1. Applying dependency parses and SRL: Subject and Generic Attribute Discovery Stephen Wu, Mayo Clinic SHARPn Summit 2012 June 11, 2012

2. Outline Motivation and Role Generic Attribute Definition Methods & Examples Subject Attribute Definition Methods & Examples Status & Future Work

3. Attribute Discovery Clinical Element Models Exclude generic Family history Methods: Dependency Parser and SRL

4. Methods summary Types of rules Noun phrase structure Path to root Path between pairs Semantic arguments Feature vector Decision logic/ML

5. (a) The patient was referred to the Lupus clinic. (b) We discussed increased risk of breast cancer Definition : “refers to mentions, which are generic, i.e., not related to the instance of a disorder, sign/symptom, etc…” “… Mentioned as part of a general statement with no clear subject/experiencer.” Values: in {true, false}default=false Generic: Attribute Definition

6. Ex: Noun phrase structure Rule (a) The patient was referred to the Lupus clinic. Find the headword of the NE Modifies another noun (nmod)? Generic: Dependency parse rules referred patient was sbj adv nmod to the pmod vc nmod clinic the nmod Lupus generic=true

7. Ex: Path to root Rule (“Discussion” context) (b) We discussed increased risk of breast cancer Find NE headword Path to top “Discussion” word? Generic: Dependency parse rules increased discussed sbjnmod risk We pmod obj of breast nmod cancer discuss, ask, understand, understood, tell, told, mention, talk, speak, spoke, address generic=true

8. (c) The patient’s son has schizophrenia. (d) Father died of MI in 50’s Definition : “The person the observation is on. This modifier refers to the entity experiencing the disorder.” Values: in {Patient, Family_Member, default=Patient Donor_Family_Member, Donor_Other, and Other} Subject: Attribute Definition

9. Ex: Semantic argument Rule (c) The patient’s son has schizophrenia. Semantic argument (ARG0, ARG1) Family term (WordNet) Subject: Semantic role labeling rules ‘s patient has PRED the schizophrenia ARG1 subject=family_member son ARG0 father, dad, mother, mom, bro, sis, sib, cousin, aunt, uncle, grandm, grandp, grandf, wife, spouse, husband, child, offspring, progeny, son, daughter, nephew, niece, kin, family

10. Ex: Path to root Rule (family) (d) … father who died of MI in 50's Find NE headword Path to top Family term? Subject: Dependency parse rules MI father pmod died pmod adv in 50s tmp subject=family_member of who nmod

11. Ex: Dependency paths Rule (d) Father died of MI in 50's NE + “Family” pairs Find dependency path Once-removed? Subject: Dependency parse rules MI Father pmod died pmod adv in 50s tmp subject=family_member of sbj

12. Methods summary Types of rules Noun phrase structure Path to root Path between pairs Semantic arguments Feature vector Decision logic/ML

13. Status and Future Work cTAKES v2.5 “Assertion” module Default Future work (with data) Evaulation & Error analysis Improved rules Features in machine learning

14. THANK YOU. https://sites.google.com/site/stephentzeinnwu wu.stephen@mayo.edu Task 4/6 team: Stephen Wu Cheryl Clark James Masanz Matt Coarr Ben Wellner Special thanks to: Lee Becker Guergana Savova Pei Chen 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.