AIMC Topic: ROC Curve

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Prediction of early unplanned intensive care unit readmission in a UK tertiary care hospital: a cross-sectional machine learning approach.

BMJ open Sep 15, 2017
OBJECTIVES: Unplanned readmissions to the intensive care unit (ICU) are highly undesirable, increasing variance in care, making resource planning difficult and potentially increasing length of stay and mortality in some settings. Identifying patients...
Patient Readmission United Kingdom Aged Adult Cross-Sectional Studies Male Middle Aged ROC Curve Area Under Curve Tertiary Care Centers Machine Learning Retrospective Studies Humans Intensive Care Units Female
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A deep learning framework for supporting the classification of breast lesions in ultrasound images.

Physics in medicine and biology Sep 15, 2017
In this research, we exploited the deep learning framework to differentiate the distinctive types of lesions and nodules in breast acquired with ultrasound imaging. A biopsy-proven benchmarking dataset was built from 5151 patients cases containing a ...
Humans Middle Aged Diagnosis, Computer-Assisted Neural Networks, Computer ROC Curve Machine Learning Ultrasonography, Mammary Female Image Interpretation, Computer-Assisted Breast Neoplasms
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Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning.

IEEE transactions on medical imaging Sep 12, 2017
We describe an automated methodology for the analysis of unregistered cranio-caudal (CC) and medio-lateral oblique (MLO) mammography views in order to estimate the patient's risk of developing breast cancer. The main innovation behind this methodolog...
Humans Machine Learning Databases, Factual Female Breast Neoplasms ROC Curve Imaging, Three-Dimensional Mammography Radiographic Image Interpretation, Computer-Assisted Breast
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A novel and reliable computational intelligence system for breast cancer detection.

Medical & biological engineering & computing Sep 11, 2017
Cancer is the second important morbidity and mortality factor among women and the most incident type is breast cancer. This paper suggests a hybrid computational intelligence model based on unsupervised and supervised learning techniques, i.e., self-...
Models, Theoretical Early Detection of Cancer Decision Making Female Reproducibility of Results Breast Neoplasms ROC Curve Humans Artificial Intelligence
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Predicting posttraumatic stress disorder following a natural disaster.

Journal of psychiatric research Sep 8, 2017
Earthquakes are a common and deadly natural disaster, with roughly one-quarter of survivors subsequently developing posttraumatic stress disorder (PTSD). Despite progress identifying risk factors, limited research has examined how to combine variable...
Disasters ROC Curve Machine Learning Logistic Models Algorithms Prevalence Area Under Curve Humans Prognosis Surveys and Questionnaires Chile Risk Assessment Interviews as Topic Stress Disorders, Post-Traumatic
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Combining Biomarkers with EMR Data to Identify Patients in Different Phases of Sepsis.

Scientific reports Sep 7, 2017
Sepsis is a leading cause of death and is the most expensive condition to treat in U.S. hospitals. Despite targeted efforts to automate earlier detection of sepsis, current techniques rely exclusively on using either standard clinical data or novel b...
Electronic Data Processing Sepsis ROC Curve United States Predictive Value of Tests Humans Machine Learning Decision Support Techniques Biomarkers Electronic Health Records
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Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis.

Journal of healthcare engineering Sep 6, 2017
BACKGROUND: Of the machine learning techniques used in predicting coronary heart disease (CHD), neural network (NN) is popularly used to improve performance accuracy.
Adult Female Humans ROC Curve Male Risk Factors Neural Networks, Computer Nutrition Surveys Middle Aged Databases, Factual Aged Coronary Artery Disease Risk Assessment Republic of Korea Aged, 80 and over
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Gene2DisCo: Gene to disease using disease commonalities.

Artificial intelligence in medicine Sep 4, 2017
OBJECTIVE: Finding the human genes co-causing complex diseases, also known as "disease-genes", is one of the emerging and challenging tasks in biomedicine. This process, termed gene prioritization (GP), is characterized by a scarcity of known disease...
Humans Machine Learning Algorithms Databases, Genetic Area Under Curve Genetic Markers Data Mining ROC Curve Gene Regulatory Networks Genetic Predisposition to Disease Models, Genetic Linear Models Toxicogenetics
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A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme.

Scientific reports Sep 4, 2017
Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep features extracted via transfer learning can generate radiomics signatures for prediction of overall sur...
Young Adult Glioblastoma Adult Male Image Processing, Computer-Assisted Image Interpretation, Computer-Assisted Middle Aged Kaplan-Meier Estimate Nomograms Deep Learning Models, Theoretical Adolescent Magnetic Resonance Imaging Child ROC Curve Female Humans Reproducibility of Results Prognosis Aged Aged, 80 and over
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Accuracy of deep learning, a machine-learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment.

Scientific reports Aug 25, 2017
Rhegmatogenous retinal detachment (RRD) is a serious condition that can lead to blindness; however, it is highly treatable with timely and appropriate treatment. Thus, early diagnosis and treatment of RRD is crucial. In this study, we applied deep le...
Male Middle Aged Deep Learning Humans ROC Curve Fundus Oculi Machine Learning Ophthalmoscopy Female Retinal Detachment Aged Sensitivity and Specificity
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