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Ontology Summit 2016 Health Sciences - Thu 2016-04-07     (1)

Session Co-Chairs: Leo Obrst and Ram D. Sriram     (1A)

Abstract     (1B)

  • Healthcare and biomedical domains: a very large ecosystem of vocabularies and ontologies     (1B1)
  • Goal: To provide insight into the nature of semantic interoperability issues and approaches in the Healthcare ecosystem     (1B2)
  • Mission: Present issues of semantic interoperability and integration in domain of healthcare: Example: Semantic Electronic Health Record (EHR) and Systems     (1B3)
    • Discuss approaches in semantic interoperability and integration in healthcare: precoordination, postcoordination, mapping, anchoring, foundational ontologies and architectures, hybrid approaches     (1B3A)
    • Discuss approaches for achieving semantic interoperability of vocabularies and ontologies in healthcare     (1B3B)
    • Discuss gaps in current approaches in semantic interoperability in healthcare     (1B3C)
    • Discuss current/future challenges and prospects in semantic interoperability in healthcare     (1B3D)
    • Propose best methods for achieving semantic interoperability     (1B3E)

Agenda     (1C)

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Proceedings     (1E)

[12:38] Mark Underwood: Session hashtag #ontologysummit     (1E1)

[12:41] Ram D. Sriram: @Keith: Since the slides are in the PDF format, you can see the slide number on the left side of the information Bar on the top.     (1E2)

[12:43] LeoObrst: @keith: might be good to define "postcoordination".     (1E3)

[12:50] RaviSharma: Keith - I still do not get it, is post-coordination the final clinical analysis step after Snowmed+Loinc + RxNorm+ symantic interoperation with? etc.?     (1E5)

[12:52] Mark Underwood: I like this deck - Keith, if u send the original I can footnote pages for credits, or you can do it if you have time. Is there an affiliation to be supplied? Thanks!     (1E6)

[12:53] RaviSharma: Or is it reduction of contending Definitions?     (1E7)

[12:55] TerryLongstreth: I think it's formalization     (1E8)

[12:56] Sirarat Sarntivijai: in Example slide 19 - have these concepts reused reference ontologies?     (1E9)

[12:56] Sirarat Sarntivijai: e.g. systolic blood pressure can be represented with http://www.ebi.ac.uk/ols/beta/ontologies/efo/terms?iri=http%3A%2F%2Fwww.ebi.ac.uk%2Fefo%2FEFO_0006335     (1E10)

[12:57] Csongor Nyulas: The example expression on page 19 (and the same expression repeated on 21 and 23) are missing two closing parenthesis.     (1E11)

[12:57] Csongor Nyulas: Not sure where it was suppose to go     (1E12)

[12:57] LeoObrst: Postcoordination is similar to normalization.     (1E13)

[12:57] Keith Campbell: yes     (1E14)

[12:58] RaviSharma: Request Keith to write a paragraph on post coordination i.e. removal of contention.     (1E15)

[12:59] Keith Campbell: Has an entry for post-coordination.     (1E17)

[13:00] Sirarat Sarntivijai: I think the post-coordination approach is similar to what is called the "post-composed" of concept in biomed ontology domain - some example: https://www.ncbi.nlm.nih.gov/pubmed/?term=Reporting+phenotypes+in+mouse+models+when+considering+body+size+as+a+potential+confounder     (1E19)

[13:01] RaviSharma: from Snomed on Wiki: Precoordination and postcoordination[edit]     (1E20)

SNOMED CT provides a compositional syntax[16] that can be used to create expressions that represent clinical ideas which are not explicitly represented by SNOMED CT concepts.     (1E21)

For example, there is no explicit concept for a "third degree burn of left index finger caused by hot water". However, using the compositional syntax it can be represented as     (1E22)

[13:02] RaviSharma:     (1E24)

284196006 | burn of skin | :     (1E25)

116676008 | associated morphology | = 80247002 | third degree burn injury | ,     (1E26)

272741003 | laterality | = 7771000 | left | ,     (1E27)

246075003 | causative agent | = 47448006 | hot water | ,     (1E28)

363698007 | finding site | = 83738005 | index finger structure     (1E29)

Such expressions are said to have been 'postcoordinated'. Post-coordination avoids the need to create large numbers of defined Concepts within SNOMED CT.     (1E30)

[13:07] Donna Fritzsche: @barry - what are the pros/cons of EPIC ? Can you give us more of a description?     (1E31)

[13:11] Donna Fritzsche: thanks Davide     (1E33)

[13:19] RaviSharma: EHR has to help physicians during need to quickly diagnose such as ER and also maintain individual's health history and baseline (blood type, allergies)so does EPIC do this. can there be reverse engineering?     (1E34)

[13:22] Xavier Gansel: any link between snomed and bfo that you are aware of ?     (1E35)

[13:24] Ram D. Sriram: @Ravi: We will discuss off-line wha the govt. is doing on this.     (1E36)

[13:25] gary berg-cross: The phrase "starting from scratch' may be misleading. Avoiding (sceratching) dead ends based on legacy systems more of the issue and proceeding from some of the investment is well founded ontological models and methods may be the better direction which isn't an intellectual scratch point.     (1E37)

[13:26] RaviSharma: Ram - thnks - sorry for being forceful, was keen to learn Barry's view, will email or call.     (1E38)

[13:27] RaviSharma: Barry - unless accuracies and ambiguities in EPIC are removed, how does diagnosis improve/     (1E39)

[13:31] LeoObrst: Thanks, Keith and Barry.     (1E40)

[13:32] FrankOlken: I agree with Barry Smith that EPIC is not a good starting point for a rigorous EHR system.     (1E41)

[13:33] Sirarat Sarntivijai: @RaviSharma - if i may? there are a few major types of ontology...BFO is what is usually seen as the "upper" structure ontology where it links different types of 'occurent' and 'continuant' together, then there is a "specific-domain" ontology (for example, cell ontology described in vivo cell types, UBERON describes human anatomy). Then there is the last type called the "application" ontology, where different things (e.g. organism, disease, phenotype, cell type, genes, anatomy) are put into relations to each other. My understanding is, IDO-core for example, are the core application ontology that gets expanded to describe specific infectious diseases (e.g. IDO-Malaria, IDO-Dengi), Ontology for Biomedical Investigation (OBI), or the Experimental Factor Ontology (EFO) are other examples of "application" ontologies (i.e. EFO is an application ontology that reuses OBO foundry ontologies to describe different experiment components, but then creates "relationship" between those obo ontology components with EFO semantic structure)     (1E42)

[13:34] RaviSharma: Amit-inspite of such data how well is depression behavior understood, is it reaching mostly agreed levels of acceptance by psychologists?     (1E43)

[13:34] Donna Fritzsche: jinx leo     (1E44)

[13:38] RaviSharma: Amit- slide 15 answers part of my Q.     (1E45)

[13:38] BobbinTeegarden: So isn't a '4th' option to create a medical exchange infrastructure, like a superhighway, among medical ontologies, and have it 'autonomically' integrate new terms etc (SKOS like integration)? Is it reasonable to assume arrival at a new one-and-true alternative?     (1E46)

[13:42] ToddSchneider: Is one of the interoperability challenges in the 'medical' field related to the integration across different communities, medical practitioners, medical research, biological research, and pharmacology?     (1E47)

[13:42] Sirarat Sarntivijai: "shortness of breath" and "dypsnea" can both be represented with appropriate ontology terms - have you thought about aligning these concepts to existing ontologies (which will provide you with much more information about the concept other than RDFS:label...i might have missed it if you've mentioned it already)     (1E48)

[13:45] Amanda Hicks: Can you say more about the tools you use for semantic annotation?     (1E49)

[13:45] RaviSharma: Great     (1E50)

[13:45] Sirarat Sarntivijai: the real use case of ontology at data integration work - https://www.targetvalidation.org/about     (1E51)

[13:46] Sirarat Sarntivijai: released a few months ago to the public, all data used in this project were mapped to one central application ontology to facilitate data integration     (1E52)

[13:46] Nolan Nichols: Is there a link to the Drug Abuse Ontology?     (1E53)

[13:55] Amit Sheth: Nolan: link to DAO is from here: http://wiki.knoesis.org/index.php/PREDOSE     (1E54)

[13:56] RaviSharma: Leo - As we often cross domains in Health such as Research, genomics, drugs, clinical tests, imaging, Accepted lines of treatments and alternative treatment options, etc.; these obviously have at best overlapping or sometimes least common vocabularies and terminologies, therefore ontology integration (semantics) has to have similar to what speakers showed venn-diagram like common vocabularies? Any comments on who like ohio and buffalo groups do this well as what tools?     (1E55)

[13:57] Amit Sheth: Sirarat: informally, but not formally-- we have extracted relevant knowledge from say UMLS and other public sources, but are not actively managing alignments (as we do not have immediate needs)     (1E56)

[13:58] Amit Sheth: Amanda: we have used variety of tools and techniques for semantic annotation: eg. SSN ontology give recommendation on how to annotate (largely developed by Cory Henson, my former student who was active in W3C SSN activity)     (1E57)

[14:01] Amit Sheth: public service like Alchemy API, Zemanta have been used, as well as our in house developed tool Kino: http://wiki.knoesis.org/index.php/Kino (see "NCBO integration" for Kino's used for biomedical ontology based semantic annotations)     (1E58)

[14:04] RaviSharma: Leo - is it possible to have ontologies filter items through preprocessors (knowledge based) reasoners etc to then submit external Queries that define the Business process flow for patient outcome that you described. This would then operate on existing large areas such as SNOMED and or EPIC?     (1E60)

