1. Extracting information
from clinical notes
H. Yang, I. Spasic, F. Sarafraz,
John A. Keane, Goran Nenadic
School of Computer Science
University of Manchester

2. Motivation & aim
 Electronic clinical notes
 electronic medical/health records
 hospital discharge summaries
 Extract information on
 individual patients and their diseases
 clinical practice
 treatments, drugs used, etc.
 Aim: support data analytics
 e.g. monitoring quality
 Huge interest locally and internationally

3. Clinical notes
 Highly condensed text
 sometimes without proper sentences
 hospital discharge summaries are more structured
 list of medications, symptoms, etc.
 Terminological variability
 orthographic, acronyms, local conventions
 Various sections
 previous history, social/family background

5. NLP challenges in clinical data
 A series of international challenges in information
extraction from clinical narratives
 organisers: Informatics for Integrating Biology & the
Bedside (i2b2)
 3 shared tasks so far
− De-identification of medical records and identification of
smokers from their clinical records (2007)
Identification of obesity & related diseases in patients from
hospital discharge documents (2008)
Extraction of medications and related information from
patients’ discharge documents (2009)
 2010 challenge
 concept, assertions, relations

6. i2b2 2008
 Extract status of diseases in patients
 obesity, diabetes mellitus, hypercholesterolemia,
hypertriglyceridemia, hypertension, heart failure (16 in total)
 status: yes, no, unmentioned, questionable
 on textual and “intuitive” level
 28 teams worldwide
 UoM ranked 1st in textual and 7th in intuitive
 Our methodology
 Term-based exact and approximate matching
 Context-based pattern- and rule-based matching
 Machine learning approach
Yang, H., Spasic, I., Keane, J., Nenadic, G.: A Text Mining Approach to the Prediction of a
Disease Status from Clinical Discharge Summaries, JAMIA 16(4):596-600

7. Methodology
Linguistic section splitting, sentence splitting,
pre-processing chunking, POS tagging, parsing
Information textual evidence extraction,
extraction section filtering, morphological
Medical
(rules, machine clues (e.g. drug/disease name
resources
learning) affixes)
•Disease names
•Drug names
•Body parts Template filling, filtering negative
•Symptoms results, relations and heuristics:
•Abbreviations Constructing Organ : Symptom,
•Synonyms results Symptom : Disease,
Disease : Drug,
Drug : Mode of application

8. Rule-based IE
 Disease status patterns
- context-based patterns
[N] negative for CHF
[Q] question of asthma
[U] no known diagnosis of CAD
[U] we should consider further asthma studies as an
outpatient
- semantics-based patterns
[N] normal coronaries, a thin black man
 Clinical resources used in sentence extraction
 clinical inference rules e.g., weight>90kg,
LDL>160mg/dl, HDL<35mg/dl
 medications e.g., ‘anti-depressant’

9. Textual Annotation Results
 Performance on Disease Status (Ranked 1st)
Micro-average: Accuracy (0.9723)
Macro-average: P (0.8482), R (0.7737), F-score (0.8052)
#Eval #Corr #Gold Precision Recall F-score
Y 2267 2132 2192 0.9404 0.9726 0.9562
N 56 40 65 0.7142 0.6153 0.6611
Q 12 9 17 0.7500 0.5294 0.6206
U 5709 5640 5770 0.9879 0.9774 0.9826

10. Intuitive Annotation Results
 Performance on Disease Status (Ranked 7th)
Micro-average: Accuracy (0.9572)
Macro-average: P (0.6383), R (0.6294), F-score (0.6336)
#Eval #Corr #Gold Precision Recall F-Score
Y 2160 2068 2285 0.9574 0.9050 0.9304
N 5236 5014 5100 0.9576 0.9831 0.9702
Q 3 0 14 0 0 0

11. i2b2 2009
 Extract mentions of medication and related
information
 drugs the patient takes
 dose, mode of application, frequency, duration, etc.
(for each mention)
 19 teams worldwide
 UoM ranked 3rd
 Our approach was based on combining
 extensive dictionaries
 morphological and derivational patterns

12. Evaluation (F-measure)
Medication 83.59%
Dosage 82.67%
Frequency 83.49%
Mode 85.33%
Duration 51.00%
Reason 38.81%
All fields 78.47%
Spasić I, Sarafraz F, Keane JA, Nenadic G: “Medication Information Extraction
with Linguistic Pattern Matching and Semantic Rules”, JAMIA (to appear)

13. Summary
 NLP and text mining techniques are useful for extraction
of clinical data
- disease status extraction: 95-97% accuracy
- medication information extraction: 80% F-measure
 Construction of reliable and sufficient resources
- clinical terms and abbreviations (e.g., disease synonyms,
symptoms, drugs)
- context patterns related to diseases, medication, etc.
 Domain knowledge required
 construction of domain- and task-specific resources
 complex clinical facts and conditions for inference
 more comprehensive knowledge representation needed