AIMC Topic: Humans

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The value of deep learning and radiomics models in predicting preoperative serosal invasion in gastric cancer: a dual-center study.

Abdominal radiology (New York)
PURPOSE: To establish and validate a model based on deep learning (DL), integrating radiomic features with relevant clinical features to generate nomogram, for predicting preoperative serosal invasion in gastric cancer (GC).

Relationship prediction between clinical subtypes and prognosis of critically ill patients with cirrhosis based on unsupervised learning methods: A study from two critical care databases.

International journal of medical informatics
BACKGROUND: Our objective was to identify distinct clinical subtypes among critically ill patients with cirrhosis and analyze the clinical features and prognosis of each subtype.

Utilizing machine learning for predicting mortality in patients with heat-related illness who visited the emergency department.

International journal of medical informatics
BACKGROUND: In the context of climate change and global warming, heat-related illness (HRI) is anticipated to escalate and become a major concern. Patients with severe HRI primarily present to the emergency department (ED), but there are no predictio...

Using longitudinal data and deep learning models to enhance resource allocation in home-based medical care.

International journal of medical informatics
BACKGROUND: The aging population is driving increased healthcare demands and costs, prompting the need for effective home healthcare programs. Accurate patient assessment is essential for optimizing resource allocation and tailoring services.

Artificial intelligence in asthma health literacy: a comparative analysis of ChatGPT versus Gemini.

The Journal of asthma : official journal of the Association for the Care of Asthma
BACKGROUND: Asthma is a complex and heterogeneous chronic disease affecting over 300 million individuals worldwide. Despite advances in pharmacotherapy, poor disease control remains a major challenge, necessitating innovative approaches to patient ed...

Automated detection and recognition of oocyte toxicity by fusion of latent and observable features.

Journal of hazardous materials
Oocyte quality is essential for successful pregnancy, yet no discriminant criterion exists to assess the effects of environmental pollutants on oocyte abnormalities. We developed a stepwise framework integrating deep learning-extracted latent feature...

Generalization and differentiation of affective associative memory circuit based on memristive neural network with emotion transfer.

Neural networks : the official journal of the International Neural Network Society
Most existing research on affective associative memory neural network circuits has predominantly concentrated on reinforcement and extinction, with insufficient attention given to the integration of emotion transfer alongside the principles of genera...

SSSLN:Multivariate Time Series Forecasting via Collaborative Dynamic Graph Learning.

Neural networks : the official journal of the International Neural Network Society
Multivariate time series (MTS) forecasting has achieved notable progress through graph modeling. However, existing approaches often face two key challenges. First, traditional dynamic graph learning (DGL) methods typically maintain dynamic graphs dir...

Big data-driven target identification by machine learning: DRD2 as a therapeutic target for psoriasis.

Journal of dermatological science
BACKGROUND: The development of medical treatments has traditionally relied on researchers leveraging scientific knowledge to hypothesize disease mechanisms and identify therapeutic agents. However, the depletion of novel therapeutic targets has becom...