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Welcome to the Cancer Deep Phenotype Extraction (DeepPhe) project

Who We Are

  • Boston Childrens Hospital/Harvard Medical School
    • Guergana Savova (PI)
    • Timothy Miller
    • Sean Finan
    • David Harris
    • Chen Lin
    • past members -- Dmitriy Dligach (currently faculty at Loyola University, Chicago), Pei Chen, James Masanz
  • University of Pittburgh
    • Harry Hochheiser (site PI)
    • Zhou Yuan
    • past members - through June 2017: Rebecca Crowley Jacobson (MPI), Roger Day, Adrian Lee, Robert Edwards, John Kirkwood, Kevin Mitchell, Eugene Tseytlin, Girish Chavan, Melissa Castine; Liz Legowski (through Jan 2015), Olga Medvedeva, Mike Davis
  • Vanderbilt University
    • Jeremy Warner (site PI)
    • Alicia Beeghly-Fadiel
  • Dana-Farber Cancer Institute
    • Elizabeth Buchbinder

Funding

The project described is supported by the National Cancer Institute at the US National Institutes of Health. It 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.

Publications and presentations crediting DeepPhe

  1. 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.
  2. Dmitriy Dligach, Timothy Miller, Guergana K. Savova. 2015. Semi-supervised Learning for Phenotyping Tasks. AMIA Annual Symposium. Nov 2015, San Francisco, CA.
  3. Lin, Chen; 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
  4. Lin, Chen; 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
  5. Timothy A. Miller, Sean Finan, Dmitriy Dligach, Guergana Savova. Robust Sentence Segmentation for Clinical Text. Abstract presented at the Annual Symposium of the American Medical Informatics Association, San Francisco, CA, 2015.
  6. 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
  7. 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
  8. 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.
  9. Lin, Chen; Miller, Timothy; Dligach, Dmitriy; Bethard, Steven; Savova, Guergana. 2017. Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks. BioNLP workshop at the Association for Computational Linguistics conference. Vancouver, Canada, Friday August 4, 2017
  10. Timothy A. Miller, Dmitriy Dligach, Chen Lin, Steven Bethard, Guergana Savova. Feature Portability in Cross-domain Clinical Coreference. Abstract presented at the Annual Symposium of the American Medical Informatics Association, Chicago, IL, 2016.
  11. Castro SM, Tseytlin E, Medvedeva O, Mitchell K, Visweswaran S, Bekhuis T, Jacobson RS. 2017. Automated annotation and classification of BI-RADS assessment from radiology reports. J Biomed Inform. 2017 May;69:177-187. doi: 10.1016/j.jbi.2017.04.011. PMID: 28428140; PMCID: PMC5706448 [Available on 2018-05-01] DOI:10.1016/j.jbi.2017.04.011
  12. Timothy A. Miller, Steven Bethard, Hadi Amiri, Guergana Savova. Unsupervised Domain Adaptation for Clinical Negation Detection. Proceedings of the 16th Workshop on Biomedical Natural Language Processing. 2017.
  13. Timothy A. Miller, Dmitriy Dligach, Steven Bethard, Chen Lin, and Guergana Savova. Towards generalizable entity-centric coreference resolution. Journal of Biomedical Informatics, 69; 251-258. 2017.
  14. Lin, Chen; Miller, Timothy; Dligach, Dmitriy; Bethard, Steven; Savova, Guergana. 2017. Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks. BioNLP workshop at the Association for Computational Linguistics conference. Vancouver, Canada, Friday August 4, 2017
  15. Miller, T; Bethard, S; Amiri, H; Savova, G. 2017. Unsupervised Domain Adaptation for Clinical Negation Detection. BioNLP workshop at the Association for Computational Linguistics conference. Vancouver, Canada, Friday August 4, 2017
  16. Savova, G., Tseytlin, E., Finan, S., Castine, M., Miller, T., Medvedeva, O., Haris, D., Hochheiser, H., Lin, C., Chavan, G., Jacobson R. 2017. DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records Cancer Research 77(21), November 2017 DOI: 10.1158/0008-5472.CAN-17-0615.
  17. Savova, G., Tseytlin, E., Finan, S., Castine, M., Miller, T., Medvedeva, O., Haris, D., Hochheiser, H., Lin, C., Chavan, G., Jacobson R. 2017. DeepPhe - A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records. Annual Symposium of the American Medical Informatics Association (AMIA). Nov 2017. Washington DC.
  18. Savova, G; Miller, T. 2018. DeepPhe and Extraction of Oncology Patient Phenotypes from Unstructured Text Using NLP and Other AI Tools. Presentation to Dana Farber Cancer Institute. January 24 2018. Boston, MA.
  19. Warner, Jeremy. 2018. Improving Cancer Diagnosis and Care: Patient Access to Oncologic Imaging and Pathology Expertise and Technologies. the National Cancer Policy Forum of the National Academies of Sciences, Engineering, and Medicine. http://www.nationalacademies.org/hmd/Activities/Disease/NCPF/2018-FEB-12/Videos/Session%204%20Videos/32%20Warner.aspx

