AIMC Topic: Demography

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Deep Learning Approaches for Glioblastoma Prognosis in Resource-Limited Settings: A Study Using Basic Patient Demographic, Clinical, and Surgical Inputs.

World neurosurgery
BACKGROUND: Glioblastoma (GBM) is the most common brain tumor in the United States, with an annual incidence rate of 3.21 per 100,000. It is the most aggressive type of diffuse glioma and has a median survival of months after treatment. This study ai...

XGBoost-aided prediction of lip prominence based on hard-tissue measurements and demographic characteristics in an Asian population.

American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics
INTRODUCTION: Prediction of lip prominence based on hard-tissue measurements could be helpful in orthodontic treatment planning and has been challenging and formidable thus far.

Diabetes disease detection and classification on Indian demographic and health survey data using machine learning methods.

Diabetes & metabolic syndrome
BACKGROUND & AIM: Diabetes mellitus has become one of the out brakes causing major health issues in developing countries like India. The need for leveraging technology is felt in diabetes management. The main objective of this work is to deploy machi...

Regressing Image Sub-Population Distributions with Deep Learning.

Sensors (Basel, Switzerland)
Regressing the distribution of different sub-populations from a batch of images with learning algorithms is not a trivial task, as models tend to make errors that are unequally distributed across the different sub-populations. Obviously, the baseline...

Predicting demographics from meibography using deep learning.

Scientific reports
This study introduces a deep learning approach to predicting demographic features from meibography images. A total of 689 meibography images with corresponding subject demographic data were used to develop a deep learning model for predicting gland m...

Predicting demographic characteristics from anterior segment OCT images with deep learning: A study protocol.

PloS one
INTRODUCTION: Anterior segment optical coherence tomography (AS-OCT) is a non-contact, rapid, and high-resolution in vivo modality for imaging of the eyeball's anterior segment structures. Because progressive anterior segment deformation is a hallmar...

Prediction of Labor Unemployment Based on Time Series Model and Neural Network Model.

Computational intelligence and neuroscience
With the advent of big data, statistical accounting based on artificial intelligence can realistically reflect the dynamics of labor force and market segmentation. Therefore, based on the combination of machine learning algorithm and traditional stat...

A machine learning analysis of the relationship of demographics and social gathering attendance from 41 countries during pandemic.

Scientific reports
Knowing who to target with certain messages is the prerequisite of efficient public health campaigns during pandemics. Using the COVID-19 pandemic situation, we explored which facets of the society-defined by age, gender, income, and education levels...

Optimizing hepatitis B virus screening in the United States using a simple demographics-based model.

Hepatology (Baltimore, Md.)
BACKGROUND AND AIMS: Chronic hepatitis B (CHB) affects >290 million persons globally, and only 10% have been diagnosed, presenting a severe gap that must be addressed. We developed logistic regression (LR) and machine learning (ML; random forest) mod...

Artificial neural network model effectively estimates muscle and fat mass using simple demographic and anthropometric measures.

Clinical nutrition (Edinburgh, Scotland)
BACKGROUND & AIMS: Lean muscle and fat mass in the human body are important indicators of the risk of cardiovascular and metabolic diseases. Techniques such as dual-energy X-ray absorptiometry (DXA) accurately measure body composition, but they are c...