AIMC Topic: Logistic Models

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Comparison of artificial neural network and logistic regression models for prediction of outcomes in trauma patients: A systematic review and meta-analysis.

Injury
BACKGROUND: Currently, two models of artificial neural network (ANN) and logistic regression (LR) are known as models that extensively used in medical sciences. The aim of this study was to compare the ANN and LR models in prediction of Health-relate...

Using neuroimaging to predict relapse in stimulant dependence: A comparison of linear and machine learning models.

NeuroImage. Clinical
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...

Real-time crash prediction models: State-of-the-art, design pathways and ubiquitous requirements.

Accident; analysis and prevention
Proactive traffic safety management systems can monitor traffic conditions in real-time, identify the formation of unsafe traffic dynamics, and implement suitable interventions to bring unsafe conditions back to normal traffic situations. Recent adva...

A machine learning approach to investigate potential risk factors for gastroschisis in California.

Birth defects research
BACKGROUND: To generate new leads about risk factors for gastroschisis, a birth defect that has been increasing in prevalence over time, we performed an untargeted data mining statistical approach.

Predicting persistent depressive symptoms in older adults: A machine learning approach to personalised mental healthcare.

Journal of affective disorders
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...

Association of time to colonoscopy after a positive fecal test result and fecal hemoglobin concentration with risk of advanced colorectal neoplasia.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
BACKGROUND: We evaluated the risk of advanced colorectal neoplasia (ACRN) and colorectal cancer (CRC) according to time to colonoscopy after positive fecal immunochemical test (FIT), fecal hemoglobin concentration, and combination of both.

Machine-learning prediction of adolescent alcohol use: a cross-study, cross-cultural validation.

Addiction (Abingdon, England)
BACKGROUND AND AIMS: The experience of alcohol use among adolescents is complex, with international differences in age of purchase and individual differences in consumption and consequences. This latter underlines the importance of prediction modelin...

Machine Learning Models for Genetic Risk Assessment of Infants with Non-syndromic Orofacial Cleft.

Genomics, proteomics & bioinformatics
The isolated type of orofacial cleft, termed non-syndromic cleft lip with or without cleft palate (NSCL/P), is the second most common birth defect in China, with Asians having the highest incidence in the world. NSCL/P involves multiple genes and com...

Application of data mining methods to improve screening for the risk of early gastric cancer.

BMC medical informatics and decision making
BACKGROUND: Although gastric cancer is a malignancy with high morbidity and mortality in China, the survival rate of patients with early gastric cancer (EGC) is high after surgical resection. To strengthen diagnosing and screening is the key to impro...