AIMC Topic: Machine Learning

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Machine learning of whole-brain resting-state fMRI signatures for individualized grading of frontal gliomas.

Cancer imaging : the official publication of the International Cancer Imaging Society
PURPOSE: Accurate preoperative grading of gliomas is critical for therapeutic planning and prognostic evaluation. We developed a noninvasive machine learning model leveraging whole-brain resting-state functional magnetic resonance imaging (rs-fMRI) b...

Machine learning algorithms for risk factor selection with application to 60-day sepsis morbidity risk for a geriatric hip fracture cohort.

BMC geriatrics
BACKGROUND: Sepsis after hip fracture in elderly people is a risk factor for mortality. The purpose of this study was to screen for risk factors for 60-day sepsis morbidity after hip fracture and to establish a predictive model using various machine ...

Open-radiomics: a collection of standardized datasets and a technical protocol for reproducible radiomics machine learning pipelines.

BMC medical imaging
BACKGROUND: As an important branch of machine learning pipelines in medical imaging, radiomics faces two major challenges namely reproducibility and accessibility. In this work, we introduce open-radiomics, a set of radiomics datasets along with a co...

Diagnostic systematic review and meta-analysis of machine learning in predicting biochemical recurrence of prostate cancer.

Scientific reports
Prostate cancer (PCa) is the most prevalent malignant tumor in males, and many patients remain at risk of biochemical recurrence (BCR) following initial treatment. Accurate prediction of BCR is vital for effective clinical management and treatment pl...

Unraveling global malaria incidence and mortality using machine learning and artificial intelligence-driven spatial analysis.

Scientific reports
Malaria remains a significant global health concern, contributing to substantial morbidity and mortality worldwide. To inform efforts aimed at alleviating the global malaria burden, this study utilized spatial analysis, advanced machine learning (ML)...

Learning behavior aware features across spaces for improved 3D human motion prediction.

Scientific reports
3D skeleton-based human motion prediction is an essential and challenging task for human-machine interactions, aiming to forecast future poses given a history of previous motions. However, most existing works model human motion dependencies exclusive...

Unsupervised machine learning approach to interpret complex lower urinary tract symptoms and their impact on quality of life in adult women.

World journal of urology
PURPOSE: To identify clinically meaningful clusters of lower urinary tract symptoms (LUTS) in adult women using an unsupervised machine learning approach and to examine their associations with patient-centered outcomes, including quality of life (QoL...

Prediction of the risk of transplant rejection based on RNA sequencing data of PBMCs before transplantation.

Scientific reports
Novel methods for detecting transplant rejection are craved, since conventional methods can detect ongoing rejection that may sometimes have already caused irreversible damage in transplanted organs. Here, we applied a transcriptomics database of rec...

varCADD: large sets of standing genetic variation enable genome-wide pathogenicity prediction.

Genome medicine
BACKGROUND: Machine learning and artificial intelligence are increasingly being applied to identify phenotypically causal genetic variation. These data-driven methods require comprehensive training sets to deliver reliable results. However, large unb...

A multimodal dataset for precision oncology in head and neck cancer.

Nature communications
Head and neck cancer is a common disease and is associated with a poor prognosis. A promising approach to improving patient outcomes is personalized treatment, which uses information from a variety of modalities. However, only little progress has bee...