AIMC Topic: Humans

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Prediction of cognitive impairment among Medicare beneficiaries using a machine learning approach.

Archives of gerontology and geriatrics
OBJECTIVE: Developing machine learning (ML) models to predict cognitive impairment among Medicare beneficiaries in the United States.

Risk factors for depression in China based on machine learning algorithms: A cross-sectional survey of 264,557 non-manual workers.

Journal of affective disorders
BACKGROUND: Factors related to depression differ depending on the population studied, and studies focusing on the population of non-manual workers are lacking. Thus, we aimed to identify the risk factors related to depression in non-manual workers in...

GFANC-RL: Reinforcement Learning-based Generative Fixed-filter Active Noise Control.

Neural networks : the official journal of the International Neural Network Society
The recent Generative Fixed-filter Active Noise Control (GFANC) method achieves a good trade-off between noise reduction performance and system stability. However, labelling noise data for training the Convolutional Neural Network (CNN) in GFANC is t...

Accuracy assessment of ChatGPT responses to frequently asked questions regarding anterior cruciate ligament surgery.

The Knee
BACKGROUND: The emergence of artificial intelligence (AI) has allowed users to have access to large sources of information in a chat-like manner. Thereby, we sought to evaluate ChatGPT-4 response's accuracy to the 10 patient most frequently asked que...

Houston, We Have AI Problem! Quality Issues with Neuroimaging-Based Artificial Intelligence in Parkinson's Disease: A Systematic Review.

Movement disorders : official journal of the Movement Disorder Society
In recent years, many neuroimaging studies have applied artificial intelligence (AI) to facilitate existing challenges in Parkinson's disease (PD) diagnosis, prognosis, and intervention. The aim of this systematic review was to provide an overview of...

Assessing the Performance of Machine Learning Methods Trained on Public Health Observational Data: A Case Study From COVID-19.

Statistics in medicine
From early in the coronavirus disease 2019 (COVID-19) pandemic, there was interest in using machine learning methods to predict COVID-19 infection status based on vocal audio signals, for example, cough recordings. However, early studies had limitati...

Predicting cannabis use moderation among a sample of digital self-help subscribers: A machine learning study.

Drug and alcohol dependence
BACKGROUND: For individuals who wish to reduce their cannabis use without formal help, there are a variety of self-help tools available. Although some are proven to be effective in reducing cannabis use, effect sizes are typically small. More insight...

Prediction of anhedonia in patients with first-episode schizophrenia using a Wavelet-ALFF-based Support vector regression model.

Neuroscience
Anhedonia is one of the core features of the negative symptoms of schizophrenia and can be extremely burdensome. Our study applied resting-state functional magnetic resonance imaging (fMRI)-based support vector regression (SVR) to predict anhedonia i...

Multi-omics Analysis to Identify Key Immune Genes for Osteoporosis based on Machine Learning and Single-cell Analysis.

Orthopaedic surgery
OBJECTIVE: Osteoporosis is a severe bone disease with a complex pathogenesis involving various immune processes. With the in-depth understanding of bone immune mechanisms, discovering new therapeutic targets is crucial for the prevention and treatmen...

Integrating Laser-Induced Breakdown Spectroscopy and Ensemble Learning as Minimally Invasive Optical Screening for Diabetes.

Applied spectroscopy
Diabetes mellitus is a prevalent chronic disease necessitating timely identification for effective management. This paper introduces a reliable, straightforward, and efficient method for the minimally invasive identification of diabetes mellitus thro...