AIMC Topic: Machine Learning

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Comparing machine learning with case-control models to identify confirmed dengue cases.

PLoS neglected tropical diseases
In recent decades, the global incidence of dengue has increased. Affected countries have responded with more effective surveillance strategies to detect outbreaks early, monitor the trends, and implement prevention and control measures. We have appli...

Metabolomics of Prostate Cancer Gleason Score in Tumor Tissue and Serum.

Molecular cancer research : MCR
Gleason score, a measure of prostate tumor differentiation, is the strongest predictor of lethal prostate cancer at the time of diagnosis. Metabolomic profiling of tumor and of patient serum could identify biomarkers of aggressive disease and lead to...

Assessing the predictive ability of the Suicide Crisis Inventory for near-term suicidal behavior using machine learning approaches.

International journal of methods in psychiatric research
OBJECTIVE: This study explores the prediction of near-term suicidal behavior using machine learning (ML) analyses of the Suicide Crisis Inventory (SCI), which measures the Suicide Crisis Syndrome, a presuicidal mental state.

A Random Forest Machine Learning Framework to Reduce Running Injuries in Young Triathletes.

Sensors (Basel, Switzerland)
BACKGROUND: The running segment of a triathlon produces 70% of the lower limb injuries. Previous research has shown a clear association between kinematic patterns and specific injuries during running.

Machine Learning Improvements to Human Motion Tracking with IMUs.

Sensors (Basel, Switzerland)
Inertial Measurement Units (IMUs) have become a popular solution for tracking human motion. The main problem of using IMU data for deriving the position of different body segments throughout time is related to the accumulation of the errors in the in...

Analysis of Copernicus' ERA5 Climate Reanalysis Data as a Replacement for Weather Station Temperature Measurements in Machine Learning Models for Olive Phenology Phase Prediction.

Sensors (Basel, Switzerland)
Knowledge of phenological events and their variability can help to determine final yield, plan management approach, tackle climate change, and model crop development. THe timing of phenological stages and phases is known to be highly correlated with ...

Cross-site transportability of an explainable artificial intelligence model for acute kidney injury prediction.

Nature communications
Artificial intelligence (AI) has demonstrated promise in predicting acute kidney injury (AKI), however, clinical adoption of these models requires interpretability and transportability. Non-interoperable data across hospitals is a major barrier to mo...

SVFX: a machine learning framework to quantify the pathogenicity of structural variants.

Genome biology
There is a lack of approaches for identifying pathogenic genomic structural variants (SVs) although they play a crucial role in many diseases. We present a mechanism-agnostic machine learning-based workflow, called SVFX, to assign pathogenicity score...

Pathway information extracted from 25 years of pathway figures.

Genome biology
Thousands of pathway diagrams are published each year as static figures inaccessible to computational queries and analyses. Using a combination of machine learning, optical character recognition, and manual curation, we identified 64,643 pathway figu...

Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review.

Journal of medical systems
According to the World Health Organization (WHO) report in 2016, around 800,000 of individuals have committed suicide. Moreover, suicide is the second cause of unnatural death in people between 15 and 29 years. This paper reviews state of the art on ...