AIMC Topic: Adult

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Real-time interactive artificial intelligence of things-based prediction for adverse outcomes in adult patients with pneumonia in the emergency department.

Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
BACKGROUND: Artificial intelligence of things (AIoT) may be a solution for predicting adverse outcomes in emergency department (ED) patients with pneumonia; however, this issue remains unclear. Therefore, we conducted this study to clarify it.

Automated segmentation of deep brain nuclei using convolutional neural networks and susceptibility weighted imaging.

Human brain mapping
The advent of susceptibility-sensitive MRI techniques, such as susceptibility weighted imaging (SWI), has enabled accurate in vivo visualization and quantification of iron deposition within the human brain. Although previous approaches have been intr...

Improving the predictive potential of diffusion MRI in schizophrenia using normative models-Towards subject-level classification.

Human brain mapping
Diffusion MRI studies consistently report group differences in white matter between individuals diagnosed with schizophrenia and healthy controls. Nevertheless, the abnormalities found at the group-level are often not observed at the individual level...

Early detection of COVID-19 in the UK using self-reported symptoms: a large-scale, prospective, epidemiological surveillance study.

The Lancet. Digital health
BACKGROUND: Self-reported symptoms during the COVID-19 pandemic have been used to train artificial intelligence models to identify possible infection foci. To date, these models have only considered the culmination or peak of symptoms, which is not s...

Machine learning to predict distal caries in mandibular second molars associated with impacted third molars.

Scientific reports
Impacted mandibular third molars (M3M) are associated with the occurrence of distal caries on the adjacent mandibular second molars (DCM2M). In this study, we aimed to develop and validate five machine learning (ML) models designed to predict the occ...

Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach.

PloS one
Interpersonal violence (IPV) is highly prevalent in the United States and is a major public health problem. The emergence and/or worsening of chronic pain are known sequelae of IPV; however, not all those who experience IPV develop chronic pain. To m...

Neural and computational mechanisms of momentary fatigue and persistence in effort-based choice.

Nature communications
From a gym workout, to deciding whether to persevere at work, many activities require us to persist in deciding that rewards are 'worth the effort' even as we become fatigued. However, studies examining effort-based decisions typically assume that th...

What's in a trauma? Using machine learning to unpack what makes an event traumatic.

Journal of affective disorders
What differentiates a trauma from an event that is merely upsetting? Wildly different definitions of trauma have been used in both formal (psychiatric) and informal (cultural, colloquial) settings. Yet there is a dearth of empirical work examining th...

A dual-channel language decoding from brain activity with progressive transfer training.

Human brain mapping
When we view a scene, the visual cortex extracts and processes visual information in the scene through various kinds of neural activities. Previous studies have decoded the neural activity into single/multiple semantic category tags which can caption...

A Self-Representation-Based Fuzzy SVM Model for Predicting Vascular Calcification of Hemodialysis Patients.

Computational and mathematical methods in medicine
In end-stage renal disease (ESRD), vascular calcification risk factors are essential for the survival of hemodialysis patients. To effectively assess the level of vascular calcification, the machine learning algorithm can be used to predict the vascu...