AIMC Topic: United States

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Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI.

European radiology Apr 23, 2019
OBJECTIVES: To develop and validate a proof-of-concept convolutional neural network (CNN)-based deep learning system (DLS) that classifies common hepatic lesions on multi-phasic MRI.
Bile Duct Neoplasms Cholangiocarcinoma Bile Ducts, Intrahepatic Liver Neoplasms Carcinoma, Hepatocellular United States Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Humans Adult Neural Networks, Computer Sensitivity and Specificity Female Male Reproducibility of Results Middle Aged Aged Deep Learning ROC Curve
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Regional level influenza study based on Twitter and machine learning method.

PloS one Apr 23, 2019
The significance of flu prediction is that the appropriate preventive and control measures can be taken by relevant departments after assessing predicted data; thus, morbidity and mortality can be reduced. In this paper, three flu prediction models, ...
Centers for Disease Control and Prevention, U.S. United States Humans Support Vector Machine Models, Statistical Social Media Data Analysis Influenza, Human Forecasting Disease Outbreaks Vaccination
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Accuracy of American College of Surgeons National Surgical Quality Improvement Program Universal Surgical Risk Calculator in Predicting Complications Following Robot-Assisted Radical Cystectomy at a National Comprehensive Cancer Center.

Journal of endourology Apr 22, 2019
There is paucity of literature about the validation of the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) surgical risk calculator for prediction of outcomes after robot-assisted radical cystectomy (RARC). We ...
Humans Decision Support Techniques Retrospective Studies Female Aged ROC Curve United States Robotics Aged, 80 and over Area Under Curve Sensitivity and Specificity Quality Improvement Male Cystectomy Risk Assessment Postoperative Complications
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A machine learning approach to knee osteoarthritis phenotyping: data from the FNIH Biomarkers Consortium.

Osteoarthritis and cartilage Apr 16, 2019
OBJECTIVE: Knee osteoarthritis (KOA) is a heterogeneous condition representing a variety of potentially distinct phenotypes. The purpose of this study was to apply innovative machine learning approaches to KOA phenotyping in order to define progressi...
Biological Variation, Population Collagen Type II Cartilage, Articular Menisci, Tibial Congresses as Topic National Institutes of Health (U.S.) Databases, Factual Biomarkers Female Aged Male Middle Aged Disease Progression Severity of Illness Index United States Pain Measurement Osteoarthritis, Knee Humans Machine Learning
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Application of Machine Learning to Identify Clustering of Cardiometabolic Risk Factors in U.S. Adults.

Diabetes technology & therapeutics Apr 10, 2019
The aim of this study is to compare some machine learning methods with traditional statistical parametric analyses using logistic regression to investigate the relationship of risk factors for diabetes and cardiovascular (cardiometabolic risk) for U...
Humans Machine Learning Algorithms Female Male Cardiovascular Diseases Middle Aged Risk Assessment Adolescent United States Risk Factors Aged Aged, 80 and over Young Adult Adult Smoking Alcohol Drinking Life Style Body Mass Index Cluster Analysis Lipids Waist Circumference Metabolic Diseases
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Utilization of Minimally Invasive Thymectomy and Margin-Negative Resection for Early-Stage Thymoma.

The Annals of thoracic surgery Apr 3, 2019
BACKGROUND: Minimally invasive thymectomy (MIT) has demonstrated improved short-term outcomes compared with open thymectomy (OT). Although adoption of MIT for thymoma is increasing, oncologic outcomes have not been well characterized.
Thymus Neoplasms Robotics Neoplasm Staging Margins of Excision Thymectomy Thymoma Female Aged Aged, 80 and over Young Adult Adult Survival Rate Male Middle Aged Adolescent United States Follow-Up Studies Humans Retrospective Studies
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Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions.

JAMA network open Mar 1, 2019
IMPORTANCE: Current approaches to identifying individuals at high risk for opioid overdose target many patients who are not truly at high risk.
Cohort Studies United States Analgesics, Opioid Medicare Drug Overdose Medical Informatics Applications Prescriptions Aged Aged, 80 and over Male Middle Aged Humans Risk Assessment Machine Learning Algorithms Female
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Assessment of Machine Learning vs Standard Prediction Rules for Predicting Hospital Readmissions.

JAMA network open Mar 1, 2019
IMPORTANCE: Hospital readmissions are associated with patient harm and expense. Ways to prevent hospital readmissions have focused on identifying patients at greatest risk using prediction scores.
Humans Machine Learning Research Design Prognosis Risk Assessment Female Length of Stay Risk Factors United States Hospitalization Medicare Male Patient Readmission Middle Aged Maryland
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Machine Learning Accurately Predicts Short-Term Outcomes Following Open Reduction and Internal Fixation of Ankle Fractures.

The Journal of foot and ankle surgery : official publication of the American College of Foot and Ankle Surgeons Feb 23, 2019
Ankle fractures are common orthopedic injuries with favorable outcomes when managed with open reduction and internal fixation (ORIF). Several patient-related risk factors may contribute to poor short-term outcomes, and machine learning may be a valua...
Open Fracture Reduction Middle Aged Postoperative Complications Length of Stay Hospital Mortality Medicare Hypertension United States Humans Heart Failure Databases, Factual Diabetes Mellitus Anemia Female Machine Learning Age Factors Renal Insufficiency Risk Factors Algorithms Fracture Fixation, Internal Cross-Sectional Studies Ankle Fractures Lung Diseases Male Medicaid
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A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data.

International journal of medical informatics Feb 20, 2019
OBJECTIVE: In this systematic review, we aim to synthesize the literature on the use of natural language processing (NLP) and text mining as they apply to symptom extraction and processing in electronic patient-authored text (ePAT).
Humans Data Mining Electronic Health Records Patient Participation United States National Institutes of Health (U.S.) Natural Language Processing
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