Computational Intelligence in
Biomedical and Health Care Informatics
HCA 590 (Topics in Health Sciences)
Rohit Kate
Clinical Natural Language
Processing
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Reading
Paper: What can natural language processing do for clinical
decision support?
Dina Demner-Fushman, Wendy Chapman, Clement McDonald
Journal of Biomedical Informatics 42 (2009) 760-772
Paper: 2010 i2b2/VA Challenge on Concepts, Assertions, and
Relations in Clinical Text
Uzuner Ö., South B., Shen S., DuVall S.
Journal of the American Medical Informatics Association
2011;18(5):552-556
Clinical Decision Support
Systems
• A clinical decision support (CDS) system is any
computer program designed to help healthcare
professionals to make clinical decisions or present
them with patient-specific assessments and
recommendations
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Suggest diagnosis and medications
Trigger reminders
Flag abnormal values
Alert about drug interactions
Remind the user of overlooked diagnoses
Provide advice based on patient-specific data
CDS Systems and Narrative Text
• Such computer based support is much more
effective if the computer system has access to
electronic medical records (EMRs) and has the
ability to process them
• Major portion of patient records, including
radiology reports, operative notes, discharge
summaries etc. are recorded as narrative text
(dictated, transcribed or directly entered) in a
natural language such as English
• Facts that should activate a CDS system are often
found only in free text
CDS Systems and NLP
• Much of the data that could support CDS is
textual and therefore cannot be leveraged by
a CDS system without natural language
processing (NLP)
• NLP to be used for CDS needs to be:
– Reliable
– High-quality
– Modular and flexible
– Fast
Active and Passive CDS with NLP
• Active: System leverages available information
and pushes patient-specific information to the
user
• Passive: The users themselves seek the support
• Users: Depending upon the application, besides
clinicians users could be patients, researchers,
administrators, students, and coders
• Besides free text in the clinical records, NLP for
CDS could also be processing biomedical
literature, web pages etc.
Active and Passive CDS with NLP
Figure from the paper:
Users
Example of an Idealized NLP-CDS System
• It would monitor EMR for insertions of new data
• When free text is entered for example “Right lower lung
opacity, which could be contusion or pneumonia”, NLP
system will kick in to analyze it
• NLP system will extract information that the disorder could
be “pulmonary contusion” or “pneumonia”, this will go to
the CDS system
• The CDS system will look up decision rules for suspected
pneumonia and retrieve results of blood test and evaluate
white blood cell count
• If the count is high, the system will suggest as a reminder
message (may be in natural language) that the patient is
more likely to have pneumonia than pulmonary contusion
and why
Example of an Idealized NLP-CDS System
• Idealizing further, the NLP system may look up
medical literature and solicit more information
and present natural language summaries
– For example, present summaries of best
approaches to manage both disorders
– May look up medical publications, guidelines,
actionable recommendations available in free text
• This idealized system will have to deal with all
the challenges of clinical NLP
Challenges of Clinical Language
Processing
• Good Performance
– Performance should be good enough to be used for clinical
applications, should not be significantly worse than the medical
experts
– System should have flexibility to trade-off precision and recall
• Recovery of Implicit Information
– NLP system should contain enough medical knowledge to make
appropriate inferences
– “rupture” means “rupture of membranes”
– “patchy opacity” and “focal infiltrate” may indicate
“pneumonia”
Challenges of Clinical Language
Processing
• Interoperability: NLP system should seamlessly
integrate into clinical information systems
– Many different interchange formats (e.g. HL7)
– Different types of reports with different formats, text may
contain tables, structured fields etc.
– Output of NLP system should be mapped to appropriate
controlled vocabulary, e.g. UMLS, SNOMED or ICD
Challenges of Clinical Language
Processing
• Training set availability
– Patient records are confidential and requires approval of
institutional review board (IRB)
– There are methods to de-identify names etc. but
identifying names etc. is not easy
– These issues do not arise when processing literature
• Limited availability in electronic form
– Many clinical documents are still written on paper
– Optical Character Recognition (OCR) is not accurate
especially with physicians’ notes
Challenges of Clinical Language
Processing
• Expressiveness
– More than 200 different expressions for severity
information: faint, mild, borderline, 3rd degree, mild to
moderate etc.
– Complex modifiers: “no improvement in pneumonia” in
text will match a query “improvement in pneumonia”
• A lot of abbreviations which could be ambiguous
– pvc may mean pulmonary vascular congestion in chest Xray report and premature ventricular complexes in
electrocardigram report
Challenges of Clinical Language
Processing
• Compactness of text
– Very compact containing many abbreviations
– Sentence boundaries poorly delineated
Admit 10/23
71 yo woman h/o DM, HTN, Dilated CM/CHF, Afib s/p
embolic event, chronic diarrhae, admitted with SOB.
• Rare events
– Medical errors and adverse events are not
reported frequently, difficult to train a system to
detect them
Shared Tasks in Clinical
Language Processing
• Evaluation
– Difficult to obtain gold-standard data, time-consuming for medical
experts to annotate data
– Evaluation competitions or Shared Tasks are very useful, they help
compare different systems on the same data
• i2b2 shared tasks 2008-2012:
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https://www.i2b2.org/NLP/Obesity/
https://www.i2b2.org/NLP/Medication/
https://www.i2b2.org/NLP/Relations/
https://www.i2b2.org/NLP/Coreference/
https://www.i2b2.org/NLP/TemporalRelations/
• ShARe/CLEF eHealth 2013-2014:
– https://sites.google.com/site/shareclefehealth/
– http://clefehealth2014.dcu.ie/
• SemEval 2014 Task 7- Analysis of Clinical Text:
– http://alt.qcri.org/semeval2014/task7/
• TREC Medical Records task
i2b2 2010: Concepts
• Concepts:
– Medical Problems
– Treatments
– Tests
• System input: raw text of medical records
• System output: A plain text file that contains entries of
the form:
c=“concept text” offset || t=“concept type”
(offset indicates line and token numbers of the document)
For example:
– c=“cancer” 5:8 5:8 || t=“problem”
– c=“chemotherapy” 5:4 5:4 || t=“treatment”
– c=“chest x-ray” 6:12 6:13 || t=“test”
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i2b2 2010: Assertions
• Assertions (attributes of medical problems):
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Present
Absent
Possible
Conditional
Hypothetical
Not associated with the patient
• System input: raw text of medical records and given concepts
• System output: Assertions on all problem concepts (and only
problem concepts)
c=“concept text” offset || t=“concept type” || a=“assertion value”
For example:
– c=“hypertension” 5:4 5:4 || t=“problem” || a=“absent”
– c=“diabetes” 6:12 6:12 || t=“problem” || a=“possible”
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i2b2 2010: Relations
• Extract the relations that exist between the concepts:
– medical problems and treatments
• 6 possible relations
– medical problems and tests
• 3 possible relations
– medical problems and other medical problems
• 2 possible relations
• System input: raw text medical records with given concepts and assertions
(optional)
• System output: relations of pairs of concepts in the following format:
– c="a cardiac catheterization" 9:12 9:14 || r="TeCP" || c="chest pain" 9:5 9:6
– c="a cardiac catheterization" 9:12 9:14 || r="TeRP" || c="an occluded right
coronary artery" 9:23 9:27
– c="a cardiac catheterization" 9:12 9:14 || r="TeRP" || c="a 40-50% proximal
stenosis" 9:29 9:32
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i2b2 2010: Data
• 349 Training reports
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97 discharge summaries from Partners
73 discharge summaries from Beth-Israel Deaconess Medical Center
98 Discharge summaries from University of Pittsburgh Medical Center
81 progress notes from University of Pittsburgh Medical Center
• 477 Test reports
– 133 discharge summaries from Partners
– 123 discharge summaries from Beth-Israel Deaconess Medical Center
– 102 Discharge summaries from University of Pittsburgh Medical
Center
– 119 progress notes from University of Pittsburgh Medical Center
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i2b2 2010: Best Results
• Total 41 teams participated (22 for concepts,
21 for assertions and 16 for relations)
Best F-measures (harmonic mean of precision &
recall):
• Concepts: 85% F-measure
• Assertions: 92.6% F-measure
• Relations: 73.7% F-measure
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