Evidence-based medicine intends to optimize clinical decision making by using evidence. Semantic query answering could help to find the most relevant evidence. However, at point of care, it still lacks time for clinicians to do literature study. In this poster, we propose to build an evidence graph for clinical decision support, in which an evidence ontology is defined with extension of SWRL rules. On top of this graph, we do evidence query and evidence fusion to generate the ranking list of decision options. Our prototype implementation of the evidence graph demonstrates its assistance to decision making, by combining a variety of knowledge-driven and data-driven decision services.
Providing clinical decision support needs evidence [
First is how to find the most relevant evidence. A general solution is a search engine, which requires (1) the evidence representation, (2) the evidence extraction, (3) the evidence indexing. Considering that evidence has semantics (e.g. disease hierarchy, drug interaction), the basic keyword-based search is not enough, and we propose to build an evidence graph for answering semantic queries.
Second is how to use the found evidence to provide a personalized clinical decision support. The state of the art solution is to rank the evidence search results for human reading, however, at point of care, clinicians rarely have time to do literature study. For instance, at point of care, given a patient with non-rheumatic atrial fibrillation, the clinician wants to know the patient’s risk score of stroke, instead of reading papers – even if the most relevant papers such as the CHA2DS2-VASc study have been retrieved. Therefore, our evidence graph aims to making evidence machine understandable and processable, for giving the ranked decision options (instead of the ranked documents).
Description This class represents evidence sources from external files, web pages or data sets (e.g. the NICE guideline pdf file, the Framingham Heart Study web page, the CHARLS data repository).
This class represents code systems (e.g. disease codes in ICD 10, lab test codes in LOINC).
This class represents the population studied in the evidence (e.g. Chinese retired residents, or overweight diabetes patients).
This class represents the intervention studied in the evidence (e.g. insulin therapy, or life style intervention).
This class represents the comparison studied in the evidence (e.g. placebo, or an alternative intervention) This class represents the outcome studied in the evidence (e.g. death, or hospitalization) This class represents features defined by logical and/or arithmetic expressions of terminologies (e.g. the feature of elder is defined as: age>=60, the feature of male is defined as: gender=male).
In Figure 1, the left hand side is the 11 properties of 8 classes, such as hasSource (from Evidence to Source), hasPICO (from Evidence to P,I,C,O), hasFeature (from P,I,C,O to Feature), hasTerminology (from Feature to Terminology). The right hand side illustrates an evidence instance, which has data source from the Chinese Health and Retirement Longitudinal Study (CHARLS1). It has a population instance, namely retried, with 4 feature instances, i.e., gender=male, gender=female, age>=55, age>=60, followed by two terminology instances, namely gender and age. The retried population has an expression: ((gender=male) and (age>=60)) or ((gender=female) and (age>=55)). Besides, this evidence instance has attributes of id, name, code and restService, etc., where the restService is the URI of its decision service implementation.
Here, we remark two key points in the evidence graph. One is the evidence semantic query answering, and the other is the evidence fusion for decision option ranking. Actually, the semantic part is nature for our evidence graph, which is defined by an ontology with extension of SWRL rules. Below is a SWRL sample for evidence inference. Evidence(?e1) & hasPopulation(?e1, ?p1) & hasFeature(?p1, ?f1) & hasTerminology(?f1, ?t1) & Evidence(?e2) & hasPopulation(?e2, ?p2) & hasFeature(?p2, ?f2) & hasTerminology(?f2, ?t2) & subTermOf(?t1, ?t2) => subEvidenceOf(?e1, ?e2)
From the ICD 10 code system, we have subTermOf(Stroke, CardiovascularDisease), which implies that, given a patient with diagnosis of non-rheumatic atrial fibrillation (AF), the risk models of stroke and other cardiovascular diseases (CVD) could be all applicable for him/her.
The fusion part is novel, via calling the restService URI of each retrieved evidence.
Taking the above AF patient as an example, both the CHA2DS2-VASc evidence (for
stroke risk model) and the Framingham evidence (for CVD risk model) have been
retrieved. The CHA2DS2-VASc evidence service takes input features of hypertension,
diabetes, congestive heart failure, age >= 75, pre stroke or transient ischemic attach or
thromboembolism, and outputs the stroke risk score from 0 to 6. The Framingham
evidence service takes input features of diabetes, smoking, age, systolic blood pressure,
total cholesterol, HDL cholesterol, and outputs the CVD risk score from 0 to 1. These
different evidence services will be activated (most possibly by input of different
features), and generate scores of different decision options. Finally, we will do a decision
fusion of evidence services, which could leverage fusion algorithms such as majority
voting, weighted average, and meta-classification [
An evidence graph for clinical decision support is appealing and challenging. This poster presents an early phase, and we have manually built an evidence graph, including 4 sources from Framingham, CHA2DS2-VASc, NICE, and CHARLS. Based on that, we implemented the Decision Advisor with integration of a care management system (namely Curam2), and Figure 2 is the screenshot of a patient’s care plan. At point of care, suppose that a patient Cao Ping comes, and 3 objectives (a.k.a. outcomes) are selected. By semantic query answering on the evidence graph, we retrieve out the evihttp://charls.ccer.edu.cn/en http://www-01.ibm.com/software/info/curam/ dences applicable for Cao Ping. After posting the patient data to each evidence restService URI, 5 life-style intervention options are respectively scored by different evidence services, and the decision fusion finally gives the ranked list. Social activity has been first recommended to Cao Ping, with the highest rank score.
Care plan and objectives defined in Care Plan Mgmt System
Case worker can select one or more objectives to see decision advisor result Options are from care plan mgmt. system.
List ranking score of each option for this care plan towards the selected objectives.
Case worker can take use of the result to make selection for planning.
In future work, we are planning to release the evidence graph of large scale. First of
all, we will leverage Bio2RDF [