DeepPhe Software

DeepPhe software documentation for developers is available in

DeepPhe Gold Set

  • Process for Deidentification of Source Documents.
  • Process for Deidentification of Source Documents.
  • Process for Deidentification of Source Documents.
  • Process for Selection of Gold Set Source Documents.
  • DeepPhe UPMC Training/Development/Test splits
    • training set:
      • all documents for Breast Cancer patients 03, 11, 92, 93 for a total of 48 documents (in BCH \\rc-fs\chip-nlp\Public\DeepPhe\DeepPheDatasets\breast\UPMCextendedDev); gold annotations are \\rc-fs\chip-nlp\Public\DeepPhe\DeepPheDatasets\breast\UPMCextendedDev\DeepPhe Gold Phenotype Annotations_v2.xlsm
      • all documents for Breast Cancer patients extended 04,05,06,09,10,12,13,14,18,19,20,22,23,26,27,30,31,32,33,34,35,40,41,42,43,38,39,46,47 for a total of 954 documents (in BCH \\rc-fs\chip-nlp\Public\DeepPhe\DeepPheDatasets\breast\UPMCextendedDev); gold annotations are \\rc-fs\chip-nlp\Public\DeepPhe\DeepPheDatasets\breast\UPMCextendedDev\DeepPhe Gold Phenotype Annotations_v2.xlsm
      • all documents for Melanoma patients 05, 06, 18, 19, 25, 28, 30, 33, 34, 42, for a total of 233 documents (in BCH \\rc-fs\chip-nlp\Public\DeepPhe\DeepPheDatasets\melanoma); gold annotations are \\rc-fs\chip-nlp\Public\DeepPhe\DeepPheDatasets\melanoma\trainSet\DeepPhe DevSet Phenotype Annotations.xlsm
      • all documents for Ovarian Cancer patients 3, 4, 7, 8, 12, 13, 16, 17, 18, 20, 24, 25, 26, 27, 30, 31, 32, 34, 37, 38, 41, 42, 43, 44, 46, 48 for a total of 1675 documents (in BCH \\rc-fs\chip-nlp\Public\DeepPhe\DeepPheDatasets\ovarian\final_dataset\trainSet); gold annotations are \\rc-fs\chip-nlp\Public\DeepPhe\DeepPheDatasets\ovarian\final_dataset\trainSet\DeepPhe_ovCa_Train_Set_Phenotype_Annotations_GOLD.xlsm
    • development set:
      • all documents for Breast Cancer patients 02, 21 for a total of 42 documents (in BCH \\rc-fs\chip-nlp\Public\DeepPhe\DeepPheDatasets\breast\UPMCextendedDev); gold annotations are \\rc-fs\chip-nlp\Public\DeepPhe\DeepPheDatasets\breast\UPMCextendedDev\DeepPhe Gold Phenotype Annotations_v2.xlsm
      • all documents for Breast Cancer patients extended 01,15,16,17,28,29,36,37,44,45,07,08,24,25 for a total of 457 documents (in BCH \\rc-fs\chip-nlp\Public\DeepPhe\DeepPheDatasets\breast\UPMCextendedDev); gold annotations are \\rc-fs\chip-nlp\Public\DeepPhe\DeepPheDatasets\breast\UPMCextendedDev\DeepPhe Gold Phenotype Annotations_v2.xlsm
      • all documents for Melanoma patients 07, 32, 43 for a total of 215 (processed only 211 docs) documents (in BCH \\rc-fs\chip-nlp\Public\DeepPhe\DeepPheDatasets\melanoma\devSet); gold annotations are \\rc-fs\chip-nlp\Public\DeepPhe\DeepPheDatasets\melanoma\devSet\DeepPhe DevSet Phenotype Annotations.xlsm
