AIMC Topic: Predictive Value of Tests

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Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients.

PloS one Jan 9, 2019
BACKGROUND: Patient with acute coronary syndrome benefits from early revascularization. However, methods for the selection of patients who require urgent revascularization from a variety of patients visiting the emergency room with chest symptoms is ...
Humans Neural Networks, Computer Diagnosis, Differential Acute Coronary Syndrome Predictive Value of Tests Prognosis Emergency Service, Hospital Datasets as Topic Sensitivity and Specificity Myocardial Revascularization Patient Selection Diagnosis, Computer-Assisted Decision Making, Computer-Assisted Chest Pain Time Factors Electrocardiography
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A clinical, proteomics, and artificial intelligence-driven model to predict acute kidney injury in patients undergoing coronary angiography.

Clinical cardiology Jan 8, 2019
BACKGROUND: Standard measures of kidney function are only modestly useful for accurate prediction of risk for acute kidney injury (AKI).
Artificial Intelligence Prospective Studies Biomarkers Acute Kidney Injury Female Humans Proteomics Contrast Media Predictive Value of Tests Coronary Angiography Coronary Artery Disease Creatinine Aged Male Risk Assessment Incidence ROC Curve
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Automatic thyroid nodule recognition and diagnosis in ultrasound imaging with the YOLOv2 neural network.

World journal of surgical oncology Jan 8, 2019
BACKGROUND: In this study, images of 2450 benign thyroid nodules and 2557 malignant thyroid nodules were collected and labeled, and an automatic image recognition and diagnosis system was established by deep learning using the YOLOv2 neural network. ...
Thyroid Nodule ROC Curve Diagnosis, Differential Predictive Value of Tests Humans Female Image Interpretation, Computer-Assisted Adult Aged Neural Networks, Computer Male Young Adult Prognosis Middle Aged Retrospective Studies Thyroid Gland Ultrasonography
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Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance Jan 7, 2019
BACKGROUND: Phase contrast (PC) cardiovascular magnetic resonance (CMR) is widely employed for flow quantification, but analysis typically requires time consuming manual segmentation which can require human correction. Advances in machine learning ha...
Automation Proof of Concept Study Blood Flow Velocity Aorta Myocardial Perfusion Imaging United States Aortic Valve Predictive Value of Tests Heart Diseases Image Interpretation, Computer-Assisted Magnetic Resonance Imaging, Cine Humans Machine Learning Prospective Studies Retrospective Studies Female Reproducibility of Results Aged Male Middle Aged Hemodynamics
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Machine Learning-Based Prediction of Clinical Outcomes for Children During Emergency Department Triage.

JAMA network open Jan 4, 2019
IMPORTANCE: While machine learning approaches may enhance prediction ability, little is known about their utility in emergency department (ED) triage.
Predictive Value of Tests Triage Emergency Service, Hospital Critical Care Quality Improvement Critical Care Outcomes Models, Educational Child, Hospitalized Male Adolescent Child, Preschool Humans Child Machine Learning United States Prognosis Female Hospitalization
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Machine learning-based radiomic models to predict intensity-modulated radiation therapy response, Gleason score and stage in prostate cancer.

La Radiologia medica Jan 3, 2019
OBJECTIVE: To develop different radiomic models based on the magnetic resonance imaging (MRI) radiomic features and machine learning methods to predict early intensity-modulated radiation therapy (IMRT) response, Gleason scores (GS) and prostate canc...
Aged, 80 and over Male Middle Aged Treatment Outcome Magnetic Resonance Imaging Predictive Value of Tests Humans Machine Learning Prospective Studies Aged Prostatic Neoplasms Neoplasm Staging Neoplasm Grading Image Interpretation, Computer-Assisted Radiotherapy, Intensity-Modulated
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Radiogenomics in Clear Cell Renal Cell Carcinoma: Machine Learning-Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status.

AJR. American journal of roentgenology Jan 2, 2019
OBJECTIVE: The purpose of this study is to evaluate the potential value of machine learning (ML)-based high-dimensional quantitative CT texture analysis in predicting the mutation status of the gene encoding the protein polybromo-1 (PBRM1) in patient...
Radiographic Image Interpretation, Computer-Assisted DNA-Binding Proteins Kidney Neoplasms Carcinoma, Renal Cell Female Humans Nuclear Proteins Machine Learning Reproducibility of Results Transcription Factors Algorithms Aged Contrast Media Retrospective Studies Male Predictive Value of Tests Tomography, X-Ray Computed Middle Aged Mutation
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A novel prediction method for lymph node involvement in endometrial cancer: machine learning.

International journal of gynecological cancer : official journal of the International Gynecological Cancer Society Dec 28, 2018
OBJECTIVE: The necessity of lymphadenectomy and the prediction of lymph node involvement (LNI) in endometrial cancer (EC) have been hotly-debated questions in recent years. Machine learning is a broad field that can produce results and estimations. I...
Follow-Up Studies Lymph Node Excision Models, Statistical Predictive Value of Tests Aged Humans Endometrial Neoplasms Aged, 80 and over Machine Learning Adult Retrospective Studies Middle Aged Female Lymph Nodes
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Automated data extraction and ensemble methods for predictive modeling of breast cancer outcomes after radiation therapy.

Medical physics Dec 28, 2018
PURPOSE: The purpose of this study was to compare the effectiveness of ensemble methods (e.g., random forests) and single-model methods (e.g., logistic regression and decision trees) in predictive modeling of post-RT treatment failure and adverse eve...
Middle Aged Treatment Outcome Decision Trees Predictive Value of Tests Humans Data Mining Machine Learning Radiotherapy Dosage Electronic Health Records Female Breast Neoplasms
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Predicting persistent depressive symptoms in older adults: A machine learning approach to personalised mental healthcare.

Journal of affective disorders Dec 25, 2018
BACKGROUND: Depression causes significant physical and psychosocial morbidity. Predicting persistence of depressive symptoms could permit targeted prevention, and lessen the burden of depression. Machine learning is a rapidly expanding field, and suc...
Psychiatric Status Rating Scales Depression Female Logistic Models Predictive Value of Tests Aged Humans Follow-Up Studies Aged, 80 and over Age Factors Machine Learning Male Algorithms Risk Assessment Decision Support Techniques Delivery of Health Care
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