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A deep learning-based method for assessing tricuspid regurgitation using continuous wave Doppler spectra.

Scientific reports
Transthoracic echocardiography (TTE) is widely recognized as one of the principal modalities for diagnosing tricuspid regurgitation (TR). The diagnostic procedures associated with conventional methods are intricate and labor-intensive, with human err...

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.

Deep-learning models reveal how context and listener attention shape electrophysiological correlates of speech-to-language transformation.

PLoS computational biology
To transform continuous speech into words, the human brain must resolve variability across utterances in intonation, speech rate, volume, accents and so on. A promising approach to explaining this process has been to model electroencephalogram (EEG) ...

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 ...

Explainable machine learning for early prediction of sepsis in traumatic brain injury: A discovery and validation study.

PloS one
BACKGROUND: People with traumatic brain injury (TBI) are at high risk for infection and sepsis. The aim of the study was to develop and validate an explainable machine learning(ML) model based on clinical features for early prediction of the risk of ...

Grade prediction of lesions in cerebral white matter using a convolutional neural network.

PloS one
We established a diagnostic method for cerebral white matter lesions using MRI images and examined the relationship between the MRI images and the medical checkup data. There were approximately 25 MRI images for each patient's head, from the top of t...

Postoperative Karnofsky performance status prediction in patients with IDH wild-type glioblastoma: A multimodal approach integrating clinical and deep imaging features.

PloS one
BACKGROUND AND PURPOSE: Glioblastoma is a highly aggressive brain tumor with limited survival that poses challenges in predicting patient outcomes. The Karnofsky Performance Status (KPS) score is a valuable tool for assessing patient functionality an...

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...

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...

Acoustical features as knee health biomarkers: A critical analysis.

Artificial intelligence in medicine
Acoustical knee health assessment has long promised an alternative to clinically available medical imaging tools, but this modality has yet to be adopted in medical practice. The field is currently led by machine learning models processing acoustical...