AIMC Topic: Machine Learning

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Development of a machine learning-based risk assessment model for loneliness among elderly Chinese: a cross-sectional study based on Chinese longitudinal healthy longevity survey.

BMC geriatrics
BACKGROUND: Loneliness is prevalent among the elderly and has intensified due to global aging trends. It adversely affects both mental and physical health. Traditional scales for measuring loneliness may yield biased results due to varying definition...

A new approach for sex prediction by evaluating mandibular arch and canine dimensions with machine-learning classifiers and intraoral scanners (a retrospective study).

Scientific reports
In circumstances where antemortem information concerning the deceased individual is unavailable, forensic experts prepare biological profiling for unidentified human remains that aids in narrowing the search for identity. Biological profiling include...

Prediction of acute respiratory infections using machine learning techniques in Amhara Region, Ethiopia.

Scientific reports
Many studies have shown that infectious diseases are responsible for the majority of deaths in children under five. Among these children, Acute Respiratory Infections is the most prevalent illness and cause of death worldwide. Acute respiratory infec...

Machine learning algorithms for prediction of measles one vaccination dropout among 12-23 months children in Ethiopia.

BMJ open
INTRODUCTION: Despite the availability of a safe and effective measles vaccine in Ethiopia, the country has experienced recurrent and significant measles outbreaks, with a nearly fivefold increase in confirmed cases from 2021 to 2023. The WHO has ide...

An antimicrobial drug recommender system using MALDI-TOF MS and dual-branch neural networks.

eLife
Timely and effective use of antimicrobial drugs can improve patient outcomes, as well as help safeguard against resistance development. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is currently routinely...

Predictive models for secondary epilepsy in patients with acute ischemic stroke within one year.

eLife
BACKGROUND: Post-stroke epilepsy (PSE) is a critical complication that worsens both prognosis and quality of life in patients with ischemic stroke. An interpretable machine learning model was developed to predict PSE using medical records from four h...

Predicting stroke severity of patients using interpretable machine learning algorithms.

European journal of medical research
BACKGROUND: Stroke is a significant global health concern, ranking as the second leading cause of death and placing a substantial financial burden on healthcare systems, particularly in low- and middle-income countries. Timely evaluation of stroke se...

An experimental analysis of graph representation learning for Gene Ontology based protein function prediction.

PeerJ
Understanding protein function is crucial for deciphering biological systems and facilitating various biomedical applications. Computational methods for predicting Gene Ontology functions of proteins emerged in the 2000s to bridge the gap between the...

Prediction model and technical and tactical decision analysis of women's badminton singles based on machine learning.

PloS one
In the Paris Olympic cycle, South Korean women's athlete An Se-young rose to the top of the 2023 BWF Olympic points with a win rate of 89.5%. With An Se-young as the subject, this paper aims to carry out technical and tactical analysis of women's bad...