AIMC Topic: Machine Learning

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Selecting measures of visual function to classify diabetic retinopathy status: a cross-sectional study.

BMJ open ophthalmology
AIM: To identify combinations of up to three visual function tests with the best performance for classifying diabetic retinopathy (DR) severity stage. To describe in detail the measurements from a comprehensive set of visual function tests. METHODS: ...

First trimester prediction of gestational diabetes mellitus by machine learning in twin pregnancies.

Archives of gynecology and obstetrics
INTRODUCTION: We aimed to develop a machine learning model for first-trimester prediction of gestational diabetes mellitus (GDM) in twin pregnancies using a prospective international, multi-center cohort and identify useful predictive markers. METHOD...

Reassessing feature-based Android malware detection in a contemporary context.

PloS one
We report the findings of a reimplementation of 18 foundational studies in feature-based machine learning for Android malware detection, published during the period 2013-2023. These studies are reevaluated on a level playing field using a contemporar...

Insights into long-acting reversible contraceptive practices in Sub-Saharan Africa: A machine learning perspective.

PloS one
INTRODUCTION: Long-acting reversible contraceptives (LARCs) are critical for reducing maternal mortality and unintended pregnancies, yet adoption remains low in Sub-Saharan Africa (SSA) due to systemic inequities, cultural barriers, and fragmented he...

Cell death-related gene signatures as dual-function biomarkers: Early diagnosis and therapeutic targeting in Staphylococcus aureus pneumonia.

PloS one
BACKGROUND: Staphylococcus aureus (S. aureus) pneumonia constitutes a lethal respiratory infection with persistently high clinical mortality. Although programmed cell death (PCD) pathways are implicated in diverse disease processes, their mechanistic...

Efficient AI-driven allegation screening: A case study of Thailand's National Anti-Corruption Commission.

PloS one
Efficient screening of corruption allegations is crucial for promoting accountability and transparency in public administration. However, many institutions still rely on manual processes that are prone to inefficiency and inconsistency. As AI gains t...

Prediction of myeloid malignant cells in Fanconi anemia using machine learning.

PloS one
Fanconi anemia (FA) is an inherited bone marrow failure syndrome with cancer predisposition. Most FA patients develop aplastic anemia during childhood and have an extremely high cumulative risk to develop cancer during their lifespan. Myeloid maligna...

Machine Learning Prediction Models for Preeclampsia: Systematic Review and Meta-Analysis.

Journal of medical Internet research
BACKGROUND: Preeclampsia is a severe hypertensive disorder with rising global prevalence. While machine learning (ML) models for predicting preeclampsia are increasingly published, existing evidence shows high heterogeneity, and the distinction betwe...

Decoding Non-Neuronal Mechanisms and Therapeutic Targets in Huntington's Disease Through Integrative Transcriptomics and Machine Learning.

Journal of molecular neuroscience : MN
Huntington's disease (HD) is a rare, inherited neurodegenerative disorder caused by the expanded CAG repeats in the huntingtin gene. The HD domain still lacks detailed knowledge of validated drug targets, limiting the effectiveness of classical metho...

Machine learning-based risk modeling for safety-focused learning curve assessment in robotic left-sided colorectal cancer surgery.

Journal of robotic surgery
The transition from laparoscopic to robotic surgery for left-sided colorectal cancer raises safety concerns during the learning curve, particularly when complex cases are preferentially selected for the robotic platform. We evaluated a machine learni...