ArticleBMC medical informatics and decision making2012
Predicting sample size required for classification performance.
Article in BMC medical informatics and decision making, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 176 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Towards a Wearable Alcohol Biosensor: Examining the Accuracy of BAC Estimates From New-Generation Transdermal Technology Using Large-Scale Human Testing and Machine Learning Algorithms
Multimodal Imaging Biomarkers for Investigating Fascia, Muscle and Vasculature in Myofascial Pain
Who cites it
176 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- A systematic review and meta-analysis of predictive and prognostic models for outcome prediction using positron emission tomography radiomics in head and neck squamous cell carcinoma patients.Cancer medicine · 2023Pooled it
- Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- The Natural Growth of Subsolid Nodules Predicted by Quantitative Initial CT Features: A Systematic Review.Frontiers in oncology · 2020Pooled it
- Prediction of outcome in internet-delivered cognitive behaviour therapy for paediatric obsessive-compulsive disorder: A machine learning approach.International journal of methods in psychiatric research · 2018Trial
- Advancing Tumor Treatment Through Artificial Intelligence and Mathematical Modeling: A Comprehensive Review.Health science reports · 2026Article
- Article
- Bridging the gap from clinical to home ECG: quantifying and overcoming accuracy loss in AI-enabled single-lead ECG models.NPJ digital medicine · 2026Article
- PIGMENT: A deep learning framework for Porcine Immunohistochemistry seGMENTation.bioRxiv : the preprint server for biology · 2026Article
- Sample size calculation for training ensemble machine learning models on health data.Patterns (New York, N.Y.) · 2026Article
- A Deep Learning Approach to Automatically Classify Ice Hockey Shooting Actions Using Acceleration Signals.Sensors (Basel, Switzerland) · 2026Article
- Exploring an Intermediate Colorectal Cancer Screening Test Based on Stool Proteomics and Machine Learning for Optimizing the Selection of Patients for Colonoscopy Identified From FIT.Molecular & cellular proteomics : MCP · 2026Article
- Anticipatory pleasure as a key hedonic component in classifying major depressive disorder: multidimensional behavioral evidence from majority vote algorithm.BMC psychiatry · 2026Article
- Integration of RNA Editing into Multiomics Machine Learning Models for Predicting Drug Responses in Breast Cancer Patients.Biomedicines · 2026Article
- PETIL: Predicting Expansion of Tumor Infiltrating Lymphocytes for the Adoptive Cell Immunotherapy in Bladder Cancers.bioRxiv : the preprint server for biology · 2026Article
- Comparative Evaluation of Encoder- and Decoder-Based Models for Actionable Findings in CT Reports.Journal of imaging informatics in medicine · 2026Article
- Cancer classification with radiomics in controlled preclinical models.Scientific reports · 2026Article
- Machine learning for individual epigenetic fingerprints as predictors of well-being in young adults.Scientific reports · 2026Article
- Cogni-e-SpinDB 1.0: Open Dataset of Electrospinning Parameter Configurations and Resultant Nanofiber Morphologies.Scientific data · 2026Article
- Article
- Psychological inflexibility and resilience in anxiety: insights from machine-learning and robust mediation-based models.Frontiers in psychiatry · 2026Article
116 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSupervised learning methods need annotated data in order to generate efficient models. Annotated data, however, is a relatively scarce resource and can be expensive to obtain. For both passive and active learning methods, there is a need to estimate the size of the annotated sample required to reach a performance target.
methodsWe designed and implemented a method that fits an inverse power law model to points of a given learning curve created using a small annotated training set. Fitting is carried out using nonlinear weighted least squares optimization. The fitted model is then used to predict the classifier's performance and confidence interval for larger sample sizes. For evaluation, the nonlinear weighted curve fitting method was applied to a set of learning curves generated using clinical text and waveform classification tasks with active and passive sampling methods, and predictions were validated using standard goodness of fit measures. As control we used an un-weighted fitting method.
resultsA total of 568 models were fitted and the model predictions were compared with the observed performances. Depending on the data set and sampling method, it took between 80 to 560 annotated samples to achieve mean average and root mean squared error below 0.01. Results also show that our weighted fitting method outperformed the baseline un-weighted method (p < 0.05).
conclusionsThis paper describes a simple and effective sample size prediction algorithm that conducts weighted fitting of learning curves. The algorithm outperformed an un-weighted algorithm described in previous literature. It can help researchers determine annotation sample size for supervised machine learning.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.