Evidence map›Paper›PMID 29888298›Full record

ArticleIEEE transactions on big data2018

An Enhanced Visualization Method to Aid Behavioral Trajectory Pattern Recognition Infrastructure for Big Longitudinal Data.

Hua Fang, Zhaoyang Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in IEEE transactions on big data, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. 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 · 2024
    Article
  3. Article
  4. Article
  5. Review
  6. 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 · 2018
    Article
  7. Article
  8. Article
  9. 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 · 2016
    Article
  10. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Hua FangDepartment of Computer and Information Science, Department of Mathematics, University of Massachusetts Dartmouth, 285 Old Westport Rd, Dartmouth, MA, 02747, and Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, 01605.
Zhaoyang ZhangCollege of Engineering, University of Massachusetts Dartmouth and Department of Quantitative Health Sciences, University of Massachusetts Medical School.

Funding

University of Massachusetts Center for Clinical and Translational ScienceUL1TR000161 · NCATS · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI LUZURIAGA, KATHERINE F · 2012 to 2015
$11.0M
UNIVERSITY OF MASSACHUSETTS CENTER FOR CLINICAL AND TRANSLATIONAL SCIENCEUL1RR031982 · NCRR · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI SULLIVAN, JOHN LEWIS · 2010 to 2011
$6.8M
DISC: Describe Smoking Cessation in RCT Multi-Component Behavioral InterventionR01DA033323 · NIDA · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI FANG, HUA · 2013 to 2015
$701k
NCRR NIH HHS UL1 RR031982NIDA NIH HHS R01 DA033323
6 · The paper itself

Abstract

Big longitudinal data provide more reliable information for decision making and are common in all kinds of fields. Trajectory pattern recognition is in an urgent need to discover important structures for such data. Developing better and more computationally-efficient visualization tool is crucial to guide this technique. This paper proposes an enhanced projection pursuit (EPP) method to better project and visualize the structures (e.g. clusters) of big high-dimensional (HD) longitudinal data on a lower-dimensional plane. Unlike classic PP methods potentially useful for longitudinal data, EPP is built upon nonlinear mapping algorithms to compute its stress (error) function by balancing the paired weights for between and within structure stress while preserving original structure membership in the high-dimensional space. Specifically, EPP solves an NP hard optimization problem by integrating gradual optimization and non-linear mapping algorithms, and automates the searching of an optimal number of iterations to display a stable structure for varying sample sizes and dimensions. Using publicized UCI and real longitudinal clinical trial datasets as well as simulation, EPP demonstrates its better performance in visualizing big HD longitudinal data.

Indexed as

Enhanced projection pursuitLongitudinal dataPattern recognitionVisualization

Identifiers

PMID29888298
PMCPMC5990046

What OpenQuestion holds

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Registered trials

None linked

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.