Preventive Care

LIT-PCBA: An Unbiased Data Set for Machine Learning and Virtual Screening.

Comparative evaluation of virtual screening methods requires a rigorous benchmarking procedure on diverse, realistic, and unbiased data sets. Recent investigations from numerous research groups unambiguously demonstrate that artificially constructed ligand sets classically used by the community (e.g., DUD, DUD-E, MUV) are unfortunately biased by both obvious and hidden chemical biases, therefore o...

How Artificial Intelligence Will Impact Colonoscopy and Colorectal Screening.

Artificial intelligence may improve value in colonoscopy-based colorectal screening and surveillance by improving quality and decreasing unnecessary costs. The quality of screening and surveillance as measured by adenoma detection rates can be improved through real-time computer-assisted detection of polyps. Unnecessary costs can be decreased with optical biopsies to identify low-risk polyps using...

Predicting the short-term success of human influenza virus variants with machine learning.

Seasonal influenza viruses are constantly changing and produce a different set of circulating strains each season. Small genetic changes can accumulat...

Deep learning-based radiomic features for improving neoadjuvant chemoradiation response prediction in locally advanced rectal cancer.

Radiomic features achieve promising results in cancer diagnosis, treatment response prediction, and survival prediction. Our goal is to compare the ha...

Machine learning for screening prioritization in systematic reviews: comparative performance of Abstrackr and EPPI-Reviewer.

BACKGROUND: Improving the speed of systematic review (SR) development is key to supporting evidence-based medicine. Machine learning tools which semi-...

Prediction of the Vaccine-derived Poliovirus Outbreak Incidence: A Hybrid Machine Learning Approach.

Recently, significant attention has been devoted to vaccine-derived poliovirus (VDPV) surveillance due to its severe consequences. Prediction of the o...

Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data.

Cell Population Data (CPD) provides various blood cell parameters that can be used for differential diagnosis. Data analytics using Machine Learning (...

Natural Language Processing for Mimicking Clinical Trial Recruitment in Critical Care: A Semi-Automated Simulation Based on the LeoPARDS Trial.

Clinical trials often fail to recruit an adequate number of appropriate patients. Identifying eligible trial participants is resource-intensive when r...

Multicontext multitask learning networks for mass detection in mammogram.

PURPOSE: In this paper, for the purpose of accurate and efficient mass detection, we propose a new deep learning framework, including two major stages...

Comparing Deep Learning Models for Multi-cell Classification in Liquid- based Cervical Cytology Image.

Liquid-based cytology (LBC) is a reliable automated technique for the screening of Papanicolaou (Pap) smear data. It is an effective technique for col...

Deep learning uncertainty and confidence calibration for the five-class polyp classification from colonoscopy.

There are two challenges associated with the interpretability of deep learning models in medical image analysis applications that need to be addressed...

Discovery of Small-Molecule Activators for Glucose-6-Phosphate Dehydrogenase (G6PD) Using Machine Learning Approaches.

Glucose-6-Phosphate Dehydrogenase (G6PD) is a ubiquitous cytoplasmic enzyme converting glucose-6-phosphate into 6-phosphogluconate in the pentose phos...

A comprehensive study on the multi-class cervical cancer diagnostic prediction on pap smear images using a fusion-based decision from ensemble deep convolutional neural network.

The diagnosis of cervical dysplasia, carcinoma in situ and confirmed carcinoma cases is more easily perceived by commercially available and current re...

Automatic opportunistic osteoporosis screening using low-dose chest computed tomography scans obtained for lung cancer screening.

OBJECTIVE: Osteoporosis is a prevalent and treatable condition, but it remains underdiagnosed. In this study, a deep learning-based system was develop...

Applying Machine Learning to Ultrafast Shape Recognition in Ligand-Based Virtual Screening.

Ultrafast Shape Recognition (USR), along with its derivatives, are Ligand-Based Virtual Screening (LBVS) methods that condense 3-dimensional informati...

Monitoring stance towards vaccination in twitter messages.

BACKGROUND: We developed a system to automatically classify stance towards vaccination in Twitter messages, with a focus on messages with a negative s...

Inconsistent Performance of Deep Learning Models on Mammogram Classification.

OBJECTIVES: Performance of recently developed deep learning models for image classification surpasses that of radiologists. However, there are questio...

A machine learning approach for handling big data produced by high resolution mass spectrometry after data independent acquisition of small molecules - Proof of concept study using an artificial neural network for sample classification.

Liquid chromatography coupled to high-resolution mass spectrometry (HRMS) enables data independent acquisition (DIA) and untargeted screening. However...

The application of artificial intelligence (AI) techniques to identify frailty within a residential aged care administrative data set.

INTRODUCTION: Research has shown that frailty, a geriatric syndrome associated with an increased risk of negative outcomes for older people, is highly...

Untargeted Metabolomics for Metabolic Diagnostic Screening with Automated Data Interpretation Using a Knowledge-Based Algorithm.

Untargeted metabolomics may become a standard approach to address diagnostic requests, but, at present, data interpretation is very labor-intensive. T...