AIMC Topic: Demography

Clear Filters Showing 41 to 50 of 78 articles

Using natural language processing and machine learning to classify health literacy from secure messages: The ECLIPPSE study.

PloS one Feb 22, 2019
Limited health literacy is a barrier to optimal healthcare delivery and outcomes. Current measures requiring patients to self-report limitations are time-consuming and may be considered intrusive by some. This makes widespread classification of patie...
Electronic Mail California Computer Security Demography Health Literacy Humans Machine Learning Female Male Physician-Patient Relations Data Mining Physicians, Primary Care Diabetes Mellitus Natural Language Processing
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Deep Learning Algorithms with Demographic Information Help to Detect Tuberculosis in Chest Radiographs in Annual Workers' Health Examination Data.

International journal of environmental research and public health Jan 16, 2019
We aimed to use deep learning to detect tuberculosis in chest radiographs in annual workers' health examination data and compare the performances of convolutional neural networks (CNNs) based on images only (I-CNN) and CNNs including demographic vari...
Humans Female Adult Sensitivity and Specificity Radiography Tuberculosis, Pulmonary Occupational Health Demography Physical Examination Male Middle Aged Deep Learning ROC Curve
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Predominant polarity classification and associated clinical variables in bipolar disorder: A machine learning approach.

Journal of affective disorders Nov 6, 2018
BACKGROUND: Bipolar disorder (BD) is a severe psychiatric disorder characterized by periodic episodes of manic and depressive symptomatology. Predominant polarity (PP) appears to be an important specifier of BD. The present study employed machine lea...
Humans Machine Learning Algorithms Female Middle Aged Bipolar Disorder Cohort Studies ROC Curve Age Factors Hospitalization Adult Cross-Sectional Studies Male Demography Feeding and Eating Disorders Hallucinations Delusions Substance-Related Disorders Area Under Curve Alcoholism Tobacco Use Disorder
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JOURNAL CLUB: Use of Gradient Boosting Machine Learning to Predict Patient Outcome in Acute Ischemic Stroke on the Basis of Imaging, Demographic, and Clinical Information.

AJR. American journal of roentgenology Oct 24, 2018
OBJECTIVE: When treatment decisions are being made for patients with acute ischemic stroke, timely and accurate outcome prediction plays an important role. The optimal rehabilitation strategy also relies on long-term outcome predictions. The decision...
Stroke Brain Ischemia Computed Tomography Angiography Demography Biomarkers Prognosis Retrospective Studies Tomography, X-Ray Computed Middle Aged Decision Trees Predictive Value of Tests Female Male Humans Machine Learning Algorithms
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Predictive models for charitable giving using machine learning techniques.

PloS one Oct 3, 2018
Private giving represents more than three fourths of all U.S. charitable donations, about 2% of total Gross Domestic Product (GDP). Private giving is a significant factor in funding the nonprofit sector of the U.S. economy, which accounts for more th...
Demography Socioeconomic Factors Charities Models, Economic Data Accuracy Linear Models Humans Neural Networks, Computer Machine Learning United States
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Demographic and Symptomatic Features of Voice Disorders and Their Potential Application in Classification Using Machine Learning Algorithms.

Folia phoniatrica et logopaedica : official organ of the International Association of Logopedics and Phoniatrics (IALP) Sep 5, 2018
BACKGROUND: Studies have used questionnaires of dysphonic symptoms to screen voice disorders. This study investigated whether the differential presentation of demographic and symptomatic features can be applied to computerized classification.
Supervised Machine Learning Sex Factors Smoking Alcohol Drinking Male Middle Aged Severity of Illness Index Age Factors Voice Disorders Laryngeal Neoplasms Voice Quality Glottis Symptom Assessment Wounds and Injuries Demography Vocal Cord Paralysis Humans Neural Networks, Computer Female Aged Adult Algorithms Retrospective Studies
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Residential scene classification for gridded population sampling in developing countries using deep convolutional neural networks on satellite imagery.

International journal of health geographics May 9, 2018
BACKGROUND: Conducting surveys in low- and middle-income countries is often challenging because many areas lack a complete sampling frame, have outdated census information, or have limited data available for designing and selecting a representative s...
Guatemala Developing Countries Data Collection Demography Satellite Imagery Neural Networks, Computer Nigeria Residence Characteristics Humans
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Spatial extreme learning machines: An application on prediction of disease counts.

Statistical methods in medical research Apr 9, 2018
Extreme learning machines have gained a lot of attention by the machine learning community because of its interesting properties and computational advantages. With the increase in collection of information nowadays, many sources of data have missing ...
Incidence Lung Neoplasms Bayes Theorem Monte Carlo Method Demography Machine Learning Computer Simulation Brazil HIV Infections Humans
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Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI.

EBioMedicine Mar 23, 2018
BACKGROUND: A lack of a sufficiently large sample at single sites causes poor generalizability in automatic diagnosis classification of heterogeneous psychiatric disorders such as schizophrenia based on brain imaging scans. Advanced deep learning met...
Neural Pathways Demography Support Vector Machine Magnetic Resonance Imaging Discriminant Analysis Schizophrenia Machine Learning Female Adult Male Humans
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Robust Machine Learning Variable Importance Analyses of Medical Conditions for Health Care Spending.

Health services research Mar 11, 2018
OBJECTIVE: To propose nonparametric double robust machine learning in variable importance analyses of medical conditions for health spending.
Humans Machine Learning Male United States Chronic Disease Databases, Factual Female Health Expenditures Risk Adjustment Demography
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