Terminologies and ontologies: concrete application examples
Terminologies and ontologies can improve healthcare, for example in drug therapy safety, but also in other use cases.
Although few people are aware of them, terminologies and ontologies play a major role in fields such as medicine.
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The use cases for terminologies and ontologies in medicine are not always trivial, but sometimes require quite complex algorithms. One of the biggest challenges is the processing of continuous or free text, which is still widely used in medicine.
This involves mapping phrases, sentences or entire sections with the corresponding concepts of a terminology. Often referred to in specialist literature as concept tagging or annotation, algorithms from the field of Natural Language Processing (NLP) are required for this.
Placeholders (*) and inheritance attributes(< (children of), << (element itself and children of), >> (element itself and parents of), > (parents of)) can be used and the complete syntax in the current version also includes the mapping of nested expressions, cardinalities and negations. A concept can be supplemented with a comment or label using two pipes.
In its simplest form, the expression <<* finds or expands to the total set of all "SNOMED CT" concepts. More practical would be this expression: <36989005|Mumps| which returns all (20) specializations of mumps.
Attribution can now be used to restrict such purely hierarchical queries based on the concept model. For example, all fractures that affect the femur can be represented with this syntax:
<125605004|fracture| : 363698007|where| =<71341001|on the femur|
In the case of SNOMED CT, this query currently returns 132 terms. If you replace the fracture (125605004) with an"*", you would get all 237 terms that have a topographical reference to the femur.
Interestingly, you can also invert this query and ask: Which bones can break? To do this, the focus term is simply used as the target term (note: use inheritance!) and the role is inverted by prefixing it with "R":
<* : R363698007 =<<125605004
There are almost 800 terms that satisfy this expression, i.e. structures in the human body that can break. A tool for trying out expressions can be found at https://browser.ihtsdotools.org/
Especially in German and in the field of medicine, there is a lot to master. Honeck et al. have summarized this very succinctly:
"Not only is the German language known for excessive single-word nominal compounding, but also its medical sublanguage, in particular, is characterized by a mix of Latin and Greek roots."
It is precisely at the NLP interface that potential synergies between the various AI approaches are now emerging for the first time, as the task of concept tagging can be significantly improved with large AI models (LLMs) and their methods. In particular, the processing of modalities, i.e. those elements of human language that allow assumptions to be made and hypotheses to be formulated, is now reliably possible for the first time. In future, rule-based NLP algorithms will only have an independent significance in marginal areas, for example when it comes to differentiated syntactic analyses.
"Clinical decision support system
When implementing a Clinical Decision Support System (CDSS), the aim is to provide assistance to a user, for example by drawing conclusions about diagnoses from symptoms or searching for similar patients, which can then be used as an example to plan and decide on a therapy. This requires special algorithms in the background, including so-called inference algorithms. In this article, the aspect of queries from ontologies will be explained, as there is still a clear advantage here compared to machine learning-based approaches.
The "expression constraint language" (ECL) was developed specifically for querying terms from SNOMED CT and analogously structured ontologies (SNOMED ECL). This allows formal expressions D⊆∃R.C to be represented and executed in the form focus term: attribute=target term.
Strengths compared to other approaches
The strengths compared to other approaches lie primarily in the extremely clear and efficient way in which certain facts can be defined and the traceability of the results.
In the case of "major heart surgery on children", for example, a CDSS can provide advance advice for an interdisciplinary board in order to minimize potential risks. Or, in the case of "infections in the abdomen", initiate a specific process to evaluate the cause. But how do you define "major heart surgery" and "abdominal infections"? Often simply by listing them.