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An information model for computable cancer phenotypes

Overview of attention for article published in BMC Medical Informatics and Decision Making, September 2016
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Title
An information model for computable cancer phenotypes
Published in
BMC Medical Informatics and Decision Making, September 2016
DOI 10.1186/s12911-016-0358-4
Pubmed ID
Authors

Harry Hochheiser, Melissa Castine, David Harris, Guergana Savova, Rebecca S. Jacobson

Abstract

Standards, methods, and tools supporting the integration of clinical data and genomic information are an area of significant need and rapid growth in biomedical informatics. Integration of cancer clinical data and cancer genomic information poses unique challenges, because of the high volume and complexity of clinical data, as well as the heterogeneity and instability of cancer genome data when compared with germline data. Current information models of clinical and genomic data are not sufficiently expressive to represent individual observations and to aggregate those observations into longitudinal summaries over the course of cancer care. These models are acutely needed to support the development of systems and tools for generating the so called clinical "deep phenotype" of individual cancer patients, a process which remains almost entirely manual in cancer research and precision medicine. Reviews of existing ontologies and interviews with cancer researchers were used to inform iterative development of a cancer phenotype information model. We translated a subset of the Fast Healthcare Interoperability Resources (FHIR) models into the OWL 2 Description Logic (DL) representation, and added extensions as needed for modeling cancer phenotypes with terms derived from the NCI Thesaurus. Models were validated with domain experts and evaluated against competency questions. The DeepPhe Information model represents cancer phenotype data at increasing levels of abstraction from mention level in clinical documents to summaries of key events and findings. We describe the model using breast cancer as an example, depicting methods to represent phenotypic features of cancers, tumors, treatment regimens, and specific biologic behaviors that span the entire course of a patient's disease. We present a multi-scale information model for representing individual document mentions, document level classifications, episodes along a disease course, and phenotype summarization, linking individual observations to high-level summaries in support of subsequent integration and analysis.

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X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 95 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
Unknown 93 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 21%
Student > Master 14 15%
Student > Ph. D. Student 11 12%
Professor > Associate Professor 10 11%
Student > Bachelor 8 8%
Other 15 16%
Unknown 17 18%
Readers by discipline Count As %
Medicine and Dentistry 25 26%
Computer Science 23 24%
Engineering 8 8%
Agricultural and Biological Sciences 4 4%
Biochemistry, Genetics and Molecular Biology 2 2%
Other 9 9%
Unknown 24 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 November 2016.
All research outputs
#7,487,737
of 22,888,307 outputs
Outputs from BMC Medical Informatics and Decision Making
#765
of 1,994 outputs
Outputs of similar age
#113,599
of 321,166 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#14
of 30 outputs
Altmetric has tracked 22,888,307 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,994 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 58% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 321,166 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.