SHARP Project Wiki:Project Background

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{{#ev:youtube|qy-d36wAtsU}} SHARP program from Dr. Chuck Friedman Charles P. Friedman, PhD.; Chief Scientific Officer for Information Technology at the Office of the National Coordinator for Health Information Technology (ONC) in the U.S. Department of Health and Human Services speaks about the Strategic Health IT Advanced Research Projects (SHARP) Programs.

Program Organization

SHARP Program Organization

Background

Area 4: Themes & Projects

Area 4: Project Initiation Meeting Slides PDF

Area 4: Project Progress Update Sept 3, 2010

Area 4: 2010 Progress Report (04/01/2010-12/31/2010)


Project Proposal

Abstract

We propose research that will generate a framework of open-source services that can be dynamically configured to transform EHR data into standards-conforming, comparable information suitable for large-scale analyses, inferencing, and integration of disparate health data. We will apply these services to phenotype recognition (disease, risk factor, eligibility, or adverse event) in medical centers and population-based settings. Finally, we will examine data quality and repair strategies with real-world evaluations of their behavior in Clinical and Translational Science Awards (CTSAs), health information exchanges (HIEs), and National Health Information Network (NwHIN) connections.

We have assembled a federated informatics research community committed to open-source resources that can industrially scale to address barriers to the broad-based, facile, and ethical use of EHR data for secondary purposes. We will collaborate to create, evaluate, and refine informatics artifacts that advance the capacity to efficiently leverage EHR data to improve care, generate new knowledge, and address population needs. Our goal is to make these artifacts available to the community of secondary EHR data users, manifest as open-source tools, services, and scalable software. In addition, we have partnered with industry developers who can make these resources available with commercial deployment. We propose to assemble modular services and agents from existing open-source software to improve the utilization of EHR data for a spectrum of use-cases and focus on three themes: Normalization, Phenotypes, and Data Quality/Evaluation. Our six projects span one or more of these themes, though together constitute a coherent ensemble of related research and development. Finally, these services will have open-source deployments as well as commercially supported implementations.

There are six strongly intertwined, mutually dependent projects, including: 1) Semantic and Syntactic Normalization; 2) Natural Language Processing (NLP); 3) Phenotype Applications; 4) Performance Optimization; 5) Data Quality Metrics; and 6) Evaluation Frameworks. The first two projects align with our Data Normalization theme, while Phenotype Applications and Performance Optimization span themes 1 and 2 (Normalization and Phenotyping); while the last two projects correspond to our third theme.

Narrative PDF

Literature_Cited_PDF

Introduction to SHARP

Reports

ONC & PAC Reports

2012 Annual Progress Report (01/01/2012-12/31/2012)

2012 Semi-Annual Progress Report (01/01/2012-06/30/2012)

2011 Annual Progress Report (01/01/2011-12/31/2011)

2011 Semi-Annual Progress Report (01/01/2011-06/30/2011)

Dr. Friedman Site Visit Presentation (09/03/2010)

