AIMC Topic: Machine Learning

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The critical role of neutrophil-endothelial cell interactions in sepsis: new synergistic approaches employing organ-on-chip, omics, immune cell phenotyping and modeling to identify new therapeutics.

Frontiers in cellular and infection microbiology
Sepsis is a global health concern accounting for more than 1 in 5 deaths worldwide. Sepsis is now defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Sepsis can develop from bacterial (gram negative or g...

A machine learning model to predict the risk of depression in US adults with obstructive sleep apnea hypopnea syndrome: a cross-sectional study.

Frontiers in public health
OBJECTIVE: Depression is very common and harmful in patients with obstructive sleep apnea hypopnea syndrome (OSAHS). It is necessary to screen OSAHS patients for depression early. However, there are no validated tools to assess the likelihood of depr...

Identification and verification of diagnostic biomarkers based on mitochondria-related genes related to immune microenvironment for preeclampsia using machine learning algorithms.

Frontiers in immunology
Preeclampsia is one of the leading causes of maternal and fetal morbidity and mortality worldwide. Preeclampsia is linked to mitochondrial dysfunction as a contributing factor in its progression. This study aimed to develop a novel diagnostic model b...

Estimating vigilance from the pre-work shift sleep using an under-mattress sleep sensor.

Journal of sleep research
Predicting vigilance impairment in high-risk shift work occupations is critical to help to reduce workplace errors and accidents. Current methods rely on multi-night, often manually entered, sleep data. This study developed a machine learning model f...

HyperPCM: Robust Task-Conditioned Modeling of Drug-Target Interactions.

Journal of chemical information and modeling
A central problem in drug discovery is to identify the interactions between drug-like compounds and protein targets. Over the past few decades, various quantitative structure-activity relationship (QSAR) and proteo-chemometric (PCM) approaches have b...

Exploring White Matter Abnormalities in Young Children with Autism Spectrum Disorder: Integrating Multi-shell Diffusion Data and Machine Learning Analysis.

Academic radiology
RATIONALE AND OBJECTIVES: This study employed tract-based spatial statistics (TBSS) to investigate abnormalities in the white matter microstructure among children with autism spectrum disorder (ASD). Additionally, an eXtreme Gradient Boosting (XGBoos...

Current status of artificial intelligence technologies in pituitary adenoma surgery: a scoping review.

Pituitary
PURPOSE: Pituitary adenoma surgery is a complex procedure due to critical adjacent neurovascular structures, variations in size and extensions of the lesions, and potential hormonal imbalances. The integration of artificial intelligence (AI) and mach...

Application of a human-centered design for embedded machine learning model to develop data labeling software with nurses: Human-to-Artificial Intelligence (H2AI).

International journal of medical informatics
BACKGROUND: Nurses are essential for assessing and managing acute pain in hospitalized patients, especially those who are unable to self-report pain. Given their role and subject matter expertise (SME), nurses are also essential for the design and de...

Using AI/ML to predict blending performance and process sensitivity for Continuous Direct Compression (CDC).

International journal of pharmaceutics
Utilising three artificial intelligence (AI)/machine learning (ML) tools, this study explores the prediction of fill level in inclined linear blenders at steady state by mapping a wide range of bulk powder characteristics to processing parameters. Pr...

Assessing the impact of occlusal plane rotation on facial aesthetics in orthodontic treatment: a machine learning approach.

BMC oral health
BACKGROUND: Adequate occlusal plane (OP) rotation through orthodontic therapy enables satisfying profile improvements for patients who are disturbed by their maxillomandibular imbalance but reluctant to surgery. The study aims to quantify profile imp...