Developing a Low Dimensional Patient Class Profile in Accordance to Their Respiration-Induced Tumor Motion

Summary: Introduces adaptive segmentation of respiration-induced tumor-motion signals into multisets of persistent-variation segments and base behaviors. Modified clustering produces low-dimensional patient profiles capturing baseline, ES-range, and D-range shifts, with physician-validated medical characterization. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: 11632
Venue: VLDB
Year: 2017
Pagerank: 5.093636e-05
Overall Rank: 12,005 | 17.64%
DOI: 10.14778/3137765.3137768

Incoming Non-self Citations Over Time

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Authors

1. Rittika Shamsuddin (University of Texas)
2. Amit Sawant (University of Maryland)
3. Balakrishnan Prabhakaran (University of Texas)

BibTeX Citation

@article{shamsuddin_vldb17,
        title = {{Developing a Low Dimensional Patient Class Profile in Accordance to Their Respiration-Induced Tumor Motion}},
        author = {Shamsuddin, Rittika and Sawant, Amit and Prabhakaran, Balakrishnan},
        journal = {PVLDB},
        series = {{VLDB} '17},
        volume = {10},
        number = {12},
        doi = {10.14778/3137765.3137768},
        url = {https://doi.org/10.14778/3137765.3137768},
        year = {2017}
}

Incoming Citations (Sorted by Pagerank)

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Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
1,084	A Data-adaptive and Dynamic Segmentation Index for Whole Matching on Time Series	2013	VLDB	0.00012256753
6,128	GEMINI: An Integrative Healthcare Analytics System	2014	VLDB	5.9668307e-05
7,523	Aggregate Profile Clustering for Telco Analytics	2013	VLDB	5.6029996e-05

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