1. i2b2 Challenge 2009
and
Our Participation
Irena Spasic
Farzaneh Sarafraz
Goran Nenadic
Summer 2009

2. About i2b2
Informatics for Integrating Biology & the Bedside
Related to NIH
3 Shared tasks so far

3. The task: Medication Extraction
Given Other
Discharge reports Event
Wanted Temporal
Medication mention Certainty
Dose
Mode of application
Frequency
Duration
Reason
List/narrative

4. Example
Ciprofloxacin 500 mg q.6h. for remaining four
doses baby aspirin 81 mg daily , Lasix 40 mg
b.i.d. , for three days along with potassium
chloride slow release 20 mEq b.i.d. for three
days , Motrin 400 mg q.8h. p.r.n. Pain
The patient had received a total of five units
of packed red blood cells due to blood loss

5. Regulations/requirements
Medical requirements
Drug taken by patient
No allergies
No food, water, diet, tobacco, alcohol, illicit drugs
Linguistic requirements
the most informative base adjective phrase or the
longest base noun phrase as reason

6. Required output
Event-based annotation
Repeat individual mention for each event
“Aspirin for headache and for leg pain”
Aspirin … headache
Aspirin … leg pain
Semantic-level expectations
NITROGLYCERIN 1/150 ( 0.4 MG ) 1 TAB SL q5min
x 3

7. Training and test data
Ground Truth, 27 records
Manually annotated by “PG students”
Scrutinised by the community
Relative f-score: ~60%
Unannotated training data: 620
Test data: 260

8. Our system
Linguistic Preprocessing
Input: plain ASCII
Output: XML
Rules
MinorThird
Template Filling

9. Preprocessing
Split sentences
A sentence and paragraph breaker
NaCTeM: sptoolkit.jar
POS tagging
A part-of-speech tagger for English
Tsujii: postagger
Parsing (chunking)
CFG parser
Tsujii: chunkparser

10. Rules
Medication Dictionary (> 1000)
Morphological: medication affix (> 100)
-bicine, -caine, etc.
Precedes a mode
Inhaler, supplement, etc.
Medication type
Cardiac, cardiovascular (~100)
Symptoms (~100)
Chest discomfort, etc.

11. Word lists and regular expressions
Dosage, mode, frequency
Duration (While, for, etc.)
Reason
Head
Diseases
Symptoms (pain, agitation, etc.) ~20
Inffixes (hyper-, -emia, etc.)
Modifier (acute, chronic, etc.) <100
Time phrases, Body parts

12. Producing output
Remove allergies
Remove laboratory results
Merge labels
<m>INSULIN</m> <m>GLARGINE</m>
<f>after dialysis</f> on <f>Monday</f>­
<f>Wednesday</f>­<f>Friday</f>
Remove negated medications
“patient instructed not to take Viagra.”
etc.

13. Evaluation process
Small training data (27)
Organisers
Community
Gold standard test data (260)
Annotated by participants
Merge and tie-break
Community
Silver data (620)
Voting

14. Evaluation on ground truth
inexact horizontal system­level X 0.8776
inexact horizontal patient­level X 0.8928
inexact vertical system­level do 0.9150
inexact vertical patient­level do 0.9160
inexact vertical system­level f 0.9172
inexact vertical patient­level f 0.9197
inexact vertical system­level mo 0.9441
inexact vertical patient­level mo 0.9471
inexact vertical system­level m 0.9544
inexact vertical patient­level m 0.9519
inexact vertical system­level r 0.5260
inexact vertical patient­level r 0.3876
inexact vertical system­level du 0.7958
inexact vertical patient­level du 0.5846

15. Preliminary evaluation on test data
inexact horizontal system­level X 0.7847
inexact horizontal patient­level X 0.7755
inexact vertical system­level do 0.8267
inexact vertical patient­level do 0.8155
inexact vertical system­level f 0.8349
inexact vertical patient­level f 0.8289
inexact vertical system­level mo 0.8359
inexact vertical patient­level mo 0.8256
inexact vertical system­level m 0.8533
inexact vertical patient­level m 0.8541
inexact vertical system­level r 0.3881
inexact vertical patient­level r 0.3883
inexact vertical system­level du 0.51
inexact vertical patient­level du 0.4969