AIMC Topic: Logistic Models

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Sex estimation: a comparison of techniques based on binary logistic, probit and cumulative probit regression, linear and quadratic discriminant analysis, neural networks, and naïve Bayes classification using ordinal variables.

International journal of legal medicine Aug 23, 2019
The performance of seven classification methods, binary logistic (BLR), probit (PR) and cumulative probit (CPR) regression, linear (LDA) and quadratic (QDA) discriminant analysis, artificial neural networks (ANN), and naïve Bayes classification (NBC)...
Statistics as Topic Skull Discriminant Analysis Pelvis Sex Determination by Skeleton Humans Neural Networks, Computer Machine Learning Algorithms Female Male Logistic Models Bayes Theorem
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Prediction model development of late-onset preeclampsia using machine learning-based methods.

PloS one Aug 23, 2019
Preeclampsia is one of the leading causes of maternal and fetal morbidity and mortality. Due to the lack of effective preventive measures, its prediction is essential to its prompt management. This study aimed to develop models using machine learning...
Bayes Theorem Female Humans Adult Machine Learning Pre-Eclampsia Pregnancy Trimester, Second Logistic Models Pregnancy Age of Onset Support Vector Machine Gestational Age Pregnancy Trimester, Third
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Machine Learning Diagnosis of Peritonsillar Abscess.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery Aug 20, 2019
Peritonsillar abscess (PTA) is a difficult diagnosis to make clinically, with clinical examination of even otolaryngologists showing poor sensitivity and specificity. Machine learning is a form of artificial intelligence that "learns" from data to ma...
Infant Adolescent Child, Preschool Child Sensitivity and Specificity Male Middle Aged Logistic Models Symptom Assessment Peritonsillar Abscess Algorithms Retrospective Studies Female Aged Aged, 80 and over Young Adult Adult Humans Machine Learning
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Statistical thinking, machine learning.

Journal of clinical epidemiology Aug 16, 2019
Machine Learning Logistic Models Artificial Intelligence
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Statistics versus machine learning: definitions are interesting (but understanding, methodology, and reporting are more important).

Journal of clinical epidemiology Aug 16, 2019
Machine Learning Algorithms Logistic Models Biometry
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Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: a retrospective, population-based registry study.

BMJ open Aug 10, 2019
OBJECTIVES: The aim of this work was to train machine learning models to identify patients at end of life with clinically meaningful diagnostic accuracy, using 30-day mortality in patients discharged from the emergency department (ED) as a proxy.
Aged Aged, 80 and over Young Adult Adult Electronic Health Records Female Sweden Emergency Service, Hospital Registries Patient Discharge Mortality Humans Male Middle Aged Machine Learning Time Factors Retrospective Studies Logistic Models
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Comparison between logistic regression and machine learning algorithms on survival prediction of traumatic brain injuries.

Journal of critical care Aug 5, 2019
PURPOSE: To compare twenty-two machine learning (ML) models against logistic regression on survival prediction in severe traumatic brain injury (STBI) patients in a single center study.
Cross-Sectional Studies Machine Learning Humans Middle Aged Area Under Curve Algorithms Logistic Models Brain Injuries, Traumatic Prognosis Female
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Predicting death by suicide using administrative health care system data: Can feedforward neural network models improve upon logistic regression models?

Journal of affective disorders Jul 30, 2019
BACKGROUND: Suicide is a leading cause of death worldwide. With the increasing volume of administrative health care data, there is an opportunity to evaluate whether machine learning models can improve upon statistical models for quantifying suicide ...
Male Predictive Value of Tests Humans Risk Assessment Neural Networks, Computer Suicide Machine Learning Logistic Models Female
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Comparison of machine learning algorithms for clinical event prediction (risk of coronary heart disease).

Journal of biomedical informatics Jul 30, 2019
AIM: The aim of this study is to compare the utility of several supervised machine learning (ML) algorithms for predicting clinical events in terms of their internal validity and accuracy. The results, which were obtained using two statistical softwa...
Coronary Disease Models, Statistical Decision Trees Databases, Factual Area Under Curve Prospective Studies Humans Logistic Models Supervised Machine Learning Risk Factors Neural Networks, Computer Support Vector Machine Computational Biology Algorithms Longitudinal Studies
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[Machine learning for predictive analyses in health: an example of an application to predict death in the elderly in São Paulo, Brazil].

Cadernos de saude publica Jul 29, 2019
This study aims to present the stages related to the use of machine learning algorithms for predictive analyses in health. An application was performed in a database of elderly residents in the city of São Paulo, Brazil, who participated in the Healt...
Humans Machine Learning Algorithms Prognosis Female Aged Sensitivity and Specificity Brazil ROC Curve Male Logistic Models Death Middle Aged Risk Assessment
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