AIMC Topic:
ROC Curve

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Predictors of in-hospital length of stay among cardiac patients: A machine learning approach.

International journal of cardiology Jan 19, 2019
OBJECTIVE: The In-hospital length of stay (LOS) is expected to increase as cardiovascular diseases complexity increases and the population ages. This will affect healthcare systems especially with the current situation of decreased bed capacity and i...
Length of Stay Humans Morbidity Electronic Health Records Saudi Arabia Female Heart Diseases ROC Curve Machine Learning Male Inpatients Middle Aged Prognosis Retrospective Studies
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Machine learning as a supportive tool to recognize cardiac arrest in emergency calls.

Resuscitation Jan 18, 2019
BACKGROUND: Emergency medical dispatchers fail to identify approximately 25% of cases of out of hospital cardiac arrest, thus lose the opportunity to provide the caller instructions in cardiopulmonary resuscitation. We examined whether a machine lear...
Humans Machine Learning Retrospective Studies Female Aged Aged, 80 and over Male Middle Aged Out-of-Hospital Cardiac Arrest Time Factors Emergency Medical Dispatch ROC Curve Emergency Medical Service Communication Systems Emergency Medical Services Cardiopulmonary Resuscitation
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EPAI-NC: Enhanced prediction of adenosine to inosine RNA editing sites using nucleotide compositions.

Analytical biochemistry Jan 18, 2019
RNA editing process like Adenosine to Intosine (A-to-I) often influences basic functions like splicing stability and most importantly the translation. Thus knowledge about editing sites is of great importance in molecular biology. With the growth of ...
User-Computer Interface ROC Curve Area Under Curve RNA RNA Editing Machine Learning Adenosine Inosine
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A Deep Neural Network-Based Pain Classifier Using a Photoplethysmography Signal.

Sensors (Basel, Switzerland) Jan 18, 2019
Side effects occur when excessive or low doses of analgesics are administered compared to the required amount to mediate the pain induced during surgery. It is important to accurately assess the pain level of the patient during surgery. We proposed a...
ROC Curve Signal Processing, Computer-Assisted Algorithms Humans Models, Theoretical Middle Aged Pain Neural Networks, Computer Photoplethysmography Deep Learning Heart Rate Support Vector Machine Time Factors Postoperative Period
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Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion.

Medical physics Jan 16, 2019
PURPOSE: We propose a deep learning-based approach to breast mass classification in sonography and compare it with the assessment of four experienced radiologists employing breast imaging reporting and data system 4th edition lexicon and assessment p...
Humans Female Adolescent Ultrasonography ROC Curve Image Processing, Computer-Assisted Color Young Adult Adult Breast Neoplasms Deep Learning
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Deep Learning Algorithms with Demographic Information Help to Detect Tuberculosis in Chest Radiographs in Annual Workers' Health Examination Data.

International journal of environmental research and public health Jan 16, 2019
We aimed to use deep learning to detect tuberculosis in chest radiographs in annual workers' health examination data and compare the performances of convolutional neural networks (CNNs) based on images only (I-CNN) and CNNs including demographic vari...
Tuberculosis, Pulmonary Occupational Health Demography Physical Examination Middle Aged Deep Learning ROC Curve Radiography Humans Female Adult Sensitivity and Specificity Male
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Rapid detection of internalizing diagnosis in young children enabled by wearable sensors and machine learning.

PloS one Jan 16, 2019
There is a critical need for fast, inexpensive, objective, and accurate screening tools for childhood psychopathology. Perhaps most compelling is in the case of internalizing disorders, like anxiety and depression, where unobservable symptoms cause c...
Anxiety Depression Wearable Electronic Devices Child, Preschool Humans Female Sensitivity and Specificity Male Psychology, Child Signal Processing, Computer-Assisted Child Supervised Machine Learning ROC Curve Affect Logistic Models Psychopathology Models, Statistical
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Binomial Logistic Regression and Artificial Neural Network Methods to Classify Opioid-Dependent Subjects and Control Group Using Quantitative EEG Power Measures.

Clinical EEG and neuroscience Jan 14, 2019
Logistic regression (LR) and artificial neural networks (ANNs) are widely referred approaches in medical data classification studies. LR, a statistical fitting model, is suggested in medical problems because of its well-established methodology and co...
Analgesics, Opioid Control Groups ROC Curve Logistic Models Electroencephalography Signal Processing, Computer-Assisted Humans Female Neural Networks, Computer Adult Algorithms Male Middle Aged
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Semi-supervised encoding for outlier detection in clinical observation data.

Computer methods and programs in biomedicine Jan 12, 2019
BACKGROUND AND OBJECTIVE: Electronic Health Record (EHR) data often include observation records that are unlikely to represent the "truth" about a patient at a given clinical encounter. Due to their high throughput, examples of such implausible obser...
Registries Data Collection Medical Informatics Male ROC Curve Models, Statistical Humans Data Interpretation, Statistical Neural Networks, Computer Algorithms Electronic Health Records Female Reproducibility of Results
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Using neuroimaging to predict relapse in stimulant dependence: A comparison of linear and machine learning models.

NeuroImage. Clinical Jan 11, 2019
OBJECTIVE: Relapse rates are consistently high for stimulant user disorders. In order to obtain prognostic information about individuals in treatment, machine learning models have been applied to neuroimaging and clinical data. Yet few efforts have b...
Humans Machine Learning Female Adult Longitudinal Studies Male Methamphetamine Depressive Disorder, Major Neuroimaging ROC Curve Recurrence Logistic Models
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