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An information model for computable cancer phenotypes.

Harry Hochheiser1,2, Melissa Castine3, David Harris4

  • 1Department of Biomedical Informatics, University of Pittsburgh School of Medicine, 5607 Baum Boulevard, Rm 523, Pittsburgh, 15206-3701, PA, USA. harryh@pitt.edu.

BMC Medical Informatics and Decision Making
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Summary
This summary is machine-generated.

A new information model, DeepPhe, integrates cancer clinical and genomic data. It enables detailed patient phenotyping, supporting precision medicine and cancer research by summarizing complex data over time.

Keywords:
CancerDeep phenotypingInformation extractionInformation model

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Precision Medicine

Background:

  • Integrating clinical and genomic data for cancer research presents significant challenges due to data volume, complexity, and heterogeneity.
  • Existing information models lack the expressiveness to represent individual observations and longitudinal summaries of cancer patient data.
  • The manual process of generating clinical "deep phenotypes" hinders advancements in cancer research and precision medicine.

Purpose of the Study:

  • To develop an advanced information model for integrating diverse cancer data types.
  • To create a system that supports the generation of comprehensive "deep phenotypes" for individual cancer patients.
  • To facilitate the analysis and integration of clinical and genomic information in oncology.

Main Methods:

  • Iterative development of a cancer phenotype information model informed by literature reviews and expert interviews.
  • Translation of Fast Healthcare Interoperability Resources (FHIR) models into OWL 2 Description Logic (DL) with extensions.
  • Validation of the model with domain experts and evaluation against competency questions using NCI Thesaurus terms.

Main Results:

  • Introduction of the DeepPhe Information model, representing cancer phenotype data at multiple abstraction levels.
  • Demonstration of the model's capability to represent phenotypic features, treatment regimens, and biologic behaviors across a patient's disease course, using breast cancer as an example.
  • The model effectively links individual data points to high-level summaries for enhanced integration and analysis.

Conclusions:

  • A novel multi-scale information model is presented for comprehensive cancer data representation.
  • The model facilitates the integration and analysis of clinical and genomic data by linking detailed observations to high-level summaries.
  • This approach supports the development of tools for generating deep cancer phenotypes, advancing precision medicine and research.