1 / 11

Enabling Medical Experts to navigate clinical text for cohort identification (meTAKES)

Enabling Medical Experts to navigate clinical text for cohort identification (meTAKES). Stephen Wu, Mayo Clinic SHARPn Summit 2012 June 12, 2012. Outline. Motivation Methods (current) System architecture Data retrieval Search Cohort management Conclusion & Future Work. Motivation.

rimona
Download Presentation

Enabling Medical Experts to navigate clinical text for cohort identification (meTAKES)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Enabling Medical Experts to navigate clinical text for cohort identification (meTAKES) Stephen Wu, Mayo Clinic SHARPn Summit 2012 June 12, 2012

  2. Outline • Motivation • Methods (current) • System architecture • Data retrieval • Search • Cohort management • Conclusion & Future Work

  3. Motivation Somali patients (unique terms) Drug-induced liver injury (rel’ns) Pediatric asthma (temporal) • Clinical NLP out-of-the-box • Comprehensive knowledge • Customize? Collaborate! • Diverse requirements • Physician/Researcher tasks • Enroll patients in study • Define retrospective cohort • Case abstraction

  4. “Medical expert”-driven NLP • Use case-agnostic • Comprehensive • Pre-computed NLP • Known requirements • Use case-specific • Streamlined • On-the-fly NLP • Diverse requirements • Interactive interface • Delivery mechanism • Available data vs. expert knowledge source text semantics expert criteria

  5. Web interface (GWT) Client Server EHR data pool parameters GUI records NLP (MedTagger) query query parser cohort mgmt Lucene cohort manipulation records ranked records

  6. Data retrieval • Parameters (current) • Patient ID • Date • Sources (current) • Enterprise Data Trust (EDT) @ Mayo Clinic • Text files on server

  7. Search • Parameters (current) • Term lists • Logic • Expansion • Techniques (current) • Dictionary (Lucene) • NLP results (e.g., negation)

  8. Cohort Management • Parameters: • Cohort name/tag • Selecting patients • Export • Iterative refinement

  9. Conclusion and Future Work • NLP / search • Text characteristics • Semantic search • Relationships • HCI / cohort management • Learning • Collaboration • Interoperability • Structured data • API • Mayo delivery: DDQB Clinical Notes Search Tool Evaluation framework

  10. meTAKES team: Stephen Wu Ravikumar K.E. Hongfang Liu Special thanks to: Siddhartha Jonnalagadda James Masanz Vinod Kaggal Sean Murphy Tom Suther Erik Voldal Carlos Garcia Melissa Gregg This work was supported in part by the SHARPn (Strategic Health IT Advanced Research Projects) Area 4: Secondary Use of EHR Data Cooperative Agreement from the HHS Office of the National Coordinator, Washington, DC. DHHS 90TR000201. https://sites.google.com/site/stephentzeinnwu wu.stephen@mayo.edu Thank you.

More Related