This paper describes a lightweight ontology for representing annotations of declarative evidence based clinical guidelines. We present the motivation and requirements for this representation, based on an analysis of several guidelines. The ontology provides the means to connect clinical questions and associated recommendations to underlying evidence, and can capture strength and quality of recommendations and evidence, respectively. The ontology was applied in the conversion of manual annotations to RDF and used as part of a prototype clinical decision support system.
Evidence based clinical guidelines follow the principle that every recommendation of the guideline should be supported by identi able evidence in the form of published medical research. This is in contrast to the older variant, where guidelines are based on consensus within the scienti c community. The strength of the evidence based approach is that such guidelines are more adaptive to new ndings in medical research (most importantly clinical trials ), and that they preserve provenance of recommendations in the form of citations to scienti c literature.
Guidelines are part of a larger network of hypotheses, claims and pieces of
evidence that span across multiple publications [
To unlock the clinical knowledge contained in a guideline, we can choose
from several languages to formalize the guideline and make it a \computer
interpretable" guideline [
The main question underlying this work is: does access to declarative
guidelines bene t from a lightweight modeling approach? In the context of clinical
decision support systems: are lightweight models adaptive enough, and do they
have su cient power to link a patient to relevant guidelines and underlying
evidence? This paper is the rst step in answering our question: a lightweight
ontology for annotating DEGs. Section 2 gives an analysis of the structure of
DEGs. This provides us with the requirements for the ontology, which is
described in section 3. The ontology describes DEGs at a meta-level; it builds on
existing work on annotation languages. We ran a small pilot on a guideline on
Febrile Neutropenia [
Declarative Evidence Based Guidelines: Requirements Evidence based guidelines5 are well structured documents that follow a clear recipe: introduction of methodology and de nitions of recommendation strength and evidence quality and level, followed by a list of clinical questions and their recommendations, and nally a discussion of the recommendation, a summary of the underlying evidence and citations of medical publications. We brie y discuss each of these. Figure 1 shows an excerpt of a clinical guideline on Febrile Neutropenia containing a number of recommendations and a part of the associated evidence summary. For instance, the recommendation:
\At least 2 sets of blood cultures are recommended " has an evidence summary containing: \Recently, 2 retrospective studies found that 2 blood culture sets detect 80%{90% of bloodstream pathogens in critically ill patients, whereas 3 sets are required to achieve >96% detection" that cites papers \49 " and \50 " as underlying evidence. It is this link between a recommendation, its summary and underlying evidence that we aim to capture. We use this excerpt as running example throughout the paper. 5 We studied a wide range of guidelines from the Department of Health (UK), AIMSA (US), NABON (NL), NHRMC (AUS) and others. 1. Laboratory tests should include a CBC count with di↵ erential leukocyte count and platelet count; measurement of serum levels of creatinine and blood urea nitrogen; and measurement of electrolytes, hepatic transaminase enzymes, and total bilirubin (A-III). 2. At least 2 sets of blood cultures are recommended, with a set collected simultaneously from each lumen of an existing CVC, if present, and from a peripheral vein site; 2 blood culture sets from separate venipunctures should be sent if no central catheter is present (A-III). Blood culture volumes should be limited to <1% of total blood volume (usually 70 mL/kg) in patients weighing <40 kg (C-III). [. . . ] Evidence Summary Physical Examination Signs and symptoms of inflammation are often attenuated or absent in neutropenic patients. Accordingly, in neutropenic patients, bacterial infections of skin and softh-taisssuseummay mlaackr yinduration, erythema, warmth, or pustulation; a pulmonary infection may have no discernible infiltrate on a radiograph; CSF pleocytosis might be modest or altogether absent in the setting of meningitis; and a urinary tract infection may demonstrate little or no pyuria. Fever is often the only sign of a serious underlying infection. [. . . ] Cultures The total volume of blood cultured is a crucial determinant of detecting a bloodstream infection [47]. Accordingly, at least 2 sets of blood culture specimens should be obtained, [. . . ] Recently, 2 retrospective studies found that 2 blood culture sets detect 80%90% of bloodstream pathogens in critically ill patients, whereas 3 sets are required to achieve >96% detection [49-50]. [. . . ]
Table 1. Excerpt of [
