AIMC Topic: Adult

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Trajectory on postpartum depression of Chinese women and the risk prediction models: A machine-learning based three-wave follow-up research.

Journal of affective disorders
BACKGROUND: Our study delves into postpartum depression (PPD) extending observation up to six months postpartum, addressing the gap in long-term follow-ups and uncover critical intervention points.

Pseudo-random Number Generator Influences on Average Treatment Effect Estimates Obtained with Machine Learning.

Epidemiology (Cambridge, Mass.)
BACKGROUND: The use of machine learning to estimate exposure effects introduces a dependence between the results of an empirical study and the value of the seed used to fix the pseudo-random number generator.

Altered dynamic large-scale brain networks and combined machine learning in primary angle-closure glaucoma.

Neuroscience
Primary angle-closure glaucoma (PACG) is a severe and irreversible blinding eye disease characterized by progressive retinal ganglion cell death. However, prior research has predominantly focused on static brain activity changes, neglecting the explo...

Comparing the effectiveness of robotic plantarflexion resistance and biofeedback between overground and treadmill walking.

Journal of biomechanics
Individuals with diminished walking performance caused by neuromuscular impairments often lack plantar flexion muscle activity. Robotic devices have been developed to address these issues and increase walking performance. While these devices have sho...

Machine Learning Recognizes Frequency-Following Responses in American Adults: Effects of Reference Spectrogram and Stimulus Token.

Perceptual and motor skills
Electrophysiological research has been widely utilized to study brain responses to acoustic stimuli. The frequency-following response (FFR), a non-invasive reflection of how the brain encodes acoustic stimuli, is a particularly propitious electrophys...

Comparison of the Accuracy of Ground Reaction Force Component Estimation between Supervised Machine Learning and Deep Learning Methods Using Pressure Insoles.

Sensors (Basel, Switzerland)
The three Ground Reaction Force (GRF) components can be estimated using pressure insole sensors. In this paper, we compare the accuracy of estimating GRF components for both feet using six methods: three Deep Learning (DL) methods (Artificial Neural ...

Multi-class segmentation of temporomandibular joint using ensemble deep learning.

Scientific reports
Temporomandibular joint disorders are prevalent causes of orofacial discomfort. Diagnosis predominantly relies on assessing the configuration and positions of temporomandibular joint components in magnetic resonance images. The complex anatomy of the...

Prediction of sepsis mortality in ICU patients using machine learning methods.

BMC medical informatics and decision making
PROBLEM: Sepsis, a life-threatening condition, accounts for the deaths of millions of people worldwide. Accurate prediction of sepsis outcomes is crucial for effective treatment and management. Previous studies have utilized machine learning for prog...

Deep learning improves quality of intracranial vessel wall MRI for better characterization of potentially culprit plaques.

Scientific reports
Intracranial vessel wall imaging (VWI), which requires both high spatial resolution and high signal-to-noise ratio (SNR), is an ideal candidate for deep learning (DL)-based image quality improvement. Conventional VWI (Conv-VWI, voxel size 0.51 × 0.51...

Predictive Study of Machine Learning-Based Multiparametric MRI Radiomics Nomogram for Perineural Invasion in Rectal Cancer: A Pilot Study.

Journal of imaging informatics in medicine
This study aimed to establish and validate the efficacy of a nomogram model, synthesized through the integration of multi-parametric magnetic resonance radiomics and clinical risk factors, for forecasting perineural invasion in rectal cancer. We retr...