AIMC Topic: Demography
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Assessing the risk of dengue severity using demographic information and laboratory test results with machine learning.
PLoS neglected tropical diseases
Dec 23, 2020
BACKGROUND: Dengue virus causes a wide spectrum of disease, which ranges from subclinical disease to severe dengue shock syndrome. However, estimating the risk of severe outcomes using clinical presentation or laboratory test results for rapid patien...
Learning Bayesian networks from demographic and health survey data.
Journal of biomedical informatics
Nov 17, 2020
Child mortality from preventable diseases such as pneumonia and diarrhoea in low and middle-income countries remains a serious global challenge. We combine knowledge with available Demographic and Health Survey (DHS) data from India, to construct Cau...
Population demographics in geographic proximity to hospitals with robotic platforms do not correlate with disparities in access to robotic surgery.
Surgical endoscopy
Sep 21, 2020
BACKGROUND: Disparities in access to robotic surgery have been shown on the local, regional, and national level. This study aims to see if the location of hospitals with robotic platforms (HWR) correlates with population trends to explain the dispari...
Predicting breast cancer risk using interacting genetic and demographic factors and machine learning.
Scientific reports
Jul 6, 2020
Breast cancer (BC) is a multifactorial disease and the most common cancer in women worldwide. We describe a machine learning approach to identify a combination of interacting genetic variants (SNPs) and demographic risk factors for BC, especially fac...
Predicting geographic location from genetic variation with deep neural networks.
eLife
Jun 8, 2020
Most organisms are more closely related to nearby than distant members of their species, creating spatial autocorrelations in genetic data. This allows us to predict the location of origin of a genetic sample by comparing it to a set of samples of kn...
Active deep learning to detect demographic traits in free-form clinical notes.
Journal of biomedical informatics
May 16, 2020
The free-form portions of clinical notes are a significant source of information for research, but before they can be used, they must be de-identified to protect patients' privacy. De-identification efforts have focused on known identifier types (nam...
Artificial intelligence-assisted prediction of preeclampsia: Development and external validation of a nationwide health insurance dataset of the BPJS Kesehatan in Indonesia.
EBioMedicine
Apr 10, 2020
BACKGROUND: We developed and validated an artificial intelligence (AI)-assisted prediction of preeclampsia applied to a nationwide health insurance dataset in Indonesia.
Automated detection and classification of diabetes disease based on Bangladesh demographic and health survey data, 2011 using machine learning approach.
Diabetes & metabolic syndrome
Mar 10, 2020
BACKGROUND AND AIMS: Diabetes has been recognized as a continuing health challenge for the twenty-first century, both in developed and developing countries including Bangladesh. The main objective of this study is to use machine learning (ML) based c...
Machine Learning to Predict Stent Restenosis Based on Daily Demographic, Clinical, and Angiographic Characteristics.
The Canadian journal of cardiology
Feb 7, 2020
BACKGROUND: Machine learning (ML) has arrived in medicine to deliver individually adapted medical care. This study sought to use ML to discriminate stent restenosis (SR) compared with existing predictive scores of SR. To develop an easily applicable ...
Demographic, psychological, behavioral, and cognitive correlates of BMI in youth: Findings from the Adolescent Brain Cognitive Development (ABCD) study.
Psychological medicine
Jul 10, 2019
BACKGROUND: Previous research has implicated demographic, psychological, behavioral, and cognitive variables in the onset and maintenance of pediatric overweight/obesity. No adequately-powered study has simultaneously modeled these variables to asses...
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