Article...IEEE...International Conference on Connected Health: Applications, Systems and Engineering Technologies. IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies2016
Multiple- vs Non- or Single-Imputation based Fuzzy Clustering for Incomplete Longitudinal Behavioral Intervention Data.
Article in ...IEEE...International Conference on Connected Health: Applications, Systems and Engineering Technologies. IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 18 citations in OpenAlex.
- Federated Choquet Regression with LASSO for Outcome Prediction in Multisite Longitudinal Trial Data.ACM transactions on computing for healthcare · 2026Article
- Feature Interaction Detection in Big Data Through a New Choquet Integral based Deep Neural Network.Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data · 2024Article
- Federated Fuzzy Clustering for Decentralized Incomplete Longitudinal Behavioral Data.IEEE internet of things journal · 2024Article
- Topic modeling for systematic review of visual analytics in incomplete longitudinal behavioral trial data.Smart health (Amsterdam, Netherlands) · 2020Article
- An Enhanced Visualization Method to Aid Behavioral Trajectory Pattern Recognition Infrastructure for Big Longitudinal Data.IEEE transactions on big data · 2018Article
- eFCM: An Enhanced Fuzzy C-Means Algorithm for Longitudinal Intervention Data.International Conference on Computing, Networking, and Communications : [proceedings]. International Conference on Computing, Networking and Communications · 2018Article
- MIFuzzy Clustering for Incomplete Longitudinal Data in Smart Health.Smart health (Amsterdam, Netherlands) · 2017Article
- Multiple Imputation based Clustering Validation (MIV) for Big Longitudinal Trial Data with Missing Values in eHealth.Journal of medical systems · 2016Article
- A New MI-Based Visualization Aided Validation Index for Mining Big Longitudinal Web Trial Data.IEEE access : practical innovations, open solutionsArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Disentangling patients' behavioral variations is a critical step for better understanding an intervention's effects on individual outcomes. Missing data commonly exist in longitudinal behavioral intervention studies. Multiple imputation (MI) has been well studied for missing data analyses in the statistical field, however, has not yet been scrutinized for clustering or unsupervised learning, which are important techniques for explaining the heterogeneity of treatment effects. Built upon previous work on MI fuzzy clustering, this paper theoretically, empirically and numerically demonstrate how MI-based approach can reduce the uncertainty of clustering accuracy in comparison to non-and single-imputation based clustering approach. This paper advances our understanding of the utility and strength of multiple-imputation (MI) based fuzzy clustering approach to processing incomplete longitudinal behavioral intervention data.
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.