AIMC Topic: Humans

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Optimization of spatio-temporal ozone (O) pollution modeling using an ensemble machine model learning with a swarm-based metaheuristic algorithm.

Ecotoxicology and environmental safety
The future of ozone (O) pollution presents significant environmental and public health challenges worldwide. High O levels can harm respiratory health, exacerbating conditions such as asthma and increasing the risk of cardiovascular diseases. Address...

Intersection crash analysis considering longitudinal and lateral risky driving behavior from connected vehicle data: A spatial machine learning approach.

Accident; analysis and prevention
Existing intersection safety analysis studies have primarily focused on macro-level static infrastructure and highly aggregated traffic features. The emergence of Connected Vehicle (CV) has enabled researchers to extract micro-level driving behavior ...

Trauma-predictive brain network connectivity adaptively responds to mild acute stress.

Proceedings of the National Academy of Sciences of the United States of America
Past traumatic experiences shape neural responses to future stress, but the mechanisms underlying this dynamic interaction remain unclear. Here, we assessed how trauma-related brain networks respond to current acute stress in real time. Using a machi...

Machine learning-assisted exploration of multidrug-drug administration regimens for organoid arrays.

Science advances
Combination therapies enhance the therapeutic effect of cancer treatment; however, identifying effective interdependent doses, durations, and sequences of multidrug administration regimens is a time- and labor-intensive task. Here, we integrated mach...

Radiation enteritis associated with temporal sequencing of total neoadjuvant therapy in locally advanced rectal cancer: a preliminary study.

Radiation oncology (London, England)
BACKGROUND: This study aimed to develop and validate a multi-temporal magnetic resonance imaging (MRI)-based delta-radiomics model to accurately predict severe acute radiation enteritis risk in patients undergoing total neoadjuvant therapy (TNT) for ...

Establishment of predictive models for postoperative delirium in elderly patients after knee/hip surgery based on total bilirubin concentration: machine learning algorithms.

BMC anesthesiology
BACKGROUND: With the aging demographic on the rise, we're seeing a spike in the occurrence of postoperative delirium (POD). Our research aims to delve into the connection between plasma bilirubin levels and postoperative delirium, with the goal of cr...

Prediction of postoperative visual cognitive impairment using graph theory and machine learning based on resting-state brain networks.

BMC medical imaging
BACKGROUND: Visual cognitive impairment is among the most common postoperative cognitive dysfunctions, significantly impacting recovery and quality of life in elderly patients. However, effective preoperative prediction methods remain lacking. We dev...

A deep learning model for predicting radiation-induced xerostomia in patients with head and neck cancer based on multi-channel fusion.

BMC medical imaging
OBJECTIVES: Radiation-induced xerostomia is a common sequela in patients who undergo head and neck radiation therapy. This study aims to develop a three-dimensional deep learning model to predict xerostomia by fusing data from the gross tumor volume ...

Skin Microbiome alterations in heroin users revealed by full-length 16S rRNA sequencing.

BMC microbiology
BACKGROUND: Identifying key characteristics of unknown suspects, such as age, height, and drug use, is essential for advancing forensic investigations.

Optimizing treatment for depression in primary care using psychotherapy versus antidepressant medication in a low-resource setting: protocol for the OptimizeD randomized controlled trial.

BMC psychiatry
BACKGROUND: Psychotherapy and antidepressant medications are first-line treatments for depression, and they both have significant treatment effects on average. However, treatment response varies widely across patients, and neither approach is univers...