AIMC Topic: Age Factors

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Brain age prediction: A comparison between machine learning models using region- and voxel-based morphometric data.

Human brain mapping Mar 19, 2021
Brain morphology varies across the ageing trajectory and the prediction of a person's age using brain features can aid the detection of abnormalities in the ageing process. Existing studies on such "brain age prediction" vary widely in terms of their...
Humans Machine Learning Brain Female Aged Male Middle Aged Support Vector Machine Age Factors Magnetic Resonance Imaging Regression Analysis Neuroimaging
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Machine Learning for Prediction and Risk Stratification of Lupus Nephritis Renal Flare.

American journal of nephrology Mar 19, 2021
BACKGROUND: Renal flare of lupus nephritis (LN) is strongly associated with poor kidney outcomes, and predicting renal flare and stratifying its risk are important for clinical decision-making and individualized management to reduce LN flare.
Lupus Nephritis Proportional Hazards Models Complement C3 Kaplan-Meier Estimate Serum Albumin Recurrence Symptom Flare Up Capillaries Antibodies, Antinuclear Young Adult Humans Adult Machine Learning Male Clinical Decision-Making Risk Assessment Female Age Factors Risk Factors Models, Statistical
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Predicting Age Groups of Reddit Users Based on Posting Behavior and Metadata: Classification Model Development and Validation.

JMIR public health and surveillance Mar 16, 2021
BACKGROUND: Social media are important for monitoring perceptions of public health issues and for educating target audiences about health; however, limited information about the demographics of social media users makes it challenging to identify conv...
Humans Machine Learning Algorithms Reproducibility of Results Young Adult Adult Models, Psychological Middle Aged Adolescent Age Factors Social Media Metadata
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Machine learning models to identify low adherence to influenza vaccination among Korean adults with cardiovascular disease.

BMC cardiovascular disorders Mar 9, 2021
BACKGROUND: Annual influenza vaccination is an important public health measure to prevent influenza infections and is strongly recommended for cardiovascular disease (CVD) patients, especially in the current coronavirus disease 2019 (COVID-19) pandem...
Nutrition Surveys Support Vector Machine Age Factors Influenza, Human Influenza Vaccines Risk Factors Aged Young Adult Adult Cross-Sectional Studies Male Cardiovascular Diseases Middle Aged Risk Assessment Medication Adherence Vaccination Humans Databases, Factual Republic of Korea Female
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Individualized prediction of COVID-19 adverse outcomes with MLHO.

Scientific reports Mar 5, 2021
The COVID-19 pandemic has devastated the world with health and economic wreckage. Precise estimates of adverse outcomes from COVID-19 could have led to better allocation of healthcare resources and more efficient targeted preventive measures, includi...
Risk Assessment Hospitalization Intensive Care Units Respiration, Artificial Aged Humans ROC Curve Aged, 80 and over Machine Learning Electronic Health Records Age Factors Adult Pandemics Female Models, Statistical Male COVID-19 Reproducibility of Results Data Mining Middle Aged Prognosis SARS-CoV-2 Retrospective Studies Risk Factors
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Machine Learning Assessment of Early Life Factors Predicting Suicide Attempt in Adolescence or Young Adulthood.

JAMA network open Mar 1, 2021
IMPORTANCE: Although longitudinal studies have reported associations between early life factors (ie, in-utero/perinatal/infancy) and long-term suicidal behavior, they have concentrated on 1 or few selected factors, and established associations, but d...
Young Adult Male Infant Suicide, Attempted Adolescent Child, Preschool Child Longitudinal Studies Follow-Up Studies Humans Age Factors Machine Learning Forecasting Female Infant, Newborn
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Relation of Quantitative Histologic and Radiologic Breast Tissue Composition Metrics With Invasive Breast Cancer Risk.

JNCI cancer spectrum Feb 6, 2021
BACKGROUND: Benign breast disease (BBD) is a strong breast cancer risk factor, but identifying patients that might develop invasive breast cancer remains a challenge.
Odds Ratio Breast Density Breast Diseases Age Factors Mammography Biopsy Breast Humans Supervised Machine Learning Confidence Intervals Adult Female Breast Neoplasms Risk Factors Middle Aged Aged Case-Control Studies Aged, 80 and over Diagnosis, Computer-Assisted Young Adult
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Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients.

Scientific reports Feb 5, 2021
Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic m...
Critical Care Sex Factors Comorbidity Prospective Studies ROC Curve Computer Simulation Humans Hypertension Prognosis Machine Learning Body Mass Index Intensive Care Units Hospitalization COVID-19 Age Factors Female Aged Risk Factors Aged, 80 and over Male Middle Aged Respiration, Artificial
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Physical Features and Vital Signs Predict Serum Albumin and Globulin Concentrations Using Machine Learning.

Asian Pacific journal of cancer prevention : APJCP Feb 1, 2021
OBJECTIVE: Serum protein concentrations are diagnostically and prognostically valuable in cancer and other diseases, but their measurement via blood test is uncomfortable, inconvenient, and costly. This study investigates the possibility of predictin...
Humans Machine Learning Female Adult Male Middle Aged China Body Mass Index Vital Signs Age Factors Serum Albumin Predictive Value of Tests Serum Globulins Body Weight Sex Factors Body Height
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Logistic regression and machine learning predicted patient mortality from large sets of diagnosis codes comparably.

Journal of clinical epidemiology Jan 22, 2021
OBJECTIVE: The objective of the study was to compare the performance of logistic regression and boosted trees for predicting patient mortality from large sets of diagnosis codes in electronic healthcare records.
Colorectal Neoplasms Male Middle Aged England Logistic Models Forecasting Myocardial Infarction Humans Age Factors Sex Factors Electronic Health Records Machine Learning Hip Fractures Female International Classification of Diseases Aged Mortality Aged, 80 and over
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