2010 Annual Progress Report (04/01/2010-12/31/2010)

ARRA Reports

Recovery.gov ARRA Reports

Publications

  1. Aberdeen J. NLP techniques for clinical record de-identification, presentation to AcademyHealth Annual Research Meeting, Seattle, June 12-14, 2011.
  2. Chapman W, Nadkarni P, Hirschman L, D’Avolio L, Savova G, Uzuner O. Overcoming barriers to NLP for clinical text: the role of shared tasks and the need for additional creative solutions. Journal of American Medical Informatics Association. 2011 -:1e4. doi:10.1136/amiajnl-2011-000465.
  3. Choi J, Palmer M. Getting the most out of Transition-based Dependency Parsing, In the Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011, June 19 - 24, 2011, Portland, OR.
  4. Choi J, Palmer M. Transition-based Semantic Role Labeling Using Predicate Argument Clustering, In the Proceedings of RELMS 2011: Relational Models of Semantics, held in conjunction with ACL-HLT 2011, June, 2011, Portland, OR.
  5. Chute CG, Pathak J, Savova GK, Bailey KR, Schor MI, Hart LA, Beebe CE, Huff SM. The SHARPn Project on Secondary Use of Electronic Medical Record Data: Progress, Plans and Possibilities. AMIA 2011 (paper).
  6. Clark C. Recent efforts in clinical NLP: Uncertainty discovery through NLP, presentation to Natural Language Processing Workshop, i2b2 Academic Users Group, Boston, June 28, 2011.
  7. Conway MA, Berg RL, Carrell D, Denny JC, Kho AN, Kullo IJ, Linneman JG, Pacheco JA, Pessig PL, Rasmussen L, Weston N, Chute CG, Pathak J. Analyzing Heterogeneity and Complexity of Electronic Health Record Oriented Phenotyping Algorithms. AMIA 2011 (paper).
  8. Conway MA, Pathak J. Analyzing the Prevalence of Hedges in Electronic Health Record Oriented Phenotyping Algorithms. AMIA 2011 (poster).
  9. Dligach D, Palmer M. Good Seed Makes a Good Crop: Accelerating Active Learning Using Language Modeling. In the Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011, June 19 - 24, 2011, Portland, OR.
  10. Dligach D, Palmer M. Reducing the Need for Double Annotation. In the Proceedings of the Fifth Linguistic Annotation Workshop (LAW V) held in conjunction with ACL-HLT 2011, June, 2011, Portland, OR.
  11. Hirschman L. Evaluation as a driver in Software Communities, presentation to Workshop on Designing an Ecosystem for Clinical NLP, Integrating Data for Analysis, Anonymization and Sharing (iDASH), University of California, San Diego, May 2-3, 2011.
  12. Liu H, Wagholikar K, Wu S. Using SNOMED CT to encode summary level data - a corpus analysis. AMIA CRI 2012.
  13. MITRE System for Clinical Assertion Status Classification, JAMIA 2011; Published Online First: 22 April 2011 doi:10.1136/amiajnl-2011-000164.
  14. Rea S, Pathak J, Savova GK, Oniki TA, Westberg L, Beebe CE, Tao C, Parker CG, Haug PJ, Huff SM, Chute CG. Building a Robust, Scalable and Standards-Driven Infrastructure for Secondary Use of EHR Data: The SHARPn Project. Second stage of review at JAMIA.
  15. Savova G, Olson J, Murphy S, Cafourek V, Couch F, Goetz M, Ingle J, Suman V, Chute C, Weinshilboum R. The electronic medical record and drug response research: automated discovery of drug treatment patterns for endocrine therapy of breast cancer. Journal of American Medical Informatics Association. 2011.
  16. Savova GK, Chapman WW, Elhadad N, Palmer M. 2011. Shared annotated resources for the clinical domain. AMIA ann symp. Panel.
  17. Sohn S, Kocher J-P, Chute CG, Savova GK. Drug side effect extraction from clinical narratives of psychiatry and psychology patients. JAMIA 2011; 18:i144-i149.
  18. Sohn S, Wu S. Dependency Parser-based Negation Detection in Clinical Narratives. AMIA CRI 2012.
  19. Tao C, Parker CG, Oniki TA, Pathak J, Huff SM, Chute CG. An OWL Meta-Ontology for Representing the Clinical Element Model. AMIA 2011 (paper).
  20. Tao C, Welch SR, Wei WQ, Oniki TA, Parker CA, Pathak J, Huff SM, Chute CG. Normalized Representation of Data Elements for Phenotype Cohort Identification in Electronic Health Record. AMIA 2011 (poster).
  21. Torii M, Wagholikar K, Liu H. Using machine learning for concept extraction on clinical documents from multiple data sources. JAMIA 2011 Sep-Oct; 18(5) 580-7
  22. Wagholikar K, Torii M, Jonnalagadda S, Liu H. Feasibility of pooling annotated corpora for clinical concept extraction. AMIA CRI 2011
  23. Wu ST, Kaggal VC, Savova GK, Liu H, Dligach D, Zheng J, Chapman WW, Chute CG. Generality and Reuse in a Common Type System for Clinical Natural Language Processing Proceedings of the First International Workshop on Managing Interoperability and compleXity in Health Systems. Glasgow, Scotland. 2011.
  24. Wu S, Liu H. Semantic Characteristics of NLP-extracted Concepts in Clinical Notes vs. Biomedical Literature Proceedings of the Annual AMIA Fall Symposium. Washington DC. 2011.
  25. Wu S, Liu H, Li D, Tao C, Musen M, Chute CG, Shah N. UMLS Term Occurrences in Clinical Notes: A Large-scale Corpus Analysis. AMIA CRI 2012.
  26. Wu S, Wagholikar K, Sohn S, Kaggal V, Liu H. Empirical Ontologies for Cohort Identification. Text REtrieval Conference. 2011.
  27. Zheng J, Chapman W, Miller T, Lin C, Crowley R, Savova G. In Press. A system for coreference resolution for the clinical narrative. Journal of the American Medical Informatics Association.

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