AIMC Topic: Age Factors

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Machine Learning Classifier Models Can Identify Acute Respiratory Distress Syndrome Phenotypes Using Readily Available Clinical Data.

American journal of respiratory and critical care medicine Oct 1, 2020
Two distinct phenotypes of acute respiratory distress syndrome (ARDS) with differential clinical outcomes and responses to randomly assigned treatment have consistently been identified in randomized controlled trial cohorts using latent class analys...
Blood Pressure Area Under Curve Interleukin-8 Oxygen Intercellular Adhesion Molecule-1 Creatinine Vasoconstrictor Agents Humans Latent Class Analysis Machine Learning Inflammation Age Factors Respiratory Distress Syndrome Randomized Controlled Trials as Topic Phenotype Tidal Volume Interleukin-6 Serum Albumin Prognosis Biomarkers, Tumor Mortality Bicarbonates Leukocyte Count Receptors, Tumor Necrosis Factor, Type I Partial Pressure Protein C Carbon Dioxide Plasminogen Activator Inhibitor 1 Platelet Count Bilirubin Pulmonary Ventilation Vital Signs
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Can Machine-learning Algorithms Predict Early Revision TKA in the Danish Knee Arthroplasty Registry?

Clinical orthopaedics and related research Sep 1, 2020
BACKGROUND: Revision TKA is a serious adverse event with substantial consequences for the patient. As the demand for TKA rises, reducing the risk of revision TKA is becoming increasingly important. Predictive tools based on machine-learning algorithm...
Humans Registries Body Weight Male Arthroplasty, Replacement, Knee Knee Injuries Machine Learning Middle Aged Reoperation Preoperative Period Algorithms Risk Assessment Osteoarthritis, Knee Female Postoperative Complications Risk Factors Age Factors Aged Denmark Adult Predictive Value of Tests
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A Novel Machine Learning Model Developed to Assist in Patient Selection for Outpatient Total Shoulder Arthroplasty.

The Journal of the American Academy of Orthopaedic Surgeons Jul 1, 2020
INTRODUCTION: Patient selection for outpatient total shoulder arthroplasty (TSA) is important to optimizing patient outcomes. This study aims to develop a machine learning tool that may aid in patient selection for outpatient total should arthroplast...
Humans Machine Learning Forecasting Decision Support Techniques Female Aged Treatment Outcome Age Factors Patient Selection Sex Factors Outpatients Arthroplasty, Replacement, Shoulder Pulmonary Disease, Chronic Obstructive Male Comorbidity Length of Stay ROC Curve Logistic Models
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Machine Learning and Mechanistic Modeling for Prediction of Metastatic Relapse in Early-Stage Breast Cancer.

JCO clinical cancer informatics Mar 1, 2020
PURPOSE: For patients with early-stage breast cancer, predicting the risk of metastatic relapse is of crucial importance. Existing predictive models rely on agnostic survival analysis statistical tools (eg, Cox regression). Here we define and evaluat...
Neoplasm Metastasis Middle Aged Age Factors Predictive Value of Tests Neoplasm Staging Neoplasm Recurrence, Local Tumor Burden Humans Machine Learning Algorithms Computer Simulation Female Aged Aged, 80 and over Young Adult Adult Survival Rate Breast Neoplasms Biomarkers, Tumor
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Development of a periodontitis risk assessment model for primary care providers in an interdisciplinary setting.

Technology and health care : official journal of the European Society for Engineering and Medicine Jan 1, 2020
BACKGROUND: Periodontitis (PD), a form of gum disease, is a major public health concern as it is globally prevalent and harms both individual quality of life and economic productivity. Global cost in lost productivity is estimated at US$54 billion an...
Age Factors Oral Hygiene Humans Lipids Neural Networks, Computer Periodontitis Artificial Intelligence Blood Pressure Retrospective Studies Comorbidity Bayes Theorem Female Primary Health Care Sex Factors Aged Decision Support Systems, Clinical Body Weights and Measures Aged, 80 and over Logistic Models Young Adult Socioeconomic Factors Adult Support Vector Machine Male Middle Aged
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A Machine Learning-Based Triage Tool for Children With Acute Infection in a Low Resource Setting.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies Dec 1, 2019
OBJECTIVES: To deploy machine learning tools (random forests) to develop a model that reliably predicts hospital mortality in children with acute infections residing in low- and middle-income countries, using age and other variables collected at hosp...
Child, Preschool Child Hospital Mortality Age Factors Triage Developing Countries Child Mortality Sex Factors Infections Vital Signs Prognosis Female Male Humans Infant Machine Learning Severity of Illness Index Prospective Studies Adolescent Rwanda
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Machine Learning Approach for Predicting Past Environmental Exposures From Molecular Profiling of Post-Exposure Human Serum Samples.

Journal of occupational and environmental medicine Dec 1, 2019
OBJECTIVE: To develop an approach for a retrospective analysis of post-exposure serum samples using diverse molecular profiles.
Smoking Tobacco Smoke Pollution Humans Machine Learning Biomarkers Support Vector Machine Female Age Factors Young Adult Disease Models, Animal Adult Mice, Inbred C57BL Animals Cotinine Male Environmental Exposure Adolescent Sex Factors
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Artificial Intelligence Estimates the Importance of Baseline Factors in Predicting Response to Anti-PD1 in Metastatic Melanoma.

American journal of clinical oncology Aug 1, 2019
OBJECTIVE: Prognosis of patients with metastatic melanoma has dramatically improved over recent years because of the advent of antibodies targeting programmed cell death protein-1 (PD1). However, the response rate is ~40% and baseline biomarkers for ...
Lymphocyte Count Neoplasms, Unknown Primary Uveal Neoplasms Humans Middle Aged Female Neural Networks, Computer Treatment Outcome Melanoma Forecasting Neutrophils Skin Neoplasms Retrospective Studies Age Factors Aged Mucous Membrane Survival Rate Programmed Cell Death 1 Receptor Male L-Lactate Dehydrogenase
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Determinants of In-Hospital Mortality After Percutaneous Coronary Intervention: A Machine Learning Approach.

Journal of the American Heart Association Mar 5, 2019
Background The ability to accurately predict the occurrence of in-hospital death after percutaneous coronary intervention is important for clinical decision-making. We sought to utilize the New York Percutaneous Coronary Intervention Reporting System...
Coronary Artery Disease Humans Data Mining New York Machine Learning Middle Aged Databases, Factual Hospital Mortality Female Treatment Outcome Risk Factors Age Factors Aged Registries Male Percutaneous Coronary Intervention Stroke Volume
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Relationship of the low-density lipoprotein (LDL)/high-density lipoprotein (HDL) index with antioxidant enzymes and with the oxLDL/HDL index.

Gaceta medica de Mexico Jan 1, 2019
INTRODUCTION: The low-density lipoprotein (LDL)/high-density lipoprotein (HDL) index is a predictive factor for atherosclerosis, which is associated with oxidative modifications.
Sex Factors Glutathione Peroxidase Humans Insulin Resistance Female Lipoproteins, HDL Superoxide Dismutase Adult Nails Age Factors Cross-Sectional Studies Smoking Male Obesity Oxidative Stress Lipoproteins, LDL Magnesium
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