AIMC Topic: Humans

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Clinical assessment of the criticality index - dynamic, a machine learning prediction model of future care needs in pediatric inpatients.

PloS one
OBJECTIVE: To assess patient characteristics and care factors that are associated with correct and incorrect predictions of future care locations (ICU vs. non-ICU) by the Criticality Index-Dynamic (CI-D), with the goal of enhancing the CI-D.

Analyzing and predicting global happiness index via integrated multilayer clustering and machine learning models.

PloS one
This study addresses the research objective of predicting global happiness and identifying its key drivers. We propose a novel predictive framework that integrates unsupervised and supervised machine learning techniques to uncover the complex pattern...

Identification and validation of programmed cell death related biomarkers for the treatment and prevention COVID-19.

Annals of medicine
PURPOSE: Programmed cell death (PCD) plays a key role in the progression of coronavirus disease 2019 (COVID-19). However, PCD-relevant biomarkers have not been fully discovered. The aim of this study was to explore the PCD-relevant biomarkers for the...

Integrative Analysis of Lactylation-Associated Features in Abdominal Aortic Aneurysm and Its Immune Microenvironment Utilizing scRNA-seq and Bulk RNA Sequencing.

Circulation journal : official journal of the Japanese Circulation Society
BACKGROUND: Abdominal aortic aneurysm (AAA) is a vascular disease strongly associated with immune dysregulation and metabolic disturbances. Although lactate metabolism and its associated process, lactylation, have been implicated in various diseases,...

Prediction of cardiac differentiation in human induced pluripotent stem cell-derived cardiomyocyte supernatant using surface-enhanced Raman spectroscopy and machine learning.

Biosensors & bioelectronics
The efficient manufacturing of cardiomyocytes from human-induced pluripotent stem cells (hiPSCs) is essential for advancing regenerative therapies for myocardial injuries. However, ensuring cell quality during production is challenging since traditio...

Development and evaluation of a machine learning model to predict acute care for opioid use disorder among Medicaid enrollees engaged in a community-based treatment program.

Addiction (Abingdon, England)
AIMS: To develop machine-learning algorithms for predicting the risk of a hospitalization or emergency department (ED) visit for opioid use disorder (OUD) (i.e. OUD acute events) in Pennsylvania Medicaid enrollees in the Opioid Use Disorder Centers o...

Machine learning and clinical EEG data for multiple sclerosis: A systematic review.

Artificial intelligence in medicine
Multiple Sclerosis (MS) is a chronic neuroinflammatory disease of the Central Nervous System (CNS) in which the body's immune system attacks and destroys the myelin sheath that protects nerve fibers, leading to a wide range of debilitating symptoms a...

Learn to explain transformer via interpretation path by reinforcement learning.

Neural networks : the official journal of the International Neural Network Society
In recent years, the Transformer model has become a key part of many AI systems, making it important to understand how it works. The large parameter size and complex structure of the Transformer make interpretation more difficult and less efficient. ...

An artificial intelligence malnutrition screening tool based on electronic medical records.

Clinical nutrition ESPEN
BACKGROUND & AIMS: Nutrition screening is a fundamental step to ensure appropriate intervention in patients with malnutrition. An automatic tool of nutritional risk screening based on electronic health records will improve efficiency and elevate the ...

The role of AI in emergency department triage: An integrative systematic review.

Intensive & critical care nursing
BACKGROUND: Overcrowding in emergency departments (EDs) leads to delayed treatments, poor patient outcomes, and increased staff workloads. Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools to optimize triage.