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* [[SEER_Project_Splits| SEER Project Train/Dev/Test Splits]]
* [[SEER_Project_Splits| SEER Project Train/Dev/Test Splits]]
* [[Paper_ideas_2016| Paper Ideas 2016]]
* [[Paper_ideas_2016| Paper Ideas 2016]]
* [[Year2_Goals| Year 2 goals (May 2015-April 20160]]
* [[Year2_Goals| Year 2 goals (May 2015-April 2016)]]
* [[Year3_Goals| Year 3 goals and Publication Ideas (May 2016-April 2017]]
* [[Year3_Goals| Year 3 goals and Publication Ideas (May 2016-April 2017]]
* [[Year4_Goals| Year 4 goals (May 2017-April 2018)]]
* [[Year4_Goals| Year 4 goals (May 2017-April 2018)]]

Revision as of 15:30, 23 May 2017

Public Site

Please visit our Cancer Deep Phenotype (DeepPhe) public site at http://deepphe.healthnlp.org.

Welcome to the Cancer Deep Phenotype Extraction (DeepPhe) project

Welcome to the Cancer Deep Phenotype Extraction (DeepPhe) project.

Cancer is a genomic disease, with enormous heterogeneity in its behavior. In the past, our methods for categorization, prediction of outcome, and treatment selection have relied largely on a morphologic classification of Cancer. But new technologies are fundamentally reframing our views of cancer initiation, progression, metastasis, and response to treatment; moving us towards a molecular classification of Cancer. This transformation depends not only on our ability to deeply investigate the cancer genome, but also on our ability to link these specific molecular changes to specific tumor behaviors. As sequencing costs continue to decline at a supra-Moore’s law rate, a torrent of cancer genomic data is looming. However, our ability to deeply investigate the cancer genome is outpacing our ability to correlate these changes with the phenotypes that they produce. Translational investigators seeking to associate specific genetic, epigenetic, and systems changes with particular tumor behaviors, lack access to detailed observable traits about the cancer (the so called ‘deep phenotype’), which has now become a major barrier to research.

We propose the advanced development and extension of a software platform for performing deep phenotype extraction directly from medical records of patients with Cancer, with the goal of enabling translational cancer research and precision medicine. The work builds on previous informatics research and software development efforts from Boston Children’s Hospital and University of Pittsburgh groups, both individually and together. Multiple software projects developed by our groups (some initially funded by NCI) that have already passed the initial prototyping and pilot development phase (eMERGE, THYME, TIES, ODIE, Apache cTAKES) will be combined and extended to produce an advanced software platform for accelerating cancer research. Previous work in a number of NIH-funded translational science initiatives has already demonstrated the benefits of these methodologies (e.g. Electronic Medical Record and Genomics (eMERGE), PharmacoGenomics Research Network (PGRN), SHARPn, i2b2). However, to date these initiatives have focused exclusively on select non-cancer phenotypes and have had the goal of dichotomizing patients for a particular phenotype of interest (for example, Type II Diabetes, Rheumatoid Arthritis, or Multiple Sclerosis). In contrast, our proposed work focuses on extracting and representing multiple phenotype features for individual patients, to build a cancer phenotype model, relating observable traits over time for individual patients.

Our first four development specific aims significantly extend the capability of our current software, focusing on challenging problems in biomedical information extraction. These aims support the development and evaluation of novel methods for cancer deep phenotype extraction:

Specific Aim 1: Develop methods for extracting phenotypic profiles. Extract patient’s deep phenotypes, and their attributes such as general modifiers (negation, uncertainty, subject) and cancer specific characteristics (e.g. grade, invasion, lymph node involvement, metastasis, size, stage)

Specific Aim 2: Extract gene/protein mentions and their variants from the clinical narrative

Specific Aim 3: Create longitudinal representation of disease process and its resolution. Link phenotypes, treatments and outcomes in temporal associations to create a longitudinal abstraction of the disease

Specific Aim 4: Extract discourses containing explanations, speculations, and hypotheses, to support explorations of causality

Our last two implementation specific aims focus on the design of the software to support the cancer research community, ensuring the usability and utility of our software. These aims support the design, dissemination and sharing of the products of this work to maximize impact on cancer research:

Specific Aim 5: Design and implement a computational platform for deep phenotype discovery and analytics for translational investigators, including integrative visual analytics.

