AIMC Topic: Algorithms

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Gender-specific aspects of socialisation and risk of cardiovascular disease among community-dwelling older adults: a prospective cohort study using machine learning algorithms and a conventional method.

Journal of epidemiology and community health
BACKGROUND: Gender influences cardiovascular disease (CVD) through norms, social relations, roles and behaviours. This study identified gender-specific aspects of socialisation associated with CVD.

Teaching deep networks to see shape: Lessons from a simplified visual world.

PLoS computational biology
Deep neural networks have been remarkably successful as models of the primate visual system. One crucial problem is that they fail to account for the strong shape-dependence of primate vision. Whereas humans base their judgements of category membersh...

Improving drug-target interaction prediction through dual-modality fusion with InteractNet.

Journal of bioinformatics and computational biology
In the drug discovery process, accurate prediction of drug-target interactions is crucial to accelerate the development of new drugs. However, existing methods still face many challenges in dealing with complex biomolecular interactions. To this end,...

Enhancing type 2 diabetes mellitus prediction by integrating metabolomics and tree-based boosting approaches.

Frontiers in endocrinology
BACKGROUND: Type 2 diabetes mellitus (T2DM) is a global health problem characterized by insulin resistance and hyperglycemia. Early detection and accurate prediction of T2DM is crucial for effective management and prevention. This study explores the ...

Research on recognition of slippery road surface and collision warning system based on deep learning.

PloS one
Aiming at the problems of slow detection speed, large prediction error and weak environmental adaptability of current vehicle collision warning system, this paper proposes a recognition method of slippery road surface and collision warning system bas...

Exploring machine learning algorithms in sickle cell disease patient data: A systematic review.

PloS one
This systematic review explores the application of machine learning (ML) algorithms in sickle cell disease (SCD), focusing on diagnosis and several clinical characteristics, such as early detection of organ failure, identification of drug dosage, and...

Automated arrhythmia classification based on a pyramid dense connectivity layer and BiLSTM.

Technology and health care : official journal of the European Society for Engineering and Medicine
BackgroundDeep neural networks (DNNs) have recently been significantly applied to automatic arrhythmia classification. However, their classification accuracy still has room for improvement.ObjectivesThe aim of this study is to address the existing li...

Multimodal fish maw type recognition based on Wasserstein generative adversarial network combined with gradient penalty and spectral fusion.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
There are many types of fish maw with significantly varying prices. The specific type directly affects its market value and medicinal efficacy. This paper proposes a fish maw type recognition method based on Wasserstein generative adversarial network...

Weakly supervised label learning flows.

Neural networks : the official journal of the International Neural Network Society
Supervised learning usually requires a large amount of labeled data. However, attaining ground-truth labels is costly for many tasks. Alternatively, weakly supervised methods learn with cheap weak signals that only approximately label some data. Many...

Sociodemographic bias in clinical machine learning models: a scoping review of algorithmic bias instances and mechanisms.

Journal of clinical epidemiology
BACKGROUND AND OBJECTIVES: Clinical machine learning (ML) technologies can sometimes be biased and their use could exacerbate health disparities. The extent to which bias is present, the groups who most frequently experience bias, and the mechanism t...