AIMC Topic: Sample Size
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Application of deep convolutional neural networks in classification of protein subcellular localization with microscopy images.
Genetic epidemiology
Jan 4, 2019
Single-cell microscopy image analysis has proved invaluable in protein subcellular localization for inferring gene/protein function. Fluorescent-tagged proteins across cellular compartments are tracked and imaged in response to genetic or environment...
Direct Feature Evaluation in Black-Box Optimization Using Problem Transformations.
Evolutionary computation
Dec 28, 2018
Exploratory Landscape Analysis provides sample-based methods to calculate features of black-box optimization problems in a quantitative and measurable way. Many problem features have been proposed in the literature in an attempt to provide insights i...
A Robust AUC Maximization Framework With Simultaneous Outlier Detection and Feature Selection for Positive-Unlabeled Classification.
IEEE transactions on neural networks and learning systems
Oct 9, 2018
The positive-unlabeled (PU) classification is a common scenario in real-world applications such as healthcare, text classification, and bioinformatics, in which we only observe a few samples labeled as "positive" together with a large volume of "unla...
The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features.
NeuroImage
Jun 2, 2018
Individualized behavioral/cognitive prediction using machine learning (ML) regression approaches is becoming increasingly applied. The specific ML regression algorithm and sample size are two key factors that non-trivially influence prediction accura...
Chronic obstructive lung disease "expert system": validation of a predictive tool for assisting diagnosis.
International journal of chronic obstructive pulmonary disease
May 28, 2018
PURPOSE: The purposes of this study were development and validation of an expert system (ES) aimed at supporting the diagnosis of chronic obstructive lung disease (COLD).
Forewarning of hypotensive events using a Bayesian artificial neural network in neurocritical care.
Journal of clinical monitoring and computing
May 24, 2018
Traumatically brain injured (TBI) patients are at risk from secondary insults. Arterial hypotension, critically low blood pressure, is one of the most dangerous secondary insults and is related to poor outcome in patients. The overall aim of this stu...
Hypotension
Critical Care
False Positive Reactions
Sample Size
Bayes Theorem
Signal Processing, Computer-Assisted
Male
Middle Aged
Diagnosis, Computer-Assisted
Pilot Projects
Adolescent
Brain Injuries, Traumatic
Brain Injuries
Area Under Curve
Aged, 80 and over
Young Adult
Adult
Humans
Sensitivity and Specificity
Neural Networks, Computer
Intensive Care Units
Machine Learning
Female
Software
Aged
Databases, Factual
Prospective Studies
Using optical tracking for kinematic testing of medical robots.
The international journal of medical robotics + computer assisted surgery : MRCAS
Jan 10, 2018
BACKGROUND: In image-guided robotic interventions, an error component is related to the positioning error of the manipulator. Therefore, measuring the kinematic error is required during robot development. However, no specialized measurement device ex...
Characteristics Associated With Decreased or Increased Mortality Risk From Glycemic Therapy Among Patients With Type 2 Diabetes and High Cardiovascular Risk: Machine Learning Analysis of the ACCORD Trial.
Diabetes care
Dec 26, 2017
OBJECTIVE: Identifying patients who may experience decreased or increased mortality risk from intensive glycemic therapy for type 2 diabetes remains an important clinical challenge. We sought to identify characteristics of patients at high cardiovasc...
Automated seizure detection using limited-channel EEG and non-linear dimension reduction.
Computers in biology and medicine
Jan 25, 2017
Electroencephalography (EEG) is an essential component in evaluation of epilepsy. However, full-channel EEG signals recorded from 18 to 23 electrodes on the scalp is neither wearable nor computationally effective. This paper presents advantages of bo...
A Fuzzy Permutation Method for False Discovery Rate Control.
Scientific reports
Jun 22, 2016
Biomedical researchers often encounter the large-p-small-n situations-a great number of variables are measured/recorded for only a few subjects. The authors propose a fuzzy permutation method to address the multiple testing problem for small sample s...
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