AIMC Topic: Humans

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Stock market forecasting research based on GA-WOA-LSTM.

PloS one
With the increasing complexity and prosperity of global financial markets, stock market forecasting plays a critical role in investment decision-making, market regulation, and economic planning. This study proposes a hybrid prediction model that inte...

Early embryo development: the current perspective in molecular evaluation and clinical status.

Systems biology in reproductive medicine
Early embryo development and competence mechanisms are paramount to ART's success but are still underexplored in human-relevant animal models. Clinical embryo evaluation remains largely based on subjectively evaluated morphological characteristics. I...

Predicting prognosis of patients with hepatitis B virus-related acute-on-chronic liver failure from longitudinal ultrasound images using a multi-task deep learning approach.

Annals of medicine
BACKGROUND: Individualized risk stratification in hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) remains challenging. This study aimed to develop and validate a multi-task deep learning model using longitudinal liver ultrasound i...

A Deep Learning Approach for Tracking Colorectal Cancer-Derived Extracellular Vesicles in Colon and Lung Models.

ACS biomaterials science & engineering
According to the International Agency for Research on Cancer and the World Health Organization, colorectal cancer (CRC) is the third most common cancer in the world and the main cause of gastrointestinal cancer-related deaths. Despite advances in the...

Enhancing Toxicity Prediction of Synthetic Chemicals via Novel SMILES Fragmentation and Interpretable Deep Learning.

Journal of chemical information and modeling
Toxicity prediction and identification of structural alerts (SAs) for synthetic chemicals are critical for assessing risks to environmental and human health. Traditional methods, which rely heavily on molecular descriptors, often suffer from poor int...

Predicting children and adolescents at high risk of poor health‑related quality of life using machine learning methods.

Health and quality of life outcomes
BACKGROUND: Existing research has identified health‑related quality of life (HRQoL) is influenced by a multitude of factors among children and adolescents. However, there has been relatively limited exploration of the multidimensional predictive fact...

Machine Learning-Driven radiomics on 18 F-FDG PET for glioma diagnosis: a systematic review and meta-analysis.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: Machine learning (ML) applied to radiomics has revolutionized neuro-oncological imaging, yet the diagnostic performance of ML models based specifically on ^18F-FDG PET features in glioma remains poorly characterized.

Explainable machine learning identifies key quality-of-life-related predictors of arthritis status: evidence from the China health and retirement longitudinal study.

Health and quality of life outcomes
BACKGROUND: Arthritis is a prevalent chronic disease substantially impacting patients' quality of life (QoL). While identifying key determinants associated with arthritis is critical for targeted interventions, traditional statistical methods often s...

Exploring the potential relationship between kidney disease index and cognitive dysfunction: a machine learning approach with NHANES data.

BMC geriatrics
OBJECTIVE: This study investigates the relationship between the Kidney Disease Index (KDI) and cognitive function, evaluating its potential as a predictive marker for cognitive impairment in older adults. We also compare the performance of KDI with t...