ICBO 2014 in Review

We wrapped up ICBO 2014 late yesterday afternoon with an "unconference" session, whereby attendees guided the topics and discussion of the final hour of the program prior to the closing session.

A couple notes:
1. Fred Trotter requests a "10 commandments" of ontology development.
2. Jim Overton says our tools for ontology development and curation are in terrible shape.  He received a round of applause, and it wasn't the applause that came at the end of his two minute spark talk.  It was instead widespread recognition of the problem.

Congratulations to Dr. Werner Ceusters, who won the best paper award for his paper and presentation on Pain Assessment Terminology in the NCBO BioPortal: Evaluation and Recommendations.  The award is sponsored by the National Center for Biomedical Ontology, and Dr. Ceusters will receive a $500 prize for the award.

Thank you once again to our sponsors.  In addition to NCBO, we thank NASA, Apelon, and DiBS (Data is Beautiful Solutions).

Thank you once again to our supporters.  We could not have had such a well-oiled, efficient, smoothly-running conference without Health 2.0 Houston.   We also received support from Houston Technology Center, and the School of Biomedical Informatics at UT Houston Health Science Center.

The 5th International Conference on Biomedical Ontology kicks off tomorrow

I'm here in Houston with my co-organizer, Sivaram Arabandi, sitting outside on a beautiful day as we put the finishing touch on what promises to be an outstanding program for the 5th International Conference on Biomedical Ontology (ICBO14).

ICBO14 kicks off Monday, Oct 6th with workshops and tutorials.  We have four--yes four--workshops that deal with the ontology of drugs and drug interactions.  There are also workshops on the ontology of biobanking, the OBO Foundry, and definitions in ontologies.  We also have tutorials on the OBO Foundry and mapping relational databases to the Resource Description Framework.

Our keynote speakers are highly distinguished.  Dr. Warren Kibbe--currently the the director of the Center for Biomedical Informatics and Information Technology (CBIIT) at the National Cancer Institute (NCI)--will give a dinner keynote on October 8th entitled Supporting Precision Oncology using Ontologies.

Also giving a keynote address is John Wilbanks--Chief Commons Officer of Sage Bionetworks, who will talk about Ontologies and Open Collaboration.  John has also worked at Harvard’s Berkman Center for Internet & Society, the World Wide Web Consortium, the US House of Representatives, and Creative Commons. John is a past affiliate of MIT’s Project on Mathematics and Computation and also started a bioinformatics company called Incellico, which is now part of Selventa.

We have a great scientific program with full-length and early-career papers, posters, software demonstrations, invited updates, and panels.  We are especially looking forward to a panel on ontology and genomics with Christian Stoeckert, Bjorn Peters, and Jim Zheng of the Center for Computational Biomedicine at the School of Biomedical Informatics here in Houston.

If you are not already registered or planning to come, you're going to miss out on a fantastic program!

Age is not a quality

No matter how many times lazy ontologists represent age as a quality of a human, organism, or other material (or immaterial I suppose) entities, it just isn't true.  Age is not a quality.

An age is a measurement of elapsed time since a particular entity came into being.  Typically, it is the measurement of elapsed time in the frame of reference of the entity itself.  For example, muons have a lifetime of 2.2 microseconds as measured from the frame of reference of the muon itself.  However, as measured from the frame of reference of the surface of the Earth, the muons entering the atmosphere have a mean lifetime of approximately 18.5 microseconds.

Therefore, age should be a type of measurement in ontologies.

We also note that age is always with respect to the time the measurement is made.  For example, if John Doe was born on Jan 1, 1970 UTC, then if we measure his age on Jan 2, 1970 UTC, we will obtain that John is 24 hours old.  But if we measure his age on Jan 1, 1971, we will obtain that he is 365*24 =8,760 hours old (1970 not being a leap year).

So an age measurement should always be accompanied by an appropriate measurement date/time.

Plant Ontology and Ontology of Biomedical Investigations Admitted to OBO Foundry

The OBO Foundry has announced that it has admitted two additional ontologies to its membership.  Specifically, the Ontology of Biomedical Investigations (OBI) and the Plant Ontology (PO) met the standards of the OBO Foundry Editorial Working Group.  This move brings the number of Foundry ontologies to ten:

1. chemical entities of biological interest
2. biological process (gene ontology)
3. cellular component (gene ontology)
4. molecular function (gene ontology)
5. protein ontology 
6. phenotypic quality
7. xenopus anatomy and development
8. zebrafish anatomy and development
9. plant ontology
10. ontology of biomedical investigations

Per the announcement, ...OBI and PO have recently been reviewed by the OBO Foundry Editorial

Working Group, and reviewers found that they performed well when measured against accepted OBO Foundry Principles... Consequently, the Editorial WG recommended that PO and OBI be given full

member status as OBO Foundry Ontologies. This decision was ratified by the OBO Foundry Operations Committee.

Progress towards high-quality, peer-reviewed ontologies deemed acceptable for use in scientific and other endeavors thereby has taken a strong step forward.

Glossary designers cannot define components of glossaries

The International Organization for Standardization (ISO) is working on standard TS 17439 Health Informatics Common Glossary Metadata Repository and Maintenance Process, which aims to define the key components of glossaries in health informatics, so that all such glossaries will be interoperable.

However, the glossary people are having a hard time defining the key components of glossaries.

For example, the definition of 'term' is: The word or group of words being defined in the glossary.

This definition implies that terms do not exist unless they are in a glossary, which is clearly untrue.  Terms existed long before people put them in glossaries.

The definition of 'term ID' is:  A computