SHARPn
Secondary Data Use
Normalization, NLP,
Phenotyping
CG Chute, SM Huff - coPIs
SHARPn Team
• Agilex Technologies
• Harvard Univ.
• CDISC (Clinical Data Interchange • Intermountain Healthcare
Standards Consortium)
• Mayo Clinic
• Centerphase Solutions
• Mirth Corporation, Inc.
• Deloitte
• MIT
• Group Health, Seattle
• MITRE Corp.
• IBM Watson Research Labs • Regenstrief Institute, Inc.
• University of Utah
• SUNY
• University of Pittsburgh
• University of Colorado
Themes &
Projects
http://informatics.mayo.edu/sharp/index.php/SHARP_Project_Wiki:Current_events
Data Normalization
Highlights
H Liu
And team
Data Normalization
Target
Value Sets
Information
Models
Normalization
Targets
Tooling
Raw EMR
Data
Normalized
EMR Data
Normalization
Process
Normalization Targets
Clinical Element Models
– Based on Intermountain Healthcare/GE
Healthcare’s detailed clinical models
– Future with CIMI
 Clinical Information Modeling Initiative
Terminology/value sets associated with
the models
– Using standards where possible
Secondary Use Clinical Element
Models
http://www.clinicalelement.com
GenericStatement
Core CEMs
SecondaryUse
CEMs
GenericComponent
Links
AdministrativeGender, …
Severity, Status
Embracing the fact that data may
not be able to be normalized and
enabling bottom-up and top-down
Normalization Process
 Configuration of Model (Syntactic) and
Terminology (Semantic) Mapping
 UIMA Pipeline to transform raw EMR data
to normalized EMR data based on
mappings
End-to-end DN framework
NLP Highlights
GS Savova
And team
•
•
•
•
•
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•
•
Sign/Symptom CEM template
Disease/Disorder CEM template
Alleviating_factor
associatedCode
Body_laterality
Body_location
Body_side
Conditional
Course
Duration
End_time
Exacerbating_factor
Generic
Negation_indicator
Relative_temporal_context
Severity
Start_time
Subject
Uncertainty_indicator
Alleviating_factor
Associated_sign_or_symptom
associatedCode
Body_laterality
Body_location
Body_side
Conditional
Course
Duration
End_time
Exacerbating_factor
Generic
Negation_indicator
Relative_temporal_context
Severity
Start_time
Subject
Uncertainty_indicator
Medication CEM template
associatedCode
Change_status
Conditional
Dosage
Duration
End_date
Form
Frequency
Generic
Negation_indicator
Route
Start_date
Strength
Subject
Uncertainty_indicator
• Change_status
associatedCode
• Dosage
Body Location
• Duration
Conditional
• End_date
Generic
• Form
Negation_indicator
Procedure CEM template
•
Frequency
Lab CEM template
associatedCode
Severity
Anatomical Site CEM• template
Route Body_laterality
Abnormal_interpretation
Body_location
associatedCode
associatedCode
Subject
Body_side
• Start_date
Body_laterality
Conditional
Conditional
Body_side
Delta_flag
Uncertainty_indicator
Device
•
Strength
Conditional
Estimated_flag
Generic
Lab_value
Negation_indicator
Ordinal_interpretation
Reference_range_narrative
Subject
Uncertainty_indicator
Generic
Negation_indicator
Subject
Uncertainty_indicator
End_date
Generic
Method
Negation_indicator
Relative_temporal_context
Start_date
Subject
Uncertainty_indicator
Processing Clinical Notes
A 43-year-old
woman was diagnosed with type 2 diabetes
A 43-year-old woman was diagnosed with type
2 diabetes
mellitus
mellitus by her family physician 3 months before
this by her family physician 3 months before this
presentation.
Her initial blood glucose was 340 mg/dL.
presentation. Her initial blood glucose was 340
mg/dL. Glyburide
2.5 mg
2.5 mg once daily was prescribed. Since then, Glyburide
self-monitoring
of once daily was prescribed. Since then,
self-monitoring
of blood glucose (SMBG) showed blood
blood glucose (SMBG) showed blood glucose levels
of 250-270
glucose
levels
of
250-270 mg/dL. She was referred to an
mg/dL. She was referred to an endocrinologist for further
endocrinologist for further evaluation.
evaluation.
On acutely
examination,
On examination, she was normotensive and not
ill. Hershe was normotensive and not acutely
ill.a Her
body
mass index (BMI) was 18.7 kg/m2 following
body mass index (BMI) was 18.7 kg/m2 following
recent
10 lb
a recentand
10 ankle
lb weight loss. Her thyroid was
weight loss. Her thyroid was symmetrically enlarged
symmetrically
enlarged and ankle reflexes absent. Her
reflexes absent. Her blood glucose was 272 mg/dL,
and her
bloodshowed
glucose
was 272 mg/dL, and her hemoglobin A1c
hemoglobin A1c (HbA1c) was 10.3%. A lipid profile
a total
(HbA1c)
was
10.3%.
A lipid profile showed a total
cholesterol of 261 mg/dL, triglyceride level of 321 mg/dL, HDL
cholesterol
of 261 mg/dL, triglyceride level of 321
level of 48 mg/dL, and an LDL of 150 mg/dL. Thyroid
function
mg/dL, HDL level of 48 mg/dL, and an LDL of 150 mg/dL.
was normal. Urinanalysis showed trace ketones.
