AIMC Topic: Machine Learning

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Machine-learning models for predicting surgical site infections using patient pre-operative risk and surgical procedure factors.

American journal of infection control
BACKGROUND: Surgical site infections (SSIs) are a significant health care problem as they can cause increased medical costs and increased morbidity and mortality. Assessing a patient's preoperative risk factors can improve risk stratification and hel...

Integrating model explanations and hybrid priors into deep stacked networks for the "safe zone" prediction of acetabular cup.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: Existing state-of-the-art "safe zone" prediction methods are statistics-based methods, image-matching techniques, and machine learning methods. Yet, those methods bring a tension between accuracy and interpretability.

Myocardial Function Prediction After Coronary Artery Bypass Grafting Using MRI Radiomic Features and Machine Learning Algorithms.

Journal of digital imaging
The main aim of the present study was to predict myocardial function improvement in cardiac MR (LGE-CMR) images in patients after coronary artery bypass grafting (CABG) using radiomics and machine learning algorithms. Altogether, 43 patients who had ...

Automatic detection of three cell types in a microscope image based on deep learning.

Journal of biophotonics
With the continuous integration of deep learning and the technique of molecular biology, target detection models must accurately detect the position of each cell in the image and classify it correctly. We present a model for the multi-scale feature f...

Challenges and opportunities associated with rare-variant pharmacogenomics.

Trends in pharmacological sciences
Recent advances in next-generation sequencing (NGS) have resulted in the identification of tens of thousands of rare pharmacogenetic variations with unknown functional effects. However, although such pharmacogenetic variations have been estimated to ...

Machine learning approach identifies prominent codons from different degenerate groups influencing gene expression in bacteria.

Genes to cells : devoted to molecular & cellular mechanisms
Unequal usage of synonymous codons is known as codon usage bias (CUB), which is generally different between the high-expression genes (HEG) and low-expression genes (LEG) in organisms is not yet adequately reported across different bacteria. In this ...

Predicting Total Drug Clearance and Volumes of Distribution Using the Machine Learning-Mediated Multimodal Method through the Imputation of Various Nonclinical Data.

Journal of chemical information and modeling
Pharmacokinetic research plays an important role in the development of new drugs. Accurate predictions of human pharmacokinetic parameters are essential for the success of clinical trials. Clearance (CL) and volume of distribution (Vd) are important ...

A Cloud Enabled Crop Recommendation Platform for Machine Learning-Driven Precision Farming.

Sensors (Basel, Switzerland)
Modern agriculture incorporated a portfolio of technologies to meet the current demand for agricultural food production, in terms of both quality and quantity. In this technology-driven farming era, this portfolio of technologies has aided farmers to...

Automatic Stones Classification through a CNN-Based Approach.

Sensors (Basel, Switzerland)
This paper presents an automatic recognition system for classifying stones belonging to different Calabrian quarries (Southern Italy). The tool for stone recognition has been developed in the SILPI project (acronym of ""), financed by POR Calabria FE...

Contactless Fall Detection by Means of Multiple Bioradars and Transfer Learning.

Sensors (Basel, Switzerland)
Fall detection in humans is critical in the prevention of life-threatening conditions. This is especially important for elderly people who are living alone. Therefore, automatic fall detection is one of the most relevant problems in geriatrics. Biora...