AIMC Topic: Machine Learning

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Investigating Predictors of Cognitive Decline Using Machine Learning.

The journals of gerontology. Series B, Psychological sciences and social sciences
OBJECTIVES: Genetic risks for cognitive decline are not modifiable; however their relative importance compared to modifiable factors is unclear. We used machine learning to evaluate modifiable and genetic risk factors for Alzheimer's disease (AD), to...

Prediction of fatal adverse prognosis in patients with fever-related diseases based on machine learning: A retrospective study.

Chinese medical journal
BACKGROUND: Fever is the most common chief complaint of emergency patients. Early identification of patients at an increasing risk of death may avert adverse outcomes. The aim of this study was to establish an early prediction model of fatal adverse ...

Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain.

Neuro-oncology
BACKGROUND: Conventional MRI cannot be used to identify H3 K27M mutation status. This study aimed to investigate the feasibility of predicting H3 K27M mutation status by applying an automated machine learning (autoML) approach to the MR radiomics fea...

A reference library for assigning protein subcellular localizations by image-based machine learning.

The Journal of cell biology
Confocal micrographs of EGFP fusion proteins localized at key cell organelles in murine and human cells were acquired for use as subcellular localization landmarks. For each of the respective 789,011 and 523,319 optically validated cell images, morph...

Machine learning reveals serum sphingolipids as cholesterol-independent biomarkers of coronary artery disease.

The Journal of clinical investigation
BACKGROUNDCeramides are sphingolipids that play causative roles in diabetes and heart disease, with their serum levels measured clinically as biomarkers of cardiovascular disease (CVD).METHODSWe performed targeted lipidomics on serum samples from ind...

MLCD: A Unified Software Package for Cancer Diagnosis.

JCO clinical cancer informatics
PURPOSE: Machine Learning Package for Cancer Diagnosis (MLCD) is the result of a National Institutes of Health/National Cancer Institute (NIH/NCI)-sponsored project for developing a unified software package from state-of-the-art breast cancer biopsy ...

Machine Learning and Mechanistic Modeling for Prediction of Metastatic Relapse in Early-Stage Breast Cancer.

JCO clinical cancer informatics
PURPOSE: For patients with early-stage breast cancer, predicting the risk of metastatic relapse is of crucial importance. Existing predictive models rely on agnostic survival analysis statistical tools (eg, Cox regression). Here we define and evaluat...

The ways of using machine learning in dentistry.

Advances in clinical and experimental medicine : official organ Wroclaw Medical University
Innovative computer techniques are starting to be employed not only in academic research, but also in commercial production, finding use in many areas of dentistry. This is conducive to the digitalization of dentistry and its increasing treatment and...

Machine Learning Applications in Endocrinology and Metabolism Research: An Overview.

Endocrinology and metabolism (Seoul, Korea)
Machine learning (ML) applications have received extensive attention in endocrinology research during the last decade. This review summarizes the basic concepts of ML and certain research topics in endocrinology and metabolism where ML principles hav...

Super learner analysis of real-time electronically monitored adherence to antiretroviral therapy under constrained optimization and comparison to non-differentiated care approaches for persons living with HIV in rural Uganda.

Journal of the International AIDS Society
INTRODUCTION: Real-time electronic adherence monitoring (EAM) systems could inform on-going risk assessment for HIV viraemia and be used to personalize viral load testing schedules. We evaluated the potential of real-time EAM (transferred via cellula...