Thyroid function was normal. Urinanalysis showed trace
She adhered to a regular exercise program and vitamin regimen,
ketones.
smoked 2 packs of cigarettes daily for the past 25 years, and
She
adhered
to a regular exercise program and vitamin
limited her alcohol intake to 1 drink daily. Her
mother's
brother
regimen, smoked 2 packs of cigarettes daily for the
was diabetic.
past 25 years, and limited her alcohol intake to 1
drink daily. Her mother's brother was diabetic.
A 43-year-old woman
A 43-year-old woman was
was diagnosed with
diagnosed with type 2
type 2 diabetes mellitus
diabetes mellitus by her
by her family physician
family physician
3
A 43-year-old
woman was3 months before this
mpresentation.
Her
initial
diagnosed with type 2 diabetes
presentation. Her
blood glucose
wasby
340
mg/dL.
mellitus
her
family physician
initial blood glucose
Glyburide
3 months before this
was 340 mg/dL.
presentation. Her initial blood
Glyburide
glucose was 340 mg/dL.
Glyburide
Clinical Element Model
Disorder CEM
text:
code:
subject:
relative temporal context:
negation indicator:
diabetes mellitus
73211009
patient
3 months ago
not negated
Medication CEM
text:
code:
subject:
frequency:
negation indicator:
strength:
Glyburide
315989
patient
once daily
not negated
2.5 mg
Tobacco Use CEM
text:
code:
subject:
relative temporal context:
negation indicator:
smoking
365981007
patient
25 years
not negated
Disorder CEM
text:
code:
subject:
relative temporal context:
negation indicator:
diabetes mellitus
73211009
family member
not negated
A 43-year-old woman was diagnosed with type 2 diabetes
mellitus by her family physician 3 months before this
presentation. Her initial blood glucose was 340 mg/dL.
Glyburide 2.5 mg once daily was prescribed. Since then,
self-monitoring of blood glucose (SMBG) showed blood
glucose levels of 250-270 mg/dL. She was referred to an
endocrinologist for further evaluation.
On examination, she was normotensive and not acutely
ill. Her body mass index (BMI) was 18.7 kg/m2 following
a recent 10 lb weight loss. Her thyroid was
symmetrically enlarged and ankle reflexes absent. Her
blood glucose was 272 mg/dL, and her hemoglobin A1c
(HbA1c) was 10.3%. A lipid profile showed a total
cholesterol of 261 mg/dL, triglyceride level of 321
mg/dL, HDL level of 48 mg/dL, and an LDL of 150 mg/dL.
Thyroid function was normal. Urinanalysis showed trace
ketones.
She adhered to a regular exercise program and vitamin
regimen, smoked 2 packs of cigarettes daily for the
past 25 years, and limited her alcohol intake to 1
drink daily. Her mother's brother was diabetic.
Apache cTAKES: Components
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Sentence boundary detection (Apache OpenNLP technology)
Tokenization (rule-based)
Morphologic normalization (NLM’s LVG)
POS tagging (Apache OpenNLP technology)
Shallow parsing (Apache OpenNLP technology)
Named Entity Recognition
 Dictionary mapping (lookup algorithm)
 types: diseases/disorders, signs/symptoms, anatomical sites, procedures, medications
• Assertion discovery (attributes for negation, uncertainty, conditional,
generic)
• Dependency parser
• Constituency parser
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15
Semantic Role Labeling
Relation Extraction
Co-reference module
Drug Profile module
Smoking status classifier
Clinical Element Model (CEM) normalization module
High Throughput
Clinical Phenotyping
Highlights
J Pathak
And team
EHR-driven Phenotyping Algorithms – The Process
Rules
Evaluation
Phenotype
Algorithm
Transform
Mappings
Visualization
Transform
Data
NLP, SQL
High-Throughput Phenotyping from EHRs
[eMERGE Network]
Algorithm Development Process - Modified
•
Standardized and structured
representation of phenotype
definition criteria
Use the NQF Quality Data
Model (QDM)
•
Rules
•
Conversion of structured
phenotype criteria into
executable queries
Evaluation
• Use JBoss® Drools (DRLs)
Semi-Automatic Execution
Phenotype
Algorithm
• Standardized representation
Visualization
•
Transform
Mappings
Transform
of clinical data
Create new and re-use existing
clinical element models (CEMs)
Data
NLP, SQL
High-Throughput Phenotyping from EHRs
[Welch et al., JBI 2012; 45(4):763-71]
DROOLS
[Li et al., AMIA 2012]
High-Throughput Phenotyping from EHRs
http://phenotypeportal.org
[Endle et al., AMIA 2012]
NLM funded Library of Computable
Phenotyping Algorithms
High-Throughput Phenotyping from EHRs
Clinical Validation
Highlights
KR Bailey
And team
Validation Highlights
Enumeration of sources by datatypes :
–a) Diagnoses
–b) Laboratory values
–c) Vital signs (Ht, Wt, BMI, SBP, DBP, HR)
–d) Medications
Characterize sources, availability, quality
Compare sources and data
–a) Within institution
–b) Across institution
URLs
http://sourceforge.net/projects/sharpn/
http://ctakes.apache.org
http://phenotypeportal.org/
SHARPn: Secondary Use of EHR Data