Evidence map›Paper›PMID 27617297›Full record

ArticleProceedings. IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology2012

Dynamic Task Optimization in Remote Diabetes Monitoring Systems.

Myung-Kyung Suh, Jonathan Woodbridge, Tannaz Moin, Mars Lan, Nabil Alshurafa, Lauren Samy, Bobak Mortazavi, Hassan Ghasemzadeh, Alex Bui, Sheila Ahmadi and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings. IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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

11 authors.

Myung-Kyung SuhComputer Science Department, University of California, Los Angeles.
Jonathan WoodbridgeComputer Science Department, University of California, Los Angeles.
Tannaz MoinDepartment of Medicine, Division of Endocrinology, University of California, Los Angeles; HSR&D Center of Excellence for the Study of Healthcare Provider Behavior, VA Greater Los Angeles.
Mars LanComputer Science Department, University of California, Los Angeles.
Nabil AlshurafaComputer Science Department, University of California, Los Angeles.
Lauren SamyComputer Science Department, University of California, Los Angeles.
Bobak MortazaviComputer Science Department, University of California, Los Angeles.
Hassan GhasemzadehComputer Science Department, University of California, Los Angeles.
Alex BuiMedical Imaging Informatics Group, Department of Radiological Sciences, University of California, Los Angeles.
Sheila AhmadiDepartment of Medicine, Division of Endocrinology, University of California, Los Angeles.
Majid SarrafzadehComputer Science Department, University of California, Los Angeles.

Funding

Training Program for Imaging Based Medical InformaticsT15LM007356 · NLM · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI BUI, ALEX · 2002 to 2011
$5.0M
NLM NIH HHS T15 LM007356
6 · The paper itself

Abstract

Diabetes is the seventh leading cause of death in the United States, but careful symptom monitoring can prevent adverse events. A real-time patient monitoring and feedback system is one of the solutions to help patients with diabetes and their healthcare professionals monitor health-related measurements and provide dynamic feedback. However, data-driven methods to dynamically prioritize and generate tasks are not well investigated in the domain of remote health monitoring. This paper presents a wireless health project (WANDA) that leverages sensor technology and wireless communication to monitor the health status of patients with diabetes. The WANDA dynamic task management function applies data analytics in real-time to discretize continuous features, applying data clustering and association rule mining techniques to manage a sliding window size dynamically and to prioritize required user tasks. The developed algorithm minimizes the number of daily action items required by patients with diabetes using association rules that satisfy a minimum support, confidence and conditional probability thresholds. Each of these tasks maximizes information gain, thereby improving the overall level of patient adherence and satisfaction. Experimental results from applying EM-based clustering and Apriori algorithms show that the developed algorithm can predict further events with higher confidence levels and reduce the number of user tasks by up to 76.19 %.

Indexed as

Apriori algorithmassociation rule miningdiabetesexpectation maximization algorithmreal-time feedbackremote health monitoringtask optimizationtelemedicinewireless health

Identifiers

PMID27617297
PMCPMC5016191

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.