AIMC Topic: Machine Learning

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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.

Integrated structural proteomics and machine learning-guided mapping of a highly protective precision vaccine against mycoplasma pulmonis.

International immunopharmacology
Mycoplasma pulmonis (M. pulmonis) is an emerging respiratory infection commonly linked to prostate cancer, and it is classified under the group of mycoplasmas. Improved management of mycoplasma infections is essential due to the frequent ineffectiven...

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.

Don't Let Your Analysis Go to Seed: On the Impact of Random Seed on Machine Learning-based Causal Inference.

Epidemiology (Cambridge, Mass.)
Machine learning techniques for causal effect estimation can enhance the reliability of epidemiologic analyses, reducing their dependence on correct model specifications. However, the stochastic nature of many machine learning algorithms implies that...

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...

Predicting tremor improvement after MRgFUS thalamotomy in essential tremor from preoperative spontaneous brain activity: A machine learning approach.

Science bulletin
Magnetic resonance-guided focused ultrasound surgery (MRgFUS) thalamotomy is an emerging technique for medication-refractory essential tremor (ET), but with variable outcomes. This study used pattern regression analysis to identify brain signatures p...

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...

Predicting tissue distribution and tumor delivery of nanoparticles in mice using machine learning models.

Journal of controlled release : official journal of the Controlled Release Society
Nanoparticles (NPs) can be designed for targeted delivery in cancer nanomedicine, but the challenge is a low delivery efficiency (DE) to the tumor site. Understanding the impact of NPs' physicochemical properties on target tissue distribution and tum...

Using Data-Driven Algorithms with Large-Scale Plasma Proteomic Data to Discover Novel Biomarkers for Diagnosing Depression.

Journal of proteome research
Given recent technological advances in proteomics, it is now possible to quantify plasma proteomes in large cohorts of patients to screen for biomarkers and to guide the early diagnosis and treatment of depression. Here we used CatBoost machine learn...

Accurate PM urban air pollution forecasting using multivariate ensemble learning Accounting for evolving target distributions.

Chemosphere
Over the past decades, air pollution has caused severe environmental and public health problems. According to the World Health Organization (WHO), fine particulate matter (PM), a key component reflecting air quality, is the fourth leading cause of de...