AIMC Topic: Machine Learning

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Predictive modeling based on machine learning for mapping risk areas of human sporotrichosis in southeastern Brazil.

Research in veterinary science
Sporotrichosis, a zoonotic mycosis with a growing public health impact, requires innovative methods to map risk areas. This study applied machine learning techniques, Artificial Neural Networks (ANN), and Decision Trees (DT) to integrate sociodemogra...

An informed machine learning based environmental risk score for hypertension in European adults.

Artificial intelligence in medicine
BACKGROUND: The exposome framework seeks to unravel the cumulated effects of environmental exposures on health. However, existing methods struggle with challenges including multicollinearity, non-linearity and confounding. To address these limitation...

Machine learning approaches for fine-grained symptom estimation in schizophrenia: A comprehensive review.

Artificial intelligence in medicine
Schizophrenia is a severe yet treatable mental disorder, and it is diagnosed using a multitude of primary and secondary symptoms. Diagnosis and treatment for each individual depends on the severity of the symptoms. Therefore, there is a need for accu...

A comprehensive review of ICU readmission prediction models: From statistical methods to deep learning approaches.

Artificial intelligence in medicine
The prediction of Intensive Care Unit (ICU) readmission has become a crucial area of research due to the increasing demand for ICU resources and the need to provide timely interventions to critically ill patients. In recent years, several studies hav...

External validation of a proprietary risk model for 1-year mortality in community-dwelling adults aged 65 years or older.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To examine the discrimination, calibration, and algorithmic fairness of the Epic End of Life Care Index (EOL-CI).

Discover important donor-recipient risk factors and interactions in heart transplant primary graft dysfunction with machine learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Primary graft dysfunction (PGD) is an essential outcome after the heart transplant, which causes severe complications and symptoms for recipients. The in advance prediction of PGD can help the transplant physician better manage the risks ...

Healing with hierarchy: Hierarchical attention empowered graph neural networks for predictive analysis in medical data.

Artificial intelligence in medicine
In healthcare, predictive analysis using unstructured medical data is crucial for gaining insights into patient conditions and outcomes. However, unstructured data, which contains valuable patient information such as symptoms and medical histories, o...

Spatio-temporal crash severity analysis with cost-sensitive multi-graphs attention network.

Accident; analysis and prevention
Most conventional crash severity models attempt to achieve a low classification error rate, implicitly assuming the same losses for all classification errors. In this paper, we suggest that this setting has limitations in terms of reasonableness, as ...

Interpretable machine learning and graph attention network based model for predicting PAMPA permeability.

Journal of molecular graphics & modelling
Parallel artificial membrane permeability assay (PAMPA) is widely used in the early phases of drug discovery as it is quite robust and offers high throughput. It serves as a platform for assessing the permeability and absorption of pharmaceutical com...