Automating the Integration of Clinical Studies into Medical Ontologies

Abstract

A popular approach to knowledge extraction from clinical databases is to first define an ontology of the concepts one wishes to model and subsequently, use these concepts to test various hypotheses and make predictions about a person's future health and wellbeing. The challenge for medical experts is in the time taken to map between their concepts/hypotheses and information contained within clinical studies. Presently, most of this work is performed manually. We have developed a method to generate links between Risk Factors in a medical ontology and the questions and result data in longitudinal studies. This can then be exploited to express complex queries based on domain concepts, to extract knowledge from external studies.

Mapping longitudinal studies to risk factors in an ontology for dementia

“…The NLTK approach was described in [28] and is briefly discussed again here for comparison with our more recent approach (discussed in Section 4.2). The method was to find synonyms for all keywords and use this larger keyword set to map to as much of the clinical study as possible.…”

“…The final phase of matching the ontology to the target clinical study is the primary focus of this paper, and is described in depth in the following section and builds on previous work [28]. In brief, the goal is to link each ontological concept (Dementia Risk Factor) with all relevant questions in the clinical study.…”

“…While there were a number of desirable outcomes that emerged from this approach, it resulted in a high number of false positives where inappropriate questions were matched to some risk factors. As a result a new approach was necessary where the goal was to reduce the false positives as much as possible, while still using the same methodology of matching in four iterations, our previous paper [28] can be referenced for what terms are used in each step.…”

“…Method 1 uses NLTK [21] and method 2 uses Lucene [17]. Both use the same four steps as outlined in our previous paper [28]. These are Concept Name Match, Concept Property Match, Vocabulary Match and Structural Match.…”

“…While ideas such as ontologies for managing healthcare surveys as proposed in [23] could greatly assist in matching new concepts to older datasets, the reality is that this type of structured approach to medical studies does not exist. In earlier work [28], we used WordNet [20,31] to generate synonyms at three descriptive levels for Risk Factors: risk factor name, risk factor properties, vocabulary associated with a risk factor. These synonyms together with the original terms were matched against all questions in the clinical study.…”