AIMC Topic: Machine Learning

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Unveiling drug-induced osteotoxicity: A machine learning approach and webserver.

Journal of hazardous materials
Drug-induced osteotoxicity refers to the harmful effects certain pharmaceuticals have on the skeletal system, posing significant safety risks. These toxic effects are critical concerns in clinical practice, drug development, and environmental managem...

Identification of key feature variables and prediction of harmful algal blooms in a water diversion lake based on interpretable machine learning.

Environmental research
Harmful algal blooms (HABs) as an increasing environmental problem in lakes, and water diversion has become a common and effective strategy for mitigating HABs. Early and accurate identification of the occurrence of HABs in lakes is essential for sci...

Exploring the Incremental Value of Aorta Enhancement Normalization Method in Evaluating Renal Cell Carcinoma Histological Subtypes: A Multi-center Large Cohort Study.

Academic radiology
RATIONALE AND OBJECTIVES: The classification of renal cell carcinoma (RCC) histological subtypes plays a crucial role in clinical diagnosis. However, traditional image normalization methods often struggle with discrepancies arising from differences i...

Machine Learning-Assisted Health Economics and Policy Reviews: A Comparative Assessment.

Applied health economics and health policy
INTRODUCTION: The growth of scientific literature in health economics and policy represents a challenge for researchers conducting literature reviews. This study explores the adoption of a machine learning (ML) tool to enhance title and abstract scre...

Development and Multi-center validation of a machine learning Model for advanced colorectal neoplasms screening.

Computers in biology and medicine
BACKGROUND: In colorectal cancer (CRC) screening programs, accurately identifying individuals at high risk for advanced colorectal neoplasia (ACN) is essential as they require further colonoscopy, early intervention, and monitoring follow-up. This st...

Machine learning models for predicting metabolic dysfunction-associated steatotic liver disease prevalence using basic demographic and clinical characteristics.

Journal of translational medicine
BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a global health concern that necessitates early screening and timely intervention to improve prognosis. The current diagnostic protocols for MASLD involve complex procedu...

Predicting changes of incisor and facial profile following orthodontic treatment: a machine learning approach.

Head & face medicine
BACKGROUND: Facial aesthetics is one of major motivations for seeking orthodontic treatment. However, even for experienced professionals, the impact and extent of incisor and soft tissue changes remain largely empirical. With the application of inter...

Developing a high-performance AI model for spontaneous intracerebral hemorrhage mortality prediction using machine learning in ICU settings.

BMC medical informatics and decision making
BACKGROUND: Spontaneous intracerebral hemorrhage (SICH) is a devastating condition that significantly contributes to high mortality rates. This study aims to construct a mortality prediction model for patients with SICH using four various artificial ...

Development and evaluation of a machine learning model for osteoporosis risk prediction in Korean women.

BMC women's health
BACKGROUND: The aim of this study was to develop a machine learning (ML) model for classifying osteoporosis in Korean women based on a large-scale population cohort study. This study also aimed to assess ML model performance compared with traditional...

Machine learning in lymphocyte and immune biomarker analysis for childhood thyroid diseases in China.

BMC pediatrics
OBJECTIVE: This study aims to characterize and analyze the expression of representative biomarkers like lymphocytes and immune subsets in children with thyroid disorders. It also intends to develop and evaluate a machine learning model to predict if ...