for identifying relevant publications in updates of systematic reviews of diagnostic test studies.

Research synthesis methods
Updating systematic reviews is often a time-consuming process that involves a lot of human effort and is therefore not conducted as often as it should be. The aim of our research project was to explore the potential of machine learning methods to red...

Introduction to The Spine Journal special issue on artificial intelligence and machine learning.

The spine journal : official journal of the North American Spine Society
In the last 5 years, artificial intelligence (AI) algorithms have made rapid advances for diagnosis and prognosis in fields ranging from dermatology to anesthesiology. How do we make sense of the rise of AI in healthcare and specifically in spine? Ho...

Transfer learning in deep neural network-based receiver coil sensitivity map estimation.

Magma (New York, N.Y.)
INTRODUCTION: The success of parallel Magnetic Resonance Imaging algorithms like SENSitivity Encoding (SENSE) depends on an accurate estimation of the receiver coil sensitivity maps. Deep learning-based receiver coil sensitivity map estimation depend...

Predicting Length of Stay of Coronary Artery Bypass Grafting Patients Using Machine Learning.

The Journal of surgical research
BACKGROUND: There is a growing need to identify which bits of information are most valuable for healthcare providers. The aim of this study was to search for the highest impact variables in predicting postsurgery length of stay (LOS) for patients who...

Prediction modeling of potentially toxic elements' hydrogeopollution using an integrated Q-mode HCs and ANNs machine learning approach in SE Nigeria.

Environmental science and pollution research international
Machine learning techniques have proven to be very useful in environmental and engineering assessments, including water quality studies. This is because they have flexible linear and nonlinear forecasting functions that can efficiently and reliably e...

Determining suitable machine learning classifier technique for prediction of malaria incidents attributed to climate of Odisha.

International journal of environmental health research
This study investigated the influence of climate factors on malaria incidence in the Sundargarh district, Odisha, India. The WEKA machine learning tool was used with two classifier techniques, Multi-Layer Perceptron (MLP) and J48, with three test opt...

Predicting postoperative opioid use with machine learning and insurance claims in opioid-naïve patients.

American journal of surgery
BACKGROUND: The clinical impact of postoperative opioid use requires accurate prediction strategies to identify at-risk patients. We utilize preoperative claims data to predict postoperative opioid refill and new persistent use in opioid-naïve patien...