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Subject-Aware Model Validation for Repeated-Measures Data: A Nested Approach for Trustworthy Medical Ai Applications
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Establishment of Best Practices for Evidence for Prediction
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Experimental evaluation of genomic selection prediction for rust resistance in sugarcane
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Random Effects Models for Longitudinal Data
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On Splitting Training and Validation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning
inferredcites →https://doi.org/10.1016/j.gaitpost.2008.09.003openalex-snapshot
The reliability of three-dimensional kinematic gait measurements: A systematic review
inferredcites →https://doi.org/10.1016/j.ins.2009.12.010openalex-snapshot
Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power
inferredcites →https://doi.org/10.1016/j.jbiomech.2018.09.009openalex-snapshot
Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities
inferredcites →https://doi.org/10.1016/j.jclinepi.2015.04.005openalex-snapshot
Prediction models need appropriate internal, internal–external, and external validation
inferredcites →https://doi.org/10.1016/j.media.2025.103504openalex-snapshot
Navigating prevalence shifts in image analysis algorithm deployment
inferredcites →https://doi.org/10.1016/j.neuroimage.2016.10.038openalex-snapshot
Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines
inferredcites →https://doi.org/10.1038/s41746-019-0207-9openalex-snapshot
Photoplethysmography based atrial fibrillation detection: a review
inferredcites →https://doi.org/10.1044/2023_jslhr-23-00273openalex-snapshot
Toward Generalizable Machine Learning Models in Speech, Language, and Hearing Sciences: Estimating Sample Size and Reducing Overfitting
inferredcites →https://doi.org/10.1080/14763141.2018.1538385openalex-snapshot
A novel standardised side hop test reliably evaluates landing mechanics for anterior cruciate ligament reconstructed persons and controls
inferredcites →https://doi.org/10.1093/gigascience/gix019openalex-snapshot
The need to approximate the use-case in clinical machine learning
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Fear of Reinjury Following Anterior Cruciate Ligament Reconstruction Is Manifested in Muscle Activation Patterns of Single-Leg Side-Hop Landings
inferredcites →https://doi.org/10.1111/iwj.14607openalex-snapshot
Machine learning application for prediction of surgical site infection after posterior cervical surgery
inferredcites →https://doi.org/10.1117/1.jmi.10.5.051804openalex-snapshot
Regulatory considerations for medical imaging AI/ML devices in the United States: concepts and challenges
inferredcites →https://doi.org/10.1136/bmj.q902openalex-snapshot
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
inferredcites →https://doi.org/10.1145/3469266.3473711openalex-snapshot
Advancing the science of digital biomarkers
inferredcites →https://doi.org/10.1155/2024/7858835openalex-snapshot
Longitudinal Monitoring of Biomechanical and Psychological State in Collegiate Female Basketball Athletes Using Principal Component Analysis
inferredcites →https://doi.org/10.1161/strokeaha.118.024293openalex-snapshot
Machine Learning–Based Model for Prediction of Outcomes in Acute Stroke
inferredcites →https://doi.org/10.11648/j.ajtas.20241305.13openalex-snapshot
Comparative Analysis of Cross-Validation Techniques: LOOCV, K-folds Cross-Validation, and Repeated K-folds Cross-Validation in Machine Learning Models
inferredcites →https://doi.org/10.1177/1120672119864554openalex-snapshot
Repeatability and reproducibility of corneal biomechanical parameters derived from Corvis ST
inferredcites →https://doi.org/10.1186/1471-2105-7-91openalex-snapshot
Bias in error estimation when using cross-validation for model selection
inferredcites →https://doi.org/10.1186/1758-2946-6-10openalex-snapshot
Cross-validation pitfalls when selecting and assessing regression and classification models
inferredcites →https://doi.org/10.1214/09-ss054openalex-snapshot
A survey of cross-validation procedures for model selection
inferredcites →https://doi.org/10.12688/f1000research.55027.1openalex-snapshot
rmcorrShiny: A web and standalone application for repeated measures correlation
inferredcites →https://doi.org/10.1371/journal.pone.0224365openalex-snapshot
Machine learning algorithm validation with a limited sample size
inferredcites →https://doi.org/10.1371/journal.pone.0236092openalex-snapshot
Challenges of machine learning model validation using correlated behaviour data: Evaluation of cross-validation strategies and accuracy measures
inferredcites →https://doi.org/10.2147/idr.s417431openalex-snapshot
Using Machine Learning to Predict Surgical Site Infection After Lumbar Spine Surgery
inferredcites →https://doi.org/10.2196/49023openalex-snapshot
Practical Considerations and Applied Examples of Cross-Validation for Model Development and Evaluation in Health Care: Tutorial
inferredcites →https://doi.org/10.2478/hukin-2018-0064openalex-snapshot
Inter-Session Reliability of the Tennis Serve and Influence of the Laboratory Context
inferredcites →https://doi.org/10.3389/fbioe.2022.863240openalex-snapshot
In Vivo Corneal Biomechanical Properties in a Selected Chinese Population, Measured Using the Corneal Visualization Scheimpflug Technology
inferredcites →https://doi.org/10.3389/fspor.2023.1113952openalex-snapshot
Increasing breast support is associated with altered knee joint stiffness and contributing knee joint biomechanics during treadmill running
inferredcites →https://doi.org/10.48550/arxiv.1712.03120openalex-snapshot
Learning Disease vs Participant Signatures: a permutation test approach to detect identity confounding in machine learning diagnostic applications
inferredcites →https://doi.org/10.5555/1756006.1859921openalex-snapshot
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
inferredcites →https://doi.org/10.5555/1953048.2078195openalex-snapshot
Scikit-learn: Machine Learning in Python
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High spatial resolution analysis using automated indentation mapping differentiates biomechanical properties of normal vs. degenerated articular cartilage in mice
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/s00203-026-05209-6openalex-snapshot
Artificial intelligence in human microbiome research: from data integration to explainable translation
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