      • all documents for Ovarian Cancer patients 9, 11, 19, 28, 29, 35, 39, 47 for a total of 562 documents (in BCH \\rc-fs\chip-nlp\Public\DeepPhe\DeepPheDatasets\ovarian\final_dataset\devSet); gold annotations are \\rc-fs\chip-nlp\Public\DeepPhe\DeepPheDatasets\ovarian\final_dataset\devSet\DeepPhe_ovCa_Dev_Set_Phenotype_Annotations_GOLD.xlsm
    • test set:
      • all documents for Breast Cancer patients 01 (in BCH \\rc-fs\chip-nlp\Public\DeepPhe\DeepPheDatasets\breast\UPMCextendedTest); gold annotations are \\rc-fs\chip-nlp\Public\DeepPhe\DeepPheDatasets\breast\UPMCextendedTest\DeepPhe Test Phenotype Annotations v2.xlsm
      • all documents for Breast Cancer extended for patients 01, 02, 09,10,12,15,17,18,19,20,23,24,27,32,36,39,44,63, 76, 100, 101, 104, 106, 109, 111, 114, 115, 117, 118, 119, 120, 121, 123, 125, 126, 129, 130, 132, 136, 137, 138, 142, 143, 155, 156, 158, 174, 181, 189, 197 for phenotyping level testing use (\\rc-fs\chip-nlp\Public\DeepPhe\DeepPheDatasets\breast\UPMCextendedTest\); gold annotations are \\rc-fs\chip-nlp\Public\DeepPhe\DeepPheDatasets\breast\UPMCextendedTest\DeepPhe Test Phenotype Annotations v2.xlsm
      • all documents for Melanoma patients 02, 03, 11, 12, 14, 16, 24, 27, 41, 44 for a total of 229 documents (in BCH \\rc-fs\chip-nlp\Public\DeepPhe\DeepPheDatasets\melanoma\testSet); gold annotations are \\rc-fs\chip-nlp\Public\DeepPhe\DeepPheDatasets\melanoma\testSet\DeepPhe TestSet Phenotype Annotations.xlsm
      • all documents for Ovarian Cancer patients 15, 21, 33, 36, 40, 45, 49, 50 for a total of 559 documents (in BCH \\rc-fs\chip-nlp\Public\DeepPhe\DeepPheDatasets\ovarian\final_dataset\testSet); gold annotations are in \\rc-fs\chip-nlp\Public\DeepPhe\DeepPheDatasets\ovarian\final_dataset\testSet\DeepPhe_ovCa_Test_Set_Phenotype_Annotations_GOLD.xlsm
    • use the training set for developing the algorithms and the development set to report results and error analysis. The test set will be used only for the final evaluation to go in publications.
  • SEER Project Train/Dev/Test Splits
  • Clinical Genomics Gold Set

Qualitative Interviews


Project materials/ WIKIs to tasks


Presentations

Communication

Scrum Sprints

Meeting Notes

Licensing

Licensing policies for DeepPhe software and ontological models.

Contact

If you need assistance or if you have further questions about the project, contact us at the DeepPhe group.

Getting started

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

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