AI Medical Compendium Topic:
Logistic Models

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Comparison of four statistical and machine learning methods for crash severity prediction.

Accident; analysis and prevention
Crash severity prediction models enable different agencies to predict the severity of a reported crash with unknown severity or the severity of crashes that may be expected to occur sometime in the future. This paper had three main objectives: compar...

Sex determination from the femur in Portuguese populations with classical and machine-learning classifiers.

Journal of forensic and legal medicine
The assessment of sex is of paramount importance in the establishment of the biological profile of a skeletal individual. Femoral relevance for sex estimation is indisputable, particularly when other exceedingly dimorphic skeletal regions are missing...

Interaction between SELP genetic polymorphisms with inflammatory cytokine interleukin-6 (IL-6) gene variants on cardiovascular disease in Chinese Han population.

Mammalian genome : official journal of the International Mammalian Genome Society
The aim of the study is to investigate the impact of SELP and IL-6 genetic single-nucleotide polymorphisms (SNPs) and its gene-gene interaction on cardiovascular disease (CVD) risk based on Chinese population. A total of 1082 subjects (519 males, 563...

Identification of immune correlates of protection in Shigella infection by application of machine learning.

Journal of biomedical informatics
BACKGROUND: Immunologic correlates of protection are important in vaccine development because they give insight into mechanisms of protection, assist in the identification of promising vaccine candidates, and serve as endpoints in bridging clinical v...

Antiphospholipid Antibodies and Heart Valve Disease in Systemic Lupus Erythematosus.

The American journal of the medical sciences
Evaluation of antiphospholipid antibodies (aPL) and correlation with heart valve abnormalities among patients with systemic lupus erythematosus (SLE). Nested case-control study was conducted with 70 patients with SLE selected from a longitudinal data...

EEG machine learning for accurate detection of cholinergic intervention and Alzheimer's disease.

Scientific reports
Monitoring effects of disease or therapeutic intervention on brain function is increasingly important for clinical trials, albeit hampered by inter-individual variability and subtle effects. Here, we apply complementary biomarker algorithms to electr...

A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD).

Medical & biological engineering & computing
Major depressive disorder (MDD), a debilitating mental illness, could cause functional disabilities and could become a social problem. An accurate and early diagnosis for depression could become challenging. This paper proposed a machine learning fra...

Estimating psychopathological networks: Be careful what you wish for.

PloS one
Network models, in which psychopathological disorders are conceptualized as a complex interplay of psychological and biological components, have become increasingly popular in the recent psychopathological literature (Borsboom, et. al., 2011). These ...

Laboratory parameter-based machine learning model for excluding non-alcoholic fatty liver disease (NAFLD) in the general population.

Alimentary pharmacology & therapeutics
BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) affects 20%-40% of the general population in developed countries and is an increasingly important cause of hepatocellular carcinoma. Electronic medical records facilitate large-scale epidemiologic...

Dysphonic Voice Pattern Analysis of Patients in Parkinson's Disease Using Minimum Interclass Probability Risk Feature Selection and Bagging Ensemble Learning Methods.

Computational and mathematical methods in medicine
Analysis of quantified voice patterns is useful in the detection and assessment of dysphonia and related phonation disorders. In this paper, we first study the linear correlations between 22 voice parameters of fundamental frequency variability, ampl...