AIMC Topic: Confidence Intervals
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Analyzing the Effectiveness of the Brain-Computer Interface for Task Discerning Based on Machine Learning.
Sensors (Basel, Switzerland)
Apr 23, 2020
The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using ...
Effects of medium- and long-distance running on cardiac damage markers in amateur runners: a systematic review, meta-analysis, and metaregression.
Journal of sport and health science
Dec 27, 2019
BACKGROUND: To finish an endurance race, athletes perform a vigorous effort that induces the release of cardiac damage markers. There are several factors that can affect the total number of these markers, so the aim of this review was to analyze the ...
Investigating cooperation with robotic peers.
PloS one
Nov 20, 2019
We explored how people establish cooperation with robotic peers, by giving participants the chance to choose whether to cooperate or not with a more/less selfish robot, as well as a more or less interactive, in a more or less critical environment. We...
Standard errors and confidence intervals for variable importance in random forest regression, classification, and survival.
Statistics in medicine
Jun 4, 2018
Random forests are a popular nonparametric tree ensemble procedure with broad applications to data analysis. While its widespread popularity stems from its prediction performance, an equally important feature is that it provides a fully nonparametric...
Machine Learning on a Genome-wide Association Study to Predict Late Genitourinary Toxicity After Prostate Radiation Therapy.
International journal of radiation oncology, biology, physics
Jan 31, 2018
PURPOSE: Late genitourinary (GU) toxicity after radiation therapy limits the quality of life of prostate cancer survivors; however, efforts to explain GU toxicity using patient and dose information have remained unsuccessful. We identified patients w...
Urination
Symptom Assessment
Urogenital System
Nocturia
Urination Disorders
Risk
Lower Urinary Tract Symptoms
Urinary Retention
Polymorphism, Single Nucleotide
Regression Analysis
Predictive Value of Tests
Area Under Curve
Confidence Intervals
Genotyping Techniques
Brachytherapy
Prostatic Neoplasms
Androgen Antagonists
Reproducibility of Results
Male
Quality of Life
Genome-Wide Association Study
Humans
Machine Learning
Changes in glomerular filtration rate and outcomes in patients with atrial fibrillation.
American heart journal
Dec 30, 2017
BACKGROUND: Patients with kidney disease are more likely to develop atrial fibrillation (AF) than individuals with normal renal function, and more likely to suffer ischemic stroke (IS)/thromboembolism (TE). We investigated the relationship of kidney ...
Aged
Aged, 80 and over
Male
Middle Aged
Humans
Retrospective Studies
Female
Risk Assessment
Cohort Studies
Confidence Intervals
Treatment Outcome
Glomerular Filtration Rate
Disease Progression
Renal Insufficiency
Follow-Up Studies
Anti-Arrhythmia Agents
Comorbidity
Proportional Hazards Models
Atrial Fibrillation
Fibrinolytic Agents
Severity of Illness Index
Survival Analysis
Drug Therapy, Combination
The use of natural language processing on narrative medication schedules to compute average weekly dose.
Pharmacoepidemiology and drug safety
Sep 16, 2016
PURPOSE: Medications with non-standard dosing and unstandardized units of measurement make the estimation of prescribed dose difficult from pharmacy dispensing data. A natural language processing tool named the SIG extractor was developed to identify...
Ensemble learning of inverse probability weights for marginal structural modeling in large observational datasets.
Statistics in medicine
Oct 15, 2014
Inverse probability weights used to fit marginal structural models are typically estimated using logistic regression. However, a data-adaptive procedure may be able to better exploit information available in measured covariates. By combining predicti...
Constructing Optimal Prediction Intervals by Using Neural Networks and Bootstrap Method.
IEEE transactions on neural networks and learning systems
Sep 10, 2014
This brief proposes an efficient technique for the construction of optimized prediction intervals (PIs) by using the bootstrap technique. The method employs an innovative PI-based cost function in the training of neural networks (NNs) used for estima...
Alzheimer's Disease Classification Based on Multi-feature Fusion.
Current medical imaging reviews
Jan 1, 2019
BACKGROUND: In this study, we investigated the fusion of texture and morphometric features as a possible diagnostic biomarker for Alzheimer's Disease (AD).
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