![](bg5.png)
expect a possibility of the discrimination between rel-
evant and non-relevant cases.
Making good use of existing mappings in the clin-
ical domain (e.g., SNOMED-CT and other ontolo-
gies) is also important. The benefit from existing
mappings includes detailed evaluations and character-
ization of mappings. In other words, it is feasible to
generalize the mapping knowledge from a gold stan-
dard and apply the knowledge on different mapping
tasks.
It is possible to compare all items of MDS and
ICF exhaustedly. However, for larger ontologies such
as SNOMED-CT, such approach will be obstructed
by computational issues. An investigation on candi-
date generation for clinical ontology mapping is also
a need. It is also worthy to find a mapping method
leveraging structural information when two ontolo-
gies are both well-structured.
As clinical ontology mapping requires curation
by domain experts, utilizing checking results by live-
feedback mechanisms is useful. A semi-supervised
mapping method will help to improve the mapping
quality.
Lastly, multiple ontologies are preferable because
it increase the coverage of assessment sheets. How-
ever, such advantage comes with challenges, includ-
ing ontology merging and deduplication. Further-
more, even when using more ontologies, it is not guar-
anteed a 100% coverage because assessment sheets
are customized for a local society. Such problem hap-
pen when we work on other health care systems as
well. Therefore, it is important to study how to define
new items with sufficient logical relations.
4 CONCLUSION
We described an ongoing research project in care
plan recommendation system in Japan and its prelim-
inary step, which is the mapping of clinical ontolo-
gies. We proposed a straightforward yet reasonable
method for mapping MDS and ICF ontologies. Al-
though there are still many challenges to overcome
as discussed, we envision an optimistic form of the
direction. We hope our study could contribute to im-
proving the quality of healthcare in different societies.
ACKNOWLEDGMENTS
This research is funded by Welmo inc.
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