Specific Aim 6: Advance translational research in driving cancer biology research projects in breast cancer, ovarian cancer, and melanoma. Include research community throughout the design of the platform and its evaluation. Disseminate freely available software.

Impact: The proposed work will produce novel methods for extracting detailed phenotype information directly from the EMR, the major source of such data for patients with cancer. Extracted phenotypes will be used in three ongoing translational studies with a precision medicine focus. Dissemination of the software will enhance the ability of cancer researchers to abstract meaningful clinical data for translational research. If successful, systematic capture and representation of these phenotypes from EMR data could later be used to drive clinical genomic decision support.

Who We Are

  • Boston Childrens Hospital/Harvard Medical School
    • Guergana Savova (MPI)
    • Dmitriy Dligach
    • Timothy Miller
    • Sean Finan
    • David Harris
    • Chen Lin
  • University of Pittburgh
    • Rebecca Crowley Jacobson (MPI)
    • Harry Hochheiser
    • Roger Day
    • Adrian Lee
    • Robert Edwards
    • John Kirkwood
    • Kevin Mitchell
    • Eugene Tseytlin
    • Girish Chavan
    • Liz Legowski (through Jan 2015)
    • Melissa Castine


The project described is supported by Grant Number 1U24CA184407-01 from the National Cancer Institute at the US National Institutes of Health. This work is part of the NCI's Informatics Technology for Cancer Research (ITCR) Initiative (http://itcr.nci.nih.gov/) The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

The project period is May 2014 - April, 2019.

Publications and presentations crediting DeepPhe

  • Hochheiser H; Jacobson R; Washington N; Denny J; Savova G. 2015. Natural language processing for phenotype extraction: challenges and representation. AMIA Annual Symposium. Nov 2015, San Francisco, CA.
  • Dmitriy Dligach, Timothy Miller, Guergana K. Savova. 2015. Semi-supervised Learning for Phenotyping Tasks. AMIA Annual Symposium. Nov 2015, San Francisco, CA.
  • Chen, Lin; Dligach, Dmitriy; Miller, Timothy; Bethard, Steven; Savova, Guergana. 2015. Layered temporal modeling for the clinical domain. Journal of the American Medical Informatics Association. http://jamia.oxfordjournals.org/content/early/2015/10/31/jamia.ocv113
  • Chen, Lin; Miller, Timothy; Dligach, Dmitriy; Bethard, Steven; Savova, Guergana. 2016. Improving Temporal Relation Extraction with Training Instance Augmentation. BioNLP workshop at the Association for Computational Linguistics conference. Berlin, Germany, Aug 2016
  • Hochheiser, Harry; Castine, Melissa; Harris, David; Savova, Guergana; Jacobson, Rebecca. 2016. An Information Model for Cancer Phenotypes. BMC Medical Informatics and Decision Making. https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-016-0358-4
  • Ethan Hartzell, Chen Lin. 2016. Enhancing Clinical Temporal Relation Discovery with Syntactic Embeddings from GloVe. International Conference on Intelligent Biology and Medicine (ICIBM 2016). December 2016, Houston, Texas, USA
  • Dligach, Dmitriy; Miller, Timothy; Lin, Chen; Bethard, Steven; Savova, Guergana. 2017. Neural temporal relation extraction. European Chapter of the Association for Computational Linguistics (EACL 2017). April 3-7, 2017. Valencia, Spain.

DeepPhe Software

The DeepPhe system will be available as part of Apache cTAKES at http://ctakes.apache.org/. It is also available at https://github.com/DeepPhe/DeepPhe.

DeepPhe software components will also be deployed in the TIES Software System for sharing and accessing deidentified NLP-processed data with tissue(http://ties.pitt.edu/) which is deployed as part of the TIES Cancer Tissue Network (TCRN) across multiple US Cancer Centers.

DeepPhe software development will be coordinated as per software development policies.

DeepPhe software documentation for developers is available in

DeepPhe Gold Set

Qualitative Interviews

Project materials/ WIKIs to tasks



Scrum Sprints

Meeting Notes


Licensing policies for DeepPhe software and ontological models.


If you need assistance or if you have further questions about the project, feel free to e-mail Guergana.Savova@childrens.harvard.edu or to Rebecca Crowley Jacobson at rebeccaj@pitt.edu.

Getting started

Consult the User's Guide for information on using the wiki software.