AIMC Topic: Retrospective Studies

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Computer-aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI.

Journal of magnetic resonance imaging : JMRI Apr 16, 2018
BACKGROUND: Deep learning is the most promising methodology for automatic computer-aided diagnosis of prostate cancer (PCa) with multiparametric MRI (mp-MRI).
Sensitivity and Specificity Male Diagnosis, Computer-Assisted Deep Learning ROC Curve Predictive Value of Tests Area Under Curve Prostatic Neoplasms Pattern Recognition, Automated Diffusion Magnetic Resonance Imaging Neural Networks, Computer Software Databases, Factual Retrospective Studies Reproducibility of Results Humans
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ESMAC BEST PAPER 2017: Using machine learning to overcome challenges in GMFCS level assignment.

Gait & posture Apr 16, 2018
We used the random forest classifier to predict Gross Motor Function Classification System (GMFCS) levels I-IV from patient reported abilities recorded on the Gillette Functional Assessment Questionnaire (FAQ). The classifier exhibited outstanding ac...
Machine Learning Retrospective Studies Female Male Surveys and Questionnaires Adolescent Child, Preschool Child Disability Evaluation Activities of Daily Living Humans Cerebral Palsy Motor Skills
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Identification of Clinically Meaningful Plasma Transfusion Subgroups Using Unsupervised Random Forest Clustering.

AMIA ... Annual Symposium proceedings. AMIA Symposium Apr 16, 2018
Statistical techniques such as propensity score matching and instrumental variable are commonly employed to "simulate" randomization and adjust for measured confounders in comparative effectiveness research. Despite such adjustments, the results of t...
Surgical Procedures, Operative Plasma Blood Component Transfusion Postoperative Complications Logistic Models Humans Blood Loss, Surgical Machine Learning Cluster Analysis Precision Medicine International Normalized Ratio Retrospective Studies Risk Factors
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Assessing patient risk of central line-associated bacteremia via machine learning.

American journal of infection control Apr 13, 2018
BACKGROUND: Central line-associated bloodstream infections (CLABSIs) contribute to increased morbidity, length of hospital stay, and cost. Despite progress in understanding the risk factors, there remains a need to accurately predict the risk of CLAB...
Catheterization, Central Venous Catheter-Related Infections Academic Medical Centers Epidemiologic Methods Adult Female Case-Control Studies Humans Infant, Newborn Adolescent Male Machine Learning Child, Preschool Middle Aged Aged Retrospective Studies Child Risk Assessment Aged, 80 and over Bacteremia Infant Young Adult
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Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients.

Journal of the American Heart Association Apr 12, 2018
BACKGROUND: Whereas heart failure (HF) is a complex clinical syndrome, conventional approaches to its management have treated it as a singular disease, leading to inadequate patient care and inefficient clinical trials. We hypothesized that applying ...
Retrospective Studies Female Reproducibility of Results Sweden Aged Humans Machine Learning Algorithms Phenotype Prognosis Middle Aged Follow-Up Studies Registries Heart Failure Diuretics Aged, 80 and over Survival Rate Male Ventricular Function, Left Stroke Volume Angiotensin Receptor Antagonists Angiotensin-Converting Enzyme Inhibitors Cardiovascular Agents Adrenergic beta-Antagonists
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Coronary CT Angiography-derived Fractional Flow Reserve: Machine Learning Algorithm versus Computational Fluid Dynamics Modeling.

Radiology Apr 10, 2018
Purpose To compare two technical approaches for determination of coronary computed tomography (CT) angiography-derived fractional flow reserve (FFR)-FFR derived from coronary CT angiography based on computational fluid dynamics (hereafter, FFR) and F...
Coronary Stenosis Fractional Flow Reserve, Myocardial Hemodynamics Predictive Value of Tests Coronary Angiography Hydrodynamics Humans Computed Tomography Angiography Machine Learning Algorithms Retrospective Studies Female Reproducibility of Results Sensitivity and Specificity Male Middle Aged
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Using machine learning techniques to develop forecasting algorithms for postoperative complications: protocol for a retrospective study.

BMJ open Apr 10, 2018
INTRODUCTION: Mortality and morbidity following surgery are pressing public health concerns in the USA. Traditional prediction models for postoperative adverse outcomes demonstrate good discrimination at the population level, but the ability to forec...
Missouri Machine Learning Algorithms Retrospective Studies Adult Postoperative Complications Humans
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Creation of knowledge-based planning models intended for large scale distribution: Minimizing the effect of outlier plans.

Journal of applied clinical medical physics Apr 6, 2018
Knowledge-based planning (KBP) can be used to estimate dose-volume histograms (DVHs) of organs at risk (OAR) using models. The task of model creation, however, can result in estimates with differing accuracy; particularly when outlier plans are not p...
Humans Retrospective Studies Male Quality Control Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated Organ Sparing Treatments Head and Neck Neoplasms Prostatic Neoplasms Models, Biological Knowledge Bases Organs at Risk
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Use of 2D U-Net Convolutional Neural Networks for Automated Cartilage and Meniscus Segmentation of Knee MR Imaging Data to Determine Relaxometry and Morphometry.

Radiology Mar 27, 2018
Purpose To analyze how automatic segmentation translates in accuracy and precision to morphology and relaxometry compared with manual segmentation and increases the speed and accuracy of the work flow that uses quantitative magnetic resonance (MR) im...
Image Processing, Computer-Assisted Magnetic Resonance Imaging Knee Joint Humans Young Adult Neural Networks, Computer Retrospective Studies Joint Diseases Meniscus Female Cartilage, Articular Adult Reproducibility of Results Male Aged Middle Aged Aged, 80 and over Adolescent
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Treatment-related features improve machine learning prediction of prognosis in soft tissue sarcoma patients.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] Mar 20, 2018
BACKGROUND AND PURPOSE: Current prognostic models for soft tissue sarcoma (STS) patients are solely based on staging information. Treatment-related data have not been included to date. Including such information, however, could help to improve these ...
Neoadjuvant Therapy Neoplasm Staging Sarcoma Proportional Hazards Models Male Middle Aged Female Risk Assessment Aged Disease Progression Aged, 80 and over Cohort Studies Adult Humans Survival Rate Machine Learning Prognosis Retrospective Studies
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