AIMC Topic: Humans

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Adaptable graph neural networks design to support generalizability for clinical event prediction.

Journal of biomedical informatics
OBJECTIVE: While many machine learning and deep learning-based models for clinical event prediction leverage various data elements from electronic healthcare records such as patient demographics and billing codes, such models face severe challenges w...

Enhanced heart failure mortality prediction through model-independent hybrid feature selection and explainable machine learning.

Journal of biomedical informatics
Heart failure (HF) remains a significant public health challenge with high mortality rates. Machine learning (ML) techniques offer a promising approach to predict HF mortality, potentially improving clinical outcomes. However, the effectiveness of th...

Managerial myopia and its barrier to green innovation in high-pollution enterprises: A machine learning approach.

Journal of environmental management
Green technology innovation has become a vital remedy in response to the world's growing ecological problems and the urgent need for sustainable development. However, businesses are sometimes discouraged from undertaking such efforts due to the signi...

Identifying the combined impact of human activities and natural factors on China's avian species richness using interpretable machine learning methods.

Journal of environmental management
With human activities-derived escalating climate change and rapid urbanization, avian species face significant survival challenges. Understanding the impact of human activities and environmental drivers on avian species richness is critical for effec...

An Artificial Intelligence Model for Sensing Affective Valence and Arousal from Facial Images.

Sensors (Basel, Switzerland)
Artificial intelligence (AI) models can sense subjective affective states from facial images. Although recent psychological studies have indicated that dimensional affective states of valence and arousal are systematically associated with facial expr...

Stress hyperglycemia ratio and machine learning model for prediction of all-cause mortality in patients undergoing cardiac surgery.

Cardiovascular diabetology
BACKGROUND: The stress hyperglycemia ratio (SHR) was developed to reduce the effects of long-term chronic glycemic factors on stress hyperglycemia levels, which was associated with adverse clinical outcomes. This study aims to evaluate the relationsh...

Development of a diagnostic classification model for lateral cephalograms based on multitask learning.

BMC oral health
OBJECTIVES: This study aimed to develop a cephalometric classification method based on multitask learning for eight diagnostic classifications.

Predicting major adverse cardiac events in diabetes and chronic kidney disease: a machine learning study from the Silesia Diabetes-Heart Project.

Cardiovascular diabetology
BACKGROUND: People living with diabetes mellitus (DM) and chronic kidney disease (CKD) are at significantly high risk of cardiovascular events (CVEs), however the predictive performance of traditional risk prediction methods are limited.

Machine learning-based bulk RNA analysis reveals a prognostic signature of 13 cell death patterns and potential therapeutic target of SMAD3 in acute myeloid leukemia.

BMC cancer
BACKGROUND: Dysregulation or abnormality of the programmed cell death (PCD) pathway is closely related to the occurrence and development of many tumors, including acute myeloid leukemia (AML). Studying the abnormal characteristics of PCD pathway-rela...

Machine learning via DARTS-Optimized MobileViT models for pancreatic Cancer diagnosis with graph-based deep learning.

BMC medical informatics and decision making
The diagnosis of pancreatic cancer presents a significant challenge due to the asymptomatic nature of the disease and the fact that it is frequently detected at an advanced stage. This study presents a novel approach combining graph-based data repres...