AIMC Topic: Risk Assessment

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System for High-Intensity Evaluation During Radiation Therapy (SHIELD-RT): A Prospective Randomized Study of Machine Learning-Directed Clinical Evaluations During Radiation and Chemoradiation.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology Sep 4, 2020
PURPOSE: Patients undergoing outpatient radiotherapy (RT) or chemoradiation (CRT) frequently require acute care (emergency department evaluation or hospitalization). Machine learning (ML) may guide interventions to reduce this risk. There are limited...
Prospective Studies Female Hospitalization Aged Male Middle Aged Humans Machine Learning Forecasting Emergency Service, Hospital Ambulatory Care Quality Improvement Chemoradiotherapy Radiotherapy Standard of Care Risk Assessment Neoplasms ROC Curve Models, Theoretical Area Under Curve
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Improving preoperative risk-of-death prediction in surgery congenital heart defects using artificial intelligence model: A pilot study.

PloS one Sep 4, 2020
BACKGROUND: Congenital heart disease accounts for almost a third of all major congenital anomalies. Congenital heart defects have a significant impact on morbidity, mortality and health costs for children and adults. Research regarding the risk of pr...
Risk Assessment Pilot Projects Registries Preoperative Period Heart Defects, Congenital Humans Artificial Intelligence Retrospective Studies Female Male
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Prognostic importance of Kidney, Heart and Interstitial lung diseases (KHI triad) in PH: A machine learning study.

Archives of cardiovascular diseases Sep 2, 2020
BACKGROUND: Pulmonary hypertension (PH) is a heterogeneous, severe and progressive disease with an impact on quality of life and life-expectancy despite specific therapies.
Cluster Analysis Kidney Diseases Registries Lung Diseases, Interstitial Data Mining Heart Diseases Male Middle Aged Risk Assessment Comorbidity Health Status France Pulmonary Arterial Hypertension Humans Machine Learning Phenotype Prospective Studies Prognosis Female Risk Factors Aged Aged, 80 and over
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Deep Learning Using Chest Radiographs to Identify High-Risk Smokers for Lung Cancer Screening Computed Tomography: Development and Validation of a Prediction Model.

Annals of internal medicine Sep 1, 2020
BACKGROUND: Lung cancer screening with chest computed tomography (CT) reduces lung cancer death. Centers for Medicare & Medicaid Services (CMS) eligibility criteria for lung cancer screening with CT require detailed smoking information and miss many ...
Humans Decision Support Techniques Tomography, X-Ray Computed Female Risk Assessment Lung Deep Learning Lung Neoplasms Early Detection of Cancer Smoking Risk Factors Aged Sensitivity and Specificity Male Middle Aged
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Validation of a Machine Learning Model to Predict Childhood Lead Poisoning.

JAMA network open Sep 1, 2020
IMPORTANCE: Childhood lead poisoning causes irreversible neurobehavioral deficits, but current practice is secondary prevention.
Lead Poisoning Resource Allocation Risk Assessment Child, Preschool Logistic Models United States Health Care Rationing Preventive Health Services Humans Machine Learning Female Sensitivity and Specificity Male
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Assessment of a Deep Learning Model to Predict Hepatocellular Carcinoma in Patients With Hepatitis C Cirrhosis.

JAMA network open Sep 1, 2020
IMPORTANCE: Deep learning, a family of machine learning models that use artificial neural networks, has achieved great success at predicting outcomes in nonmedical domains.
Veterans Health Services Humans Chronic Disease Indicators Neural Networks, Computer Prognosis Carcinoma, Hepatocellular Electronic Health Records Liver Cirrhosis Female Area Under Curve Male Hepatitis C, Chronic Middle Aged Sustained Virologic Response Risk Assessment Deep Learning Incidence Liver Neoplasms
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Predicting Coronavirus Disease 2019 Infection Risk and Related Risk Drivers in Nursing Homes: A Machine Learning Approach.

Journal of the American Medical Directors Association Aug 27, 2020
OBJECTIVE: Inform coronavirus disease 2019 (COVID-19) infection prevention measures by identifying and assessing risk and possible vectors of infection in nursing homes (NHs) using a machine-learning approach.
Forecasting Retrospective Studies SARS-CoV-2 Risk Factors Risk Assessment Humans United States Machine Learning Coronavirus Infections Pandemics Nursing Homes COVID-19 Betacoronavirus Algorithms Pneumonia, Viral
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A machine learning approach to predict intravenous immunoglobulin resistance in Kawasaki disease patients: A study based on a Southeast China population.

PloS one Aug 27, 2020
Kawasaki disease is the leading cause of pediatric acquired heart disease. Coronary artery abnormalities are the main complication of Kawasaki disease. Kawasaki disease patients with intravenous immunoglobulin resistance are at a greater risk of deve...
Drug Resistance Humans Mucocutaneous Lymph Node Syndrome Coronary Artery Disease Feasibility Studies Risk Assessment Machine Learning Models, Biological Infant Immunoglobulins, Intravenous Retrospective Studies China Child, Preschool Female ROC Curve Risk Factors Logistic Models Male
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Biomarkers of the Response to Immune Checkpoint Inhibitors in Metastatic Urothelial Carcinoma.

Frontiers in immunology Aug 25, 2020
The mechanisms underlying the resistance to immune checkpoint inhibitors (ICIs) therapy in metastatic urothelial carcinoma (mUC) patients are not clear. It is of great significance to discern mUC patients who could benefit from ICI therapy in clinica...
Risk Factors Time Factors Humans Antibodies, Monoclonal, Humanized Biomarkers, Tumor Immune Checkpoint Inhibitors Machine Learning Male Urinary Bladder Neoplasms B7-H1 Antigen Decision Support Techniques Risk Assessment Predictive Value of Tests Nomograms Clinical Decision-Making Gene Expression Profiling Carcinoma Treatment Outcome Female Transcriptome Drug Resistance, Neoplasm Urothelium
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Prediction, pattern recognition and modelling of complications post-endovascular infra renal aneurysm repair by artificial intelligence.

Vascular Aug 24, 2020
OBJECTIVES: The study evaluates the plausibility and applicability of prediction, pattern recognition and modelling of complications post-endovascular aneurysm repair (EVAR) by artificial intelligence for more accurate surveillance in practice.
Humans Aged, 80 and over Blood Vessel Prosthesis Implantation Male Treatment Outcome Artificial Intelligence Endovascular Procedures Middle Aged Support Vector Machine Graft Occlusion, Vascular Prospective Studies Risk Assessment Decision Trees Blood Vessel Prosthesis Female Postoperative Complications Aortic Aneurysm, Abdominal Foreign-Body Migration Risk Factors Time Factors Pattern Recognition, Automated Endoleak Aged
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