Automating the formalization of clinical guidelines using information extraction
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The document discusses automating the formalization of clinical guidelines through information extraction techniques, highlighting the challenges in standardizing and improving their use at the point of care. It outlines various approaches, such as computer-interpretable guidelines and information extraction methods, to convert guideline texts into formal models that can support clinical decision-making. Key issues include the complexity of guideline language, the variability in document formats, and the need for effective segmentation and mapping to clinical concepts.
![Guideline patterns
Serban et al. (2007), examples:
(med_context, target_group, recommendation_operator, med_action)
In the event of [pregnancy]med_context, [patients with diabetes]target_group
[should]recommendation_op be[prescribed calcium channel blocker]med_action
(target_group, med_context, med_goal)
For [diabetic patients]target_group with [kidney damage]med_context the [blood
pressure target is130/80]med_goal](https://image.slidesharecdn.com/goochieforformalizationofclinicalguidelines2011-120828024421-phpapp01/85/Automating-the-formalization-of-clinical-guidelines-using-information-extraction-16-320.jpg)
Automating the formalization of clinical guidelines using information extraction
- 1. Automating the formalization of clinical guidelines using information extraction: an overview of recent lexical approaches 05 August 2011 Phil Gooch Centre for Health Informatics City University, London UK
- 2. Clinical guidelines • Contain recommendations for best practice based on systematic reviews of clinical evidence, consensus statements and expert opinion. • Goal is to reduce variation in medical care by promoting the most effective treatments, and to provide a means of quality control in clinical practice via audit • Produced by a variety of organizations (e.g. NICE, RCP, SIGN) in a variety of document formats usually not conducive to use at the point of care.
- 3. Clinical decision support (CDS) • Aims to provide diagnostic and treatment recommendations and advice at the point of care, i.e. information tailored for the specific patient under consideration by the clinician during a consultation • CDS systems require a knowledge base (KB), usually derived from guidelines, consisting of declarative knowledge (penicillin is-a antibiotic) and procedural (if…then) rules, and some sort of electronic patient record system (EPR)
- 4. Computer-interpretable guidelines • Early systems ‘computerized’ guidelines by making them available ‘on the computer’, e.g. as HTML or PDF • Did not lead to improved guideline compliance or use! • To standardize the format of the knowledge-base, ease development of CDS, and to improve guideline use at the point of care, a number of formalisms for representing guidelines have been developed
- 5. Computer-interpretable guidelines (CIGs) Rule-based: ‘if ... then’, e.g. Arden Syntax for individual clinical decisions LET Last_HgA1C BE READ LATEST {"HgA1C Value"}; LET Diabetic_Patient BE READ LATEST {"Problem: Diabetes"}; if Diabetic_Patient and Last_HgA1C Occurred not within past 6 months and Last_HgA1C is less than or equal 7 then conclude true; Document based, e.g. GEM, for complete guideline documents in XML OO expression query languages e.g. GELLO: observation.code == ‘SBP’ AND observation.value > 140 AND assessment.code ==‘LVF’ Task-network models (TNM), e.g. GLIF, Asbru, PROforma, for workflow-like modelling of decisions over time
- 6. Formalization of guidelines into a CIG model • Declarative: Mapping clinical concepts in the guideline to terms within a controlled vocabulary (e.g. UMLS) or ‘virtual medical record’ • Procedural: Identification and extraction of eligibility criteria, clinical actions (tests, treatment regimes, referrals), temporal constraints and if…then decision rules • Translation to a formal model, e.g. PROforma, GLIF, Asbru • Time-consuming, iterative, manual process as the guideline text tends to assume background knowledge, is incomplete or contains ambiguity and vague terms
- 7. Example CIG fragment (Asbru) <plan name="Doxycycline : 100 mg orally twice a day for 7 days" plan_id="plan52769441"> <cyclical_plan plan_id="plan5675512"> <frequency value="12" unit="hour"/> </cyclical_plan> <duration> <min value="7" unit="day"/> <max value="7" unit="day"/> </duration> </plan>
- 8. Examples of vague guideline statements Underspecification: • Avoid the use of highly intensive management strategies to achieve an HbA1c level less than 6.5% (48 mmol/mol) • Monitor HbA1c every 2–6 months (according to individual need) until it is stable on unchanging treatment Qualitative terms requiring mapping to numeric values or ranges: • The moderate use of alcohol may increase HDL-cholesterol • If blood pressure remains uncontrolled on adequate doses of three drugs, consider adding a fourth and/or seeking expert advice
- 9. Information extraction for guideline formalization • Helpful to automate • Knowledge base construction: text to formal model translation • Identification of opportunities for decision support: mapping guideline concepts and rules to concepts in the EPR • Measurement of guideline compliance
- 10. Information extraction approaches • Bottom-up: identification of individual clinical terms, temporal expressions, units of measure • Look-up lists, regular expressions • Shallow parsing to identify noun phrases • Terminology services: UMLS, MetaMap • Co-reference resolution: WordNet • Top-down: identification of guideline structure: preamble, eligibility, recommendations, ‘action’ sentences and rules • Shallow parsing to identify verb phrases • Ontologies for semantic relations, e.g. UMLS Semantic Network • Use of linguistic guideline patterns (see later)
- 11. Mapping text to UMLS concepts - problems • Identification of clinical terms is dependent on context: - family history of congestive heart failure - probable diagnosis of congestive heart failure - no evidence of congestive heart failure - patient does not have established cardiovascular disease • Clearly just identifying the raw concepts congestive heart failure and cardiovascular disease and mapping them to UMLS terms is inadequate.
- 12. Mapping guideline text to UMLS concepts - problems • Guideline documents are typically large (100 pages), in PDF or XML format • Requires guideline text to be segmented to enable efficient processing - How best to segment the text that maximizes contextual clinical concept identification?
- 13. Solutions: Text segmentation • Customised phrase chunker to identify candidate terms: - Noun phrases (NP), prepositional phrases (PP), verb phrases (VP) - Neoclassical combining forms phrases (Token groups containing Latin/Greek prefixes, roots, suffixes) - Past-participle and gerund NPs: - 'results in increased blood pressure', 'fasting blood glucose' - List expansion: - 'mild, moderate and severe hypertension → mild hypertension, moderate hypertension and severe hypertension' - 'lowering of heart rate and blood pressure → lowering of heart rate and lowering of blood pressure' - Abbreviation expansion: 'waist circumference (WC)'
- 14. Solutions: GATE-MetaMap Server integration plugin - Extracts clinical concepts, in context, from large guideline texts in multiple formats and encodings (PDF, XML, RTF, ASCII, UTF-8) - Exchanges data/annotations with a MetaMap server - Implements Unicode Normalization Forms for UTF-8 → ASCII - Provides flexible text chunking options - Optimises input data to MetaMap for mapping to UMLS concepts - Integrates with other information extraction pipelines
- 16. Guideline patterns Serban et al. (2007), examples: (med_context, target_group, recommendation_operator, med_action) In the event of [pregnancy]med_context, [patients with diabetes]target_group [should]recommendation_op be[prescribed calcium channel blocker]med_action (target_group, med_context, med_goal) For [diabetic patients]target_group with [kidney damage]med_context the [blood pressure target is130/80]med_goal
- 18. Extracting guideline recommendations … and rules from guideline text
- 19. Information extraction from patient data
- 20. Patient data: automatic spelling correction
- 21. Patient data: automatic spelling correction
- 22. Patient data: WordNet mappings for coreferencing