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underlying evidence studies. However, we have found that in many cases,
recommendations exist without a discussion in the evidence summary. And conversely,
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recomnot any of the intermediate results. This is a regrettable loss of information:
mendation [
Although evidence categorization schemes are based on the same principles,
the resulting categories (or codes) may vary signi cantly across di erent
guidelines. Quality and strength may be represented independently or jointly; strength
may be associated with the recommendation, rather than the evidence; and
the number of categories may di er. For instance, in [
Another observation is that in the guidelines we have seen, strength and quality codes are linked to a recommendation, but not to the text in the evidence summary, nor to the studies themselves. In other words, the reader is presented only the outcome of the meticulous weighing and categorizing of evidence, but not any of the intermediate results. This is a regrettable loss of information: insight in the assessed quality of individual studies can assist the clinician in weighing more nuanced cases. It is not always clear how or if the quality of evidence contributes to the strength of a recommendation. In our example, a strong `A' recommendation may be backed by weak `III' evidence. Lifecycle Whenever a su cient quantity, or su ciently important new evidence arises, a guideline needs to be updated. Guidelines may thus undergo multiple revisions that are very similar in some respects (e.g. structure, questions) but very di erent in others (recommendations, evidence, authors). This is where guidelines di er from more regular publications in science, but it is similar to e.g. working papers in the social sciences and humanities. The relative dynamics of guidelines creates a signi cant maintenance burden for approaches that intend to capture the contents of a guideline in a formal model. 6 See http://www.nhmrc.gov.au/guidelines/publications/cp30 and http://www.oncoline. nl/mammacarcinoom respectively.
The analysis above identi es ve core parts of evidence based guidelines: clinical questions, recommendations, evidence summaries, evidence studies and evidence quality scores. Our ontology should allow us to identify these parts, indicate their type, and relate them amongst each other. Given the variety in scoring schemes, we intend to express the strength and quality of recommendations and evidence in an explicit but lightweight model. The relations and types express an interpretation of a guideline: we do not purport to provide o cial representations, but rather use annotations to make explicit the structure of guidelines. This will allow for multiple, possibly competing, interpretations of the annotations. In this context, provenance information about the annotation process is very important.
Annotation of medical publications is certainly not new. Tools such as the
BioPortal annotator [
Unfortunately, the OA model cannot be used to relate (parts of) one or more documents.11 In our scenario, we would like to be able to express e.g. that this particular bit of text in document A is a recommendation, that is supported 7 See http://code.google.com/p/annotation-ontology/ 8 See http://www.openannotation.org/ 9 See http://www.openannotation.org/spec/core/20120501.html 10 See http://www.w3.org/TR/prov-o/ 11 The vocabularies allow for multiple targets to an annotation, but this does not capture the directedness of the support relation. d2sa:RecommendationAnnotation rdf:type rdf:type oa:Annotation oax:TextQuoteSelector oa:SpecificResource by that particular bit of text in document B. The SWAN ontology12 identi es discourse elements (research statements such as hypothesis and claim) and the relations between them, but can only be used to refer to the sources of those statements (actual publications) at the document level. There is no prescribed way to directly use SWAN relations together with the annotation vocabularies. SWAN relations hold between the research statements themselves, and not between annotations on the statements. Similarly, the Citation Typing Ontology (CITO)13 and the Bibliographic Ontology Speci cation (BIBO)14 are both catered more to traditional citation metadata. 3.1
Open Annotation-based Representation The structure of OA-based annotations allows for three alternative representations for the link from a recommendation, its evidence summary to the underlying evidence in (external) publications (Figure 2): 1. as a single annotation with multiple targets, 2. as two annotations, distinguishing the recommendation from the evidence summary, and 3. using three separate annotations for recommendation, evidence summary and evidence.
Approaches 1 and 2 have some drawbacks. The rst approach is concise, but makes it di cult to distinguish between recommendation, summary and evidence. The second approach confuses representation and annotation by saying that the summary is an annotation: who should be listed as the author of the annotation? Is that the author of the paper, or the creator of the annotation?
