AIMC Topic: Machine Learning

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Predictive biomarkers for embryotoxicity: a machine learning approach to mitigating multicollinearity in RNA-Seq.

Archives of toxicology
Multicollinearity, characterized by significant co-expression patterns among genes, often occurs in high-throughput expression data, potentially impacting the predictive model's reliability. This study examined multicollinearity among closely related...

MyofAPPcial: Construct validity of a novel technological aid for improving clinical reasoning in the management of myofascial pain syndrome.

European journal of clinical investigation
BACKGROUND: Physiotherapists encounter challenges in diagnosing myofascial trigger points (MTrPs), which are crucial for managing myofascial pain but difficult due to their complex referred pain patterns. We aimed to assess if an interactive software...

Spatial prediction of groundwater salinity in multiple aquifers of the Mekong Delta region using explainable machine learning models.

Water research
Groundwater salinization is a prevalent issue in coastal regions, yet accurately predicting and understanding its causal factors remains challenging due to the complexity of the groundwater system. Therefore, this study predicted groundwater salinity...

A machine learning model for early diagnosis of type 1 Gaucher disease using real-life data.

Journal of clinical epidemiology
OBJECTIVE: The diagnosis of Gaucher disease (GD) presents a major challenge due to the high variability and low specificity of its clinical characteristics, along with limited physician awareness of the disease's early symptoms. Early and accurate di...

Machine learning model identifies patient gait speed throughout the episode of care, generating notifications for clinician evaluation.

Gait & posture
INTRODUCTION: The advent of digital and mobile health innovations, especially use of wearables for passive data collection, allows remote monitoring and creates an abundance of data. For this information to be interpretable, machine learning (ML) pro...

Predicting Basketball Shot Outcome From Visuomotor Control Data Using Explainable Machine Learning.

Journal of sport & exercise psychology
Quiet eye (QE), the visual fixation on a target before initiation of a critical action, is associated with improved performance. While QE is trainable, it is unclear whether QE can directly predict performance, which has implications for training int...

Dose Individualisation of Antimicrobials from a Pharmacometric Standpoint: The Current Landscape.

Drugs
Successful antimicrobial therapy depends on achieving optimal drug concentrations within individual patients. Inter-patient variability in pharmacokinetics (PK) and differences in pathogen susceptibility (reflected in the minimum inhibitory concentra...

Development and validation of a machine learning-based approach to identify high-risk diabetic cardiomyopathy phenotype.

European journal of heart failure
AIMS: Abnormalities in specific echocardiographic parameters and cardiac biomarkers have been reported among individuals with diabetes. However, a comprehensive characterization of diabetic cardiomyopathy (DbCM), a subclinical stage of myocardial abn...