AIMC Topic: Humans

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Cognitive process and information processing model based on deep learning algorithms.

Neural networks : the official journal of the International Neural Network Society
According to the developmental process of infants, cognitive abilities are divided into four stages: the Exploration Stage (ES), the Mapping Stage (MS), the Phenomena-causality Stage (PCS), and the Essence-causality Stage (ECS). The MS is a training ...

Effectively saltiness enhanced odorants screening and prediction by database establish, sensory evaluation and deep learning method.

Food chemistry
Odor-taste interaction has gained success in enhancing saltiness perception. This work aimed to provide candidate odorants for saltiness enhancement. Volatile compounds and their frequencies in salty foods were systematically analyzed. The compounds ...

Predicting high-flow arteriovenous fistulas and cardiac outcomes in hemodialysis patients.

Journal of vascular surgery
BACKGROUND: Heart failure is common in patients receiving hemodialysis. A high-flow arteriovenous fistula (AVF) may represent a modifiable risk factor for heart failure and death. Currently, no tools exist to assess the risk of developing a high-flow...

Predicting chemotherapy responsiveness in gastric cancer through machine learning analysis of genome, immune, and neutrophil signatures.

Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
BACKGROUND: Gastric cancer is a major oncological challenge, ranking highly among causes of cancer-related mortality worldwide. This study was initiated to address the variability in patient responses to combination chemotherapy, highlighting the nee...

Self-supervised neural network for Patlak-based parametric imaging in dynamic [F]FDG total-body PET.

European journal of nuclear medicine and molecular imaging
PURPOSE: The objective of this study is to generate reliable K parametric images from a shortened [F]FDG total-body PET for clinical applications using a self-supervised neural network algorithm.

Assessing the efficacy of artificial intelligence to provide peri-operative information for patients with a stoma.

ANZ journal of surgery
BACKGROUND: Stomas present significant lifestyle and psychological challenges for patients, requiring comprehensive education and support. Current educational methods have limitations in offering relevant information to the patient, highlighting a po...

Multitask learning for automatic detection of meniscal injury on 3D knee MRI.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society
Magnetic resonance imaging (MRI) of the knee is the recommended diagnostic method before invasive arthroscopy surgery. Nevertheless, interpreting knee MRI scans is a time-consuming process that is vulnerable to inaccuracies and inconsistencies. We pr...

Artificial Intelligence-driven and technological innovations in the diagnosis and management of substance use disorders.

International review of psychiatry (Abingdon, England)
Substance Use Disorders (SUD) lead to a collection of health challenges such as overdoses and clinical diseases. Populations that are vulnerable and lack straightforward treatment access are vulnerable to significant economic and social effects linke...

Ontology-based integration and querying of heterogeneous rare disease data sources - POLVAS perspective.

Computers in biology and medicine
The integration of rare disease medical databases belonging to different countries is an important problem, as a large number of observations are required for reliable statistical inference of patient data in order to facilitate clinical research. Su...

Interpretable prediction of drug-drug interactions via text embedding in biomedical literature.

Computers in biology and medicine
Polypharmacy is a promising approach for treating diseases, especially those with complex symptoms. However, it can lead to unexpected drug-drug interactions (DDIs), potentially reducing efficacy and triggering adverse drug reactions (ADRs). Predicti...