AIMC Topic: Sample Size
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Bayesian Statistics for Medical Devices: Progress Since 2010.
Therapeutic innovation & regulatory science
Mar 3, 2023
The use of Bayesian statistics to support regulatory evaluation of medical devices began in the late 1990s. We review the literature, focusing on recent developments of Bayesian methods, including hierarchical modeling of studies and subgroups, borro...
Evaluation of a decided sample size in machine learning applications.
BMC bioinformatics
Feb 14, 2023
BACKGROUND: An appropriate sample size is essential for obtaining a precise and reliable outcome of a study. In machine learning (ML), studies with inadequate samples suffer from overfitting of data and have a lower probability of producing true effe...
Predicting dropout from psychological treatment using different machine learning algorithms, resampling methods, and sample sizes.
Psychotherapy research : journal of the Society for Psychotherapy Research
Jan 20, 2023
The occurrence of dropout from psychological interventions is associated with poor treatment outcome and high health, societal and economic costs. Recently, machine learning (ML) algorithms have been tested in psychotherapy outcome research. Dropout...
Maximum Decentral Projection Margin Classifier for High Dimension and Low Sample Size problems.
Neural networks : the official journal of the International Neural Network Society
Oct 22, 2022
Compared with relatively easy feature creation or generation in data analysis, manual data labeling needs a lot of time and effort in most cases. Even if automated data labeling​ seems to make it better in some cases, the labeling results still need ...
Monte Carlo cross-validation for a study with binary outcome and limited sample size.
BMC medical informatics and decision making
Oct 17, 2022
Cross-validation (CV) is a resampling approach to evaluate machine learning models when sample size is limited. The number of all possible combinations of folds for the training data, known as CV rounds, are often very small in leave-one-out CV. Alte...
Hybrid Fine-Tuning Strategy for Few-Shot Classification.
Computational intelligence and neuroscience
Oct 8, 2022
Few-shot classification aims to enable the network to acquire the ability of feature extraction and label prediction for the target categories given a few numbers of labeled samples. Current few-shot classification methods focus on the pretraining st...
On the Rates of Convergence From Surrogate Risk Minimizers to the Bayes Optimal Classifier.
IEEE transactions on neural networks and learning systems
Oct 5, 2022
In classification, the use of 0-1 loss is preferable since the minimizer of 0-1 risk leads to the Bayes optimal classifier. However, due to the nonconvexity of 0-1 loss, this optimization problem is NP-hard. Therefore, many convex surrogate loss func...
Sample Size Calculation for Clinical Trials of Medical Decision Support Systems with Binary Outcome.
Sovremennye tekhnologii v meditsine
May 28, 2022
Currently, software products for use in medicine are actively developed. Among them, the dominant share belongs to clinical decision support systems (CDSS), which can be intelligent (based on mathematical models obtained by machine learning methods o...
Brain Functional Connectivity Analysis via Graphical Deep Learning.
IEEE transactions on bio-medical engineering
Apr 21, 2022
OBJECTIVE: Graphical deep learning models provide a desirable way for brain functional connectivity analysis. However, the application of current graph deep learning models to brain network analysis is challenging due to the limited sample size and c...
Robot-enhanced diabetes care for middle-aged and older adults living with diabetes in the community: A small sample size mixed-method evaluation.
PloS one
Apr 15, 2022
PURPOSE: This study assessed robot-enhanced healthcare in practical settings for the purpose of community diabetes care.
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