AI Medical Compendium Topic

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Data Interpretation, Statistical

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Skeletal bone age assessments for young children based on regression convolutional neural networks.

Mathematical biosciences and engineering : MBE
Pediatricians and pediatric endocrinologists utilize Bone Age Assessment (BAA) for in-vestigations pertaining to genetic disorders, hormonal complications and abnormalities in the skeletal system maturity of children. Conventional methods dating back...

Extremely Reduced Data Sets Indicate Optimal Stimulation Parameters for Classification in Brain-Computer Interfaces.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The time between the onset of subsequent auditory or visual stimuli - also known as stimulus onset asynchrony (SOA) - determines many of the event-related potential characteristics of the resulting evoked brain signals. Specifically, the SOA value in...

Can Hyperparameter Tuning Improve the Performance of a Super Learner?: A Case Study.

Epidemiology (Cambridge, Mass.)
BACKGROUND: Super learning is an ensemble machine learning approach used increasingly as an alternative to classical prediction techniques. When implementing super learning, however, not tuning the hyperparameters of the algorithms in it may adversel...

Signals Among Signals: Prioritizing Nongenetic Associations in Massive Data Sets.

American journal of epidemiology
Massive data sets are often regarded as a panacea to the underpowered studies of the past. At the same time, it is becoming clear that in many of these data sets in which thousands of variables are measured across hundreds of thousands or millions of...

Common Audiological Functional Parameters (CAFPAs): statistical and compact representation of rehabilitative audiological classification based on expert knowledge.

International journal of audiology
OBJECTIVE: As a step towards objectifying audiological rehabilitation and providing comparability between different test batteries and clinics, the Common Audiological Functional Parameters (CAFPAs) were introduced as a common and abstract representa...

A sparse deep learning model for privacy attack on remote sensing images.

Mathematical biosciences and engineering : MBE
Deep learning tools have been a new way for privacy attacks on remote sensing images. However, since labeled data of privacy objects in remote sensing images are less, the samples for training are commonly small. Besides, traditional deep neural netw...

Causal models adjusting for time-varying confounding-a systematic review of the literature.

International journal of epidemiology
BACKGROUND: Obtaining unbiased causal estimates from longitudinal observational data can be difficult due to exposure-affected time-varying confounding. The past decade has seen considerable development in methods for analysing such complex longitudi...

Statistical Learning Methods to Determine Immune Correlates of Herpes Zoster in Vaccine Efficacy Trials.

The Journal of infectious diseases
Using Super Learner, a machine learning statistical method, we assessed varicella zoster virus-specific glycoprotein-based enzyme-linked immunosorbent assay (gpELISA) antibody titer as an individual-level signature of herpes zoster (HZ) risk in the Z...

PredPsych: A toolbox for predictive machine learning-based approach in experimental psychology research.

Behavior research methods
Recent years have seen an increased interest in machine learning-based predictive methods for analyzing quantitative behavioral data in experimental psychology. While these methods can achieve relatively greater sensitivity compared to conventional u...

Data-Adaptive Estimation for Double-Robust Methods in Population-Based Cancer Epidemiology: Risk Differences for Lung Cancer Mortality by Emergency Presentation.

American journal of epidemiology
In this paper, we propose a structural framework for population-based cancer epidemiology and evaluate the performance of double-robust estimators for a binary exposure in cancer mortality. We conduct numerical analyses to study the bias and efficien...