The third approach o ers the most ne grained control over the link between the various parts of a guideline and express more detailed information about the relation between the summary and the evidence. We distinguish three types of annotations:15. 12 See http://code.google.com/p/swan-ontology/ 13 See http://purl.org/spar/cito 14 See http://purl.org/ontology/bibo/ 15 The `d2sa' namespace is de ned as http://aers.data2semantics.org/vocab/annotation/. d2sa:RecommendationAnnotation oa:Annotation d2sa:EvidenceSummaryAnnotation d2sa:EvidenceAnnotation { An instance of d2sa:RecommendationAnnotation is an annotation for the recommendation text in the guideline. Figure 2 shows an example using an OA oax:TextSelector to identify the recommendation in the guideline text (the target of the annotation). We will use a d2sa:hasEvidenceSummary property to relate it to the evidence summary. { The d2sa:EvidenceSummaryAnnotation captures the relevant summaries for individual recommendations. A single recommendation may have multiple underlying evidences. The annotation uses a swan:referencesAsSupportingEvidence property to relate it to one or more evidence annotations.16 { Finally, the d2sa:EvidenceAnnotation is an annotation on the document that contains the evidence described in the evidence summary (not on the guideline). In its simplest form, it is an annotation (without body) on the evidence document as a whole. We can also make it more expressive by adding a selector for the actual text that provides the evidence.
The relation between a recommendation, its evidence summaries and underlying evidence is depicted in Figure 3. We believe that the separation between annotations on the guideline and annotations on the underlying evidence is a good thing. High granularity allows for expressive relations between evidence, summaries and recommendations. It has the advantage that evidence becomes a rst class citizen, rather than just the target of an evidence summary annotation. For instance, we can reuse the evidence annotation as supporting some claim or hypothesis in yet another publication. More importantly, we have encountered examples where the evidence cited in an evidence summary may be a recommendation by itself, that in turn refers to some evidence.
A drawback is that increased expressiveness and verbosity go hand in hand:
a single recommendation-summary-evidence chain (with evidence selector) is
captured as 26 triples. In practice, every annotation will maintain a link to a
timestamped, cached resource (a oa:State) on which the annotation was
originally made. Adding an additional 4 triples per annotation.
16 For the SWAN ontology, see http://code.google.com/p/swan-ontology/.
d2sa:RecommendationAnnotation
oa:Annotation
d2sa:EvidenceSummaryAnnotation
skos:ConceptScheme
Evidence Quality The multitude of evidence rating schemes [
We represent the strength of recommendation as an annotation that has both the d2sa:RecommendationAnnotation and d2sa:EvidenceSummaryAnnotation as target (Figure 4). The quality of evidence when mentioned separately, can be an annotation to the evidence summary annotation (if it is an aggregated quality indication) or to a single d2sa:EvidenceAnnotation. The body of the annotation contains the strength or quality itself. We represent the strength and quality codes as instances of skos:Concept, belonging to a skos:ConceptScheme that represents the rating scheme being used. Using SKOS allows us to use lightweight relations between ratings without having to commit to expressive formal semantics.17 For instance, we may say that a rating A-II is skos:broader than A-I (for retrieving all A-II grade and stronger recommendations) without committing to a set-theoretic semantics that says that all A-I grade recommendations are necessarily also A-II grade. 4
In the preceding sections, we analyze the structure of declarative evidence based guidelines, and introduce a lightweight ontology for making the structure of these guidelines explicit. The analysis shows that guidelines are often incomplete and even inconsistent, supporting the argument for an annotation-based approach. The ontology allows us to break down guidelines and evidence papers into their basic parts, and relating these parts to allow for a chain connecting 17 Simple Knowledge Organization System, http://www.w3.org/2004/02/skos/. every recommendation to its underlying evidence. We can provide context to a recommendation.
We used the ontology to add guideline and recommendation information to
Hubble [
data2semantics.org/. in a 4Store triple store. 19 An example recommendation represented using the lightweight ontology can be browsed from http://bit.ly/RecommendationExample (see also Figure 5).
The Hubble CDS is patient centric, that is, it presents relevant information based on a patient description (the top half of Figure 6). Since guidelines can be quite abstract, we used the BioPortal annotator to annotate all documents in our repository, and converted the annotations XML to the OA RDF format. UMLS CUI identi ers allowed us to link BioPortal output to LinkedLifeData.com and from there on to other Linked Data resources, including AERS-LD. The similarity between patients and scienti c publications allows us to retrieve all recommendations grounded in a publication. These recommendations are listed in the bottom half of the Hubble UI, ordered by relevance. Following a drill-down model, selecting a recommendation shows the underlying evidence summaries, and selecting a summary shows the underlying evidences. The `arrow` icons link to the RDF resource in the triple store, or, if applicable directly to the evidence paper. 19 See http://4store.org.
Future work includes the addition of annotations for clinical questions as
these form the primary entry point for clinicians. Secondly, we aim to support
automatic annotation of guidelines using this ontology, either through a
patternbased approach [