AIMC Topic: Machine Learning

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Dermacen analytica: A novel methodology integrating multi-modal large language models with machine learning in dermatology.

International journal of medical informatics
OBJECTIVE: To design, implement, evaluate, and quantify a novel and adaptable Artificial Intelligence-empowered methodology aimed at supporting a dermatologist's workflow in assessing and diagnosing skin conditions, leveraging AI's deep image analyti...

Associations of dietary patterns with serum 25(OH) vitamin D and serum anemia related biomarkers among expectant mothers: A machine learning based approach.

International journal of medical informatics
BACKGROUND: Machine learning algorithms (MLA) gained prominence in nutritional epidemiology for analyzing dietary associations and uncovering intricate patterns within data. We explored dietary patterns associated with serum iron biomarkers and vitam...

From classical approaches to artificial intelligence, old and new tools for PDAC risk stratification and prediction.

Seminars in cancer biology
Pancreatic ductal adenocarcinoma (PDAC) is recognized as one of the most lethal malignancies, characterized by late-stage diagnosis and limited therapeutic options. Risk stratification has traditionally been performed using epidemiological studies an...

Development and validation of a convenient dementia risk prediction tool for diabetic population: A large and longitudinal machine learning cohort study.

Journal of affective disorders
BACKGROUND: Diabetes mellitus has been shown to increase the risk of dementia, with diabetic patients demonstrating twice the dementia incidence rate of non-diabetic populations. We aimed to develop and validate a novel machine learning-based dementi...

Comparison of machine learning and logistic regression models for predicting emergence delirium in elderly patients: A prospective study.

International journal of medical informatics
OBJECTIVE: To compare the performance of machine learning and logistic regression algorithms in predicting emergence delirium (ED) in elderly patients.

Promoting lignocellulosic biorefinery by machine learning: progress, perspectives and challenges.

Bioresource technology
The lignocellulosic biorefinery involves pretreatment, enzymatic hydrolysis, mixed sugar fermentation, and optional anaerobic digestion. This pipeline could be effectively implemented through machine learning (ML)-guided process optimization and stra...

Construction and verification of risk prediction model for suicidal attempts of mood disorder based on machine learning.

Journal of affective disorders
BACKGROUND: Mood disorders (MD) are closely related to suicide attempt (SA). Developing an effective prediction model for SA in MD patients could play a crucial role in the early identification of high-risk groups.

Single-shot, receptor-free, rapid detection and classification of five respiratory viruses by machine learning integrated SERS sensing platform.

Biosensors & bioelectronics
A machine learning integrated SERS sensing platform is developed for a single-shot, receptor-free, rapid detection and classification of five respiratory viruses including Influenza A, Respiratory Syncytial Virus (RSV), Human Rhinoviruses (Rhino), an...

Development and validation of explainable machine learning models for female hip osteoporosis using electronic health records.

International journal of medical informatics
BACKGROUND: Hip fractures are associated with reduced mobility, and higher morbidity, mortality, and healthcare costs. Approximately 90% of hip fractures in the elderly are associated with osteoporosis, making it particularly important to screen the ...

Adaptive node-level weighted learning for directed graph neural network.

Neural networks : the official journal of the International Neural Network Society
Directed graph neural networks (DGNNs) have garnered increasing interest, yet few studies have focused on node-level representation in directed graphs. In this paper, we argue that different nodes rely on neighbor information from different direction...