Automating the formalization of clinicalguidelines using information extraction:an overview of recent lexical approaches

05 August 2011

Phil GoochCentre for Health InformaticsCity University, London UK

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.

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)

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

Computer-interpretable guidelines (CIGs)

Rule-based: ‘if ... then’, e.g. Arden Syntax for individual clinical decisionsLET 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 7then 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-likemodelling of decisions over time

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

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>

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 itis 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 threedrugs, consider adding a fourth and/or seeking expert advice

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

Information extraction approaches

• Bottom-up: identification of individual clinical terms, temporalexpressions, 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)

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.

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?

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 containingLatin/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)'

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

GATE-MetaMap integration module

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

Extracting guideline recommendations

Extracting guideline recommendations

… and rules from guideline text

Information extraction from patient data

Patient data: automatic spelling correction

Patient data: automatic spelling correction

Patient data: WordNet mappings for coreferencing