@conference {ICBO_2018_10,
	title = {ICBO_2018_10: Standardization of the Histopathology Cancer Report: An Ontological Approach},
	booktitle = {International Conference on Biomedical Ontology (ICBO 2018)},
	series = {Proceedings of the International Conference on Biological Ontology (2018)},
	year = {2018},
	month = {08/06/2018},
	publisher = {International Conference on Biological Ontology},
	organization = {International Conference on Biological Ontology},
	abstract = {<p>In recent years, the complexity of cancer pathology reporting has increased significantly. The pathology report covers not only general information such as the presence or absence of cancer, but includes a collection of specific parameters such as tumor size, grade, margin, lymphatic or vascular involvement as well as molecular testing e.g. proteomics and genomics (Figure 1). Soon, biomarkers and immune profiling will play an increasingly more important role in determining the eligibility for particular therapies, along with genetic predisposition and social risk factors. The increased use of digital pathology, which allows streamlined sharing of images, has highlighted the importance of clear communication of the information displayed in the pathology report. In the past years, significant effort has been devoted to redefining the way that histopathology report information is recorded. The College of American Pathologists (CAP) (http://www.cap.org/), a leading organization of board-certified pathologists, introduced synoptic cancer reports, a structured checklist to standardize clinical documentation. Despite continuous improvement and generation of electronic reports, formal representation [1] is still lacking. This lack of standardization limits the ability to integrate pathology information with other genomic and proteomic data and often results in loss of information.</p>
},
	keywords = {Cancer, Histopathology Cancer, Histopathology report, Ontology, pathology, Pathology standard, Tumor},
	url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_10.pdf},
	author = {Anna Maria Masci and Shannon McCall and Alessandro Racioppi and Helena Judge Ellis and Jihad S. Obeid and Barry Smith and Christian Stoeckert and Jie Zheng}
}
@conference {ICBO_2018_12,
	title = {ICBO_2018_12: Transforming and Unifying Research with Biomedical Ontologies: The Penn TURBO project},
	booktitle = {International Conference on Biomedical Ontology (ICBO 2018)},
	series = {Proceedings of the International Conference on Biological Ontology (2018)},
	year = {2018},
	month = {08/06/2018},
	publisher = {International Conference on Biological Ontology},
	organization = {International Conference on Biological Ontology},
	abstract = {<p>The Penn TURBO (Transforming and Unifying Research with Biomedical Ontologies) project aims to accelerate finding and connecting key information from clinical records for research through semantic associations to the processes that generated the clinical data. Major challenges to using clinical data for research are integrating data from different sources which may contain multiple references to the same entity (e.g., person, health care encounter) and incomplete or conflicting information (e.g., gender, BMI). There is also the need to track the provenance of information used when making decisions on what is the actual phenotype of a person. We take a realism-based ontology approach to address these problems through transformation and instantiation of clinical data with an OBO-Foundry based application ontology in a semantic graph database. We have developed an application stack and used it on an 11,237 whole exome sequencing patient cohort capturing key demographics, diagnosis codes, and prescribed medications. The anticipated payoff is to be able to make use of inferencing provided by the semantics to classify and search for instances of people and specimens with desired characteristics.</p>
},
	keywords = {clinical data, diagnosis codes, OBO Foundry, prescriptions, realism-based ontology, referent tracking},
	url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_12.pdf},
	author = {Christian Stoeckert and David Birtwell and Hayden Freedman and Mark Miller and Heather Williams}
}
