AIMC Topic: Humans

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An Evaluation of the Efficacy of Machine Learning in Predicting Thyrotoxicosis and Hypothyroidism: A Comparative Assessment of Biochemical Test Parameters Used in Different Health Checkups.

Internal medicine (Tokyo, Japan)
Objective This study assessed the efficacy of machine learning in predicting thyrotoxicosis and hypothyroidism [thyroid-stimulating hormone >10.0 mIU/L] by leveraging age and sex as variables and integrating biochemical test parameters used by the Ja...

Cystic renal mass screening: machine-learning-based radiomics on unenhanced computed tomography.

Diagnostic and interventional radiology (Ankara, Turkey)
PURPOSE: The present study compares the diagnostic performance of unenhanced computed tomography (CT) radiomics-based machine learning (ML) classifiers and a radiologist in cystic renal masses (CRMs).

Enhancing safety of construction workers in Korea: an integrated text mining and machine learning framework for predicting accident types.

International journal of injury control and safety promotion
Construction workers face a high risk of various occupational accidents, many of which can result in fatalities. This study aims to develop a prediction model for nine prevalent types of construction accidents, utilizing construction tasks, activitie...

Applying data science methodologies with artificial intelligence variant reinterpretation to map and estimate genetic disorder prevalence utilizing clinical data.

American journal of medical genetics. Part A
Data science methodologies can be utilized to ascertain and analyze clinical genetic data that is often unstructured and rarely used outside of patient encounters. Genetic variants from all genetic testing resulting to a large pediatric healthcare sy...

Detection of Chylous Plasma Based on Machine Learning and Hyperspectral Techniques.

Applied spectroscopy
Chylous blood is the main cause of unqualified and scrapped blood among volunteer blood donors. Therefore, a diagnostic method that can quickly and accurately identify chylous blood before donation is needed. In this study, the GaiaSorter "Gaia" hype...

ML-FGAT: Identification of multi-label protein subcellular localization by interpretable graph attention networks and feature-generative adversarial networks.

Computers in biology and medicine
The prediction of multi-label protein subcellular localization (SCL) is a pivotal area in bioinformatics research. Recent advancements in protein structure research have facilitated the application of graph neural networks. This paper introduces a no...

Antimalarial Drug Combination Predictions Using the Machine Learning Synergy Predictor (MLSyPred©) tool.

Acta parasitologica
PURPOSE: Antimalarial drug resistance is a global public health problem that leads to treatment failure. Synergistic drug combinations can improve treatment outcomes and delay the development of drug resistance. Here, we describe the implementation o...