Evidence map›Paper›PMID 33344744›Full record

ArticleSmart health (Amsterdam, Netherlands)2020

Topic modeling for systematic review of visual analytics in incomplete longitudinal behavioral trial data.

Joshua Rumbut, Hua Fang, Honggong Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Smart health (Amsterdam, Netherlands), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Joshua RumbutDepartment of Computer and Information Science, University of Massachusetts Dartmouth, North Dartmouth, MA, 02747, USA.
Hua FangDepartment of Computer and Information Science, University of Massachusetts Dartmouth, North Dartmouth, MA, 02747, USA.
Honggong WangDepartment of Electrical and Computer Engineering, University of Massachusetts Dartmouth, North Dartmouth, MA, 02747, USA.

Funding

iPAT:Intelligent Diet Quality Pattern Analysis for Harmonized MA-National TrialsR01DK129432 · NIDDK · YESHIVA UNIVERSITY · PI FANG, HUA · 2021 to 2025
$2.7M
DISC: Describe Smoking Cessation in RCT Multi-Component Behavioral InterventionR01DA033323 · NIDA · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI FANG, HUA · 2013 to 2015
$701k
VIP:Visual-Valid Dietary Behavior Pattern Recognition for Local-National TrialsR56DK114514 · NIDDK · UNIVERSITY OF MASSACHUSETTS DARTMOUTH · PI FANG, HUA · 2019 to 2019
$452k
NIDA NIH HHS R01 DA033323NIDDK NIH HHS R01 DK129432NIDDK NIH HHS R56 DK114514
6 · The paper itself

Abstract

Longitudinal observational and randomized controlled trials (RCT) are widely applied in biomedical behavioral studies and increasingly implemented in smart health systems. These trials frequently produce data that are high-dimensional, correlated, and contain missing values, posing significant analytic challenges. Notably, visual analytics are underdeveloped in this area. In this paper, we developed a longitudinal topic model to implement the systematic review of visual analytic methods presented at the IEEE VIS conference over its 28 year history, in comparison with MIFuzzy, an integrated and comprehensive soft computing tool for behavioral trajectory pattern recognition, validation, and visualization of incomplete longitudinal data. The findings of our longitudinal topic modeling highlight the trend patterns of visual analytics development in longitudinal behavioral trials and underscore the gigantic gap of existing robust visual analytic methods and actual working algorithms for longitudinal behavioral trial data. Future research areas for visual analytics in behavioral trial studies and smart health systems are discussed.

Indexed as

MIFuzzyMissing dataSystematic reviewTopic modelingVisual analyticsVisualization

Identifiers

PMID33344744
PMCPMC7745978

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Registered trials

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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.