[14:04] SteveRay: Regarding Leo's slide #4 on the dimensions of interoperability, my favorite description of those dimensions can be found beginning on page 31 of http://www.nist.gov/msidlibrary/doc/AMIS-Concepts.pdf     (1E61)

[14:04] Sirarat Sarntivijai: UMLS is a silo of terms...might not be ideal to use as a reference terminology for data integration, just my opinion     (1E62)

[14:07] Nolan Nichols: thanks, Amit!     (1E63)

[14:08] Mark Underwood: Amit - we had no luck getting Alchemy to present here :(     (1E64)

[14:08] BobbinTeegarden: @Leo is any of this work avaliable to the public?     (1E65)

[14:09] Donna Fritzsche: @Leo - any comments on the form of the future-state Rules? Language, Process, etc     (1E66)

[14:11] Mark Underwood: @Leo Does the patient have an active role in "patient-centered" e.g., to annotate or correct? Maybe "patientcentered" refers to a spoke-and-wheel notion but perhaps the patient is actually not present?     (1E67)

[14:12] FrankOlken1: Leo, please send me your contact info (telephone, email) to my email address)     (1E68)

[14:12] FrankOlken1: My email is frankolken@gmail.com     (1E69)

[14:17] LeoObrst: @MarkUnderwood: no, the patient needs to be involved.     (1E70)

[14:23] RaviSharma: Leo and Todd and Sirarat - the real life in healthcare is accelerated learning and knowledge evolution and related changes in treatment, with standards and ontologies hopefully helping these, the speed of change is to be recognized. Analogous to multiple moving windows / targets (of opportunities).     (1E71)

[14:24] Mark Underwood: [13:45] Sirarat Sarntivijai: the real use case of ontology at data integration work - https://www.targetvalidation.org/about     (1E72)

[14:25] Mark Underwood: Sirarat - thx     (1E74)

[14:26] RaviSharma: Amit- you have done great work in being able to bring relevant information from mammoth Big Data Sources, any comments on relative accuracies or top two or three sources?     (1E75)

[14:28] LeoObrst: @FrankOlken: ok, will do.     (1E76)

[14:30] RaviSharma: Amit - we need better and CNL based parsers in large text data, wait we will also start getting voice notes also?     (1E77)

[14:31] LeoObrst: @DonnaFritszche: re: rules, etc. Yes, we are shortly to begin on our second stage, focusing on using Prolog and its variants for rule reasoning. We hope to explore especially goals, utility notions, preferences of patients, quality value spaces related to those.     (1E78)

[14:31] Mark Underwood: @Amit - Good point about data quality / curation - Sys often designed assuming data is perfect, only design in backend correcting     (1E79)

[14:31] Donna Fritzsche: Thanks Leo!     (1E80)

[14:34] Donna Fritzsche: Annual RuleML Conference - july 6 - 9/ Search RuleML 2016 for info.     (1E81)

[14:35] Donna Fritzsche: (per Frank)     (1E82)

[14:36] RaviSharma: Amit - great comments - thanks.     (1E83)

[14:37] Mark Underwood: http://2016.ruleml.org/     (1E84)

[14:40] FrankOlken: For those interested in rule systems, see the upcoming RuleML conference at SUNY Stony Brook in New York on July 6-9.     (1E85)

[14:40] Amit Sheth: twitris.knoesis.org     (1E86)

[14:40] Csongor Nyulas: thanks     (1E87)

[14:42] FrankOlken: For NSF funding of semantic web and ontology tool development contact Nan Zhang nanzhang@nsf.gov     (1E88)

[14:44] Amit Sheth: Doozer     (1E89)

[14:44] Mark Underwood: @Frank - thanks so much     (1E91)

[14:45] Csongor Nyulas: @Amit Really great stuff!     (1E92)

[14:45] FrankOlken: For biomedical and healthcare IT funding from NSF contact either Sylvia Spengler or Wendy Nilsen via email. Sspengle@nsf.gov or wnilsen@nsf.gov     (1E93)

[14:46] Amit Sheth: amit@knoesis.org     (1E94)

[14:46] RaviSharma: Leo and Ram - thanks     (1E95)

[14:46] Amit Sheth: or skype: amitpsheth     (1E96)

[14:47] Donna Fritzsche: Thanks Ram and Leo !     (1E97)

[14:47] Amit Sheth: thanks     (1E98)

[14:47] Mark Underwood: Added RuleML to the event list at ontologysummit.org     (1E99)

[14:47] LeoObrst: Thanks to our speakers and participants!     (1E100)

[14:47] Donna Fritzsche: And Speakers!     (1E101)

[14:47] Alan Ruttenberg: Great session, thanks!     (1E103)

Attendees     (1F)

Audio Recording     (1G)