ArticleIEEE access : practical innovations, open solutions
A New MI-Based Visualization Aided Validation Index for Mining Big Longitudinal Web Trial Data.
Article in IEEE access : practical innovations, open solutions. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 22 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
- Acculturation, Depression, and Smoking Cessation: a trajectory pattern recognition approach.Tobacco induced diseases · 2017Article
- Multiple Imputation based Clustering Validation (MIV) for Big Longitudinal Trial Data with Missing Values in eHealth.Journal of medical systems · 2016Article
- Multiple- vs Non- or Single-Imputation based Fuzzy Clustering for Incomplete Longitudinal Behavioral Intervention Data....IEEE...International Conference on Connected Health: Applications, Systems and Engineering Technologies. IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Web-delivered clinical trials generate big complex data. To help untangle the heterogeneity of treatment effects, unsupervised learning methods have been widely applied. However, identifying valid patterns is a priority but challenging issue for these methods. This paper, built upon our previous research on multiple imputation (MI)-based fuzzy clustering and validation, proposes a new MI-based Visualization-aided validation index (MIVOOS) to determine the optimal number of clusters for big incomplete longitudinal Web-trial data with inflated zeros. Different from a recently developed fuzzy clustering validation index, MIVOOS uses a more suitable overlap and separation measures for Web-trial data but does not depend on the choice of fuzzifiers as the widely used Xie and Beni (XB) index. Through optimizing the view angles of 3-D projections using Sammon mapping, the optimal 2-D projection-guided MIVOOS is obtained to better visualize and verify the patterns in conjunction with trajectory patterns. Compared with XB and VOS, our newly proposed MIVOOS shows its robustness in validating big Web-trial data under different missing data mechanisms using real and simulated Web-trial 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.