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Automatic Formalization of Clinical Practice Guidelines. Matthew S. Gerber and Donald E. Brown Department of Systems and Information Engineering University of Virginia. James H. Harrison Department of Public Health Sciences University of Virginia. Clinical Practice Guidelines.

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Automatic formalization of clinical practice guidelines
Automatic Formalization of Clinical Practice Guidelines

Matthew S. Gerber and Donald E. Brown

Department of Systems and Information Engineering

University of Virginia

James H. Harrison

Department of Public Health Sciences

University of Virginia

Clinical practice guidelines
Clinical Practice Guidelines

  • Many treatment options – what to do?



Randomized clinical trial: beneficial

Benefits / costs

Should consider

Meta-analysis: usually beneficial

Might consider

Expert opinion: might be beneficial

Evidence quality

Clinical practice guidelines1
Clinical Practice Guidelines

  • Development

    • Expert synthesis of current evidence

    • Example from heart failure:

Clinical practice guidelines2
Clinical Practice Guidelines

  • Expected outcomes

    • Evidence-based clinical decision aid

    • Reduction in cost and treatment/outcome variation

    • Improvement in patient health

  • Challenges

    • A guideline for any occasion

    • Guidelines change periodically

    • Lengthy (HFSA CPG is 259 pages)

Clinical decision support systems
Clinical Decision Support Systems

  • Goal: deliver CPG knowledge at point of care

  • Alleviate burden on clinician

  • Problem: CPGs contain minimally structured text

Formalization is required

Traditional cpg formalization
Traditional CPG Formalization

Knowledge representation


Knowledge engineers

Medical experts

Knowledge management

software (e.g., Protégé)

Automatic formalization


The big p icture
The Big Picture






Medical decision support

Structured knowledge

Retrospective analyses

Data collection
Data Collection

  • Yale Guideline Recommendation Corpus

    • Hussain et al. (2009)

    • 1,275 recommendations

    • Representative sample of domains and rec. types

      “Oral antiviral drugs are indicated within 5 days of the start of the episode and while new lesions are still forming.”

    • Simplifications

      • Delimited recommendations

      • No inter-recommendation dependencies

  • Random sub-sample of YGRC (n=200)

Recommendation representation
Recommendation Representation

Fidelity: Low High


    • Medical concept ontology

    • Broad coverage

Keywords ? Asbru, etc.

Automation: Trivial Impossible

Recommendation representation1
Recommendation Representation

(Sundvalls et al., 2012)

Recommendation representation2
Recommendation Representation


Recommendation annotation
Recommendation Annotation

  • Task: manually identify representational elements within recommendations

  • Example

    Diuretics are recommended for patients with heart failure.

    [DRUG Diuretics] are recommended for [POPULATION patients with [MORBIDITY heart failure]].


  • Natural language processing

  • Supervised classification

  • Per-recommendation pipeline

    • Syntactic parsing

    • Parse node classification

    • Post-processing

Methods 1 syntactic parsing
Methods: (1) Syntactic Parsing

  • Constituency parser (Charniak and Johnson, 2005)

Methods 2 parse n ode c lassification
Methods: (2) Parse Node Classification

  • Unit of classification: node

  • Multi-class logistic regression

  • Example: 1 positive, 17 negative

  • Actual

    • 12K nodes

    • 10 classes (primary)

Methods 2 parse n ode c lassification1
Methods: (2) Parse Node Classification

  • Linguistic features

    • Word stems under node

    • Syntactic configuration of node

Methods 2 parse n ode c lassification2
Methods: (2) Parse Node Classification

  • Learning

    • Forward feature selection

    • Per-class costs (LibLinear)

Methods 3 post processing
Methods: (3) Post-processing

  • Remove duplicates

  • Other possible issues

    • Conflicts

    • Embedding

Evaluation results
Evaluation Results

  • 10-fold cross-validation


  • High variance across classes

  • Alternative strategies

    • Identify more informative features

    • Change the model formulation

    • Annotate more data


  • CPGs are an important knowledge source

  • Difficult to use within CDSS

  • Prior CPG formalization

    • Manual

    • Automatic for specific domains / recommendations

  • Our contributions

    • SNOMED-CT representation

    • Manually annotated recommendation sample

    • Statistical NLP model / evaluation

Future work
Future Work

  • Refined representation

  • Model formulation

  • Feature engineering

  • Controlled natural language


  • References

    • Charniak, E. & Johnson, M. Coarse-to-fine n-best parsing and MaxEnt discriminative reranking. Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, 2005, 173-180.

    • Hussain, T.; Michel, G. & Shiffman, R. N. The Yale Guideline Recommendation Corpus: A representative sample of the knowledge content of guidelines. I. J. Medical Informatics, 2009, 78, 354-363.

    • Fan, R.-E.; Chang, K.-W.; Hsieh, C.-J.; Wang, X.-R. & Lin, C.-J. LIBLINEAR: A Library for Large Linear Classification. Journal of Machine Learning Research, 2008, 9, 1871-1874.

    • Sundvall, E.; Nystrom, M.; Petersson, H. & Ahlfeldt, H. Interactive visualization and navigation of complex terminology systems, exemplified by SNOMED CT. Studies in health technology and informatics, IOS Press; 1999, 2006, 124, 851.