AIMC Topic: Machine Learning

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Personalized Models for Injected Activity Levels in SPECT Myocardial Perfusion Imaging.

IEEE transactions on medical imaging
We propose a patient-specific ("personalized") approach for tailoring the injected activities to individual patients in order to achieve dose reduction in SPECT-myocardial perfusion imaging (MPI). First, we develop a strategy to determine the minimum...

Coronary CT angiography-derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia.

European radiology
OBJECTIVES: We sought to investigate the diagnostic performance of coronary CT angiography (cCTA)-derived plaque markers combined with deep machine learning-based fractional flow reserve (CT-FFR) to identify lesion-specific ischemia using invasive FF...

Machine learning for identification of surgeries with high risks of cancellation.

Health informatics journal
Surgery cancellations waste scarce operative resources and hinder patients' access to operative services. In this study, the Wilcoxon and chi-square tests were used for predictor selection, and three machine learning models - random forest, support v...

Exploring the interactions between serum free fatty acids and fecal microbiota in obesity through a machine learning algorithm.

Food research international (Ottawa, Ont.)
Serum free fatty acids (FFA) are generally elevated in obesity. The gut microbiota is involved in the host energy metabolism through the regulation of body fat storage, and a link between diet, FFA and the intestinal microbiota seems to exist. Our ai...

Vertebral body insufficiency fractures: detection of vertebrae at risk on standard CT images using texture analysis and machine learning.

European radiology
PURPOSE: To evaluate the diagnostic performance of bone texture analysis (TA) combined with machine learning (ML) algorithms in standard CT scans to identify patients with vertebrae at risk for insufficiency fractures.

Calibration and validation of accelerometer-based activity monitors: A systematic review of machine-learning approaches.

Gait & posture
BACKGROUND: Objective measures using accelerometer-based activity monitors have been extensively used in physical activity (PA) and sedentary behavior (SB) research. To measure PA and SB precisely, the field is shifting towards machine learning-based...

Machine-Learning-Based Cyclic Voltammetry Behavior Model for Supercapacitance of Co-Doped Ceria/rGO Nanocomposite.

Journal of chemical information and modeling
This paper examines the cobalt-doped ceria/reduced graphene oxide (Co-CeO/rGO) nanocomposite as a supercapacitor and modeling of its cyclic voltammetry behavior using Artificial Neural Network (ANN) and Random Forest Algorithm (RFA). Good agreement w...

Deep Convolutional Neural Networks for the Prediction of Molecular Properties: Challenges and Opportunities Connected to the Data.

Journal of integrative bioinformatics
We present a flexible deep convolutional neural network method for the analysis of arbitrary sized graph structures representing molecules. This method, which makes use of the Lipinski RDKit module, an open-source cheminformatics software, enables th...

Identifying Cases of Metastatic Prostate Cancer Using Machine Learning on Electronic Health Records.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Cancer stage is rarely captured in structured form in the electronic health record (EHR). We evaluate the performance of a classifier, trained on structured EHR data, in identifying prostate cancer patients with metastatic disease. Using EHR data for...

Using Neural Multi-task Learning to Extract Substance Abuse Information from Clinical Notes.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Substance abuse carries many negative health consequences. Detailed information about patients' substance abuse history is usually captured in free-text clinical notes. Automatic extraction of substance abuse information is vital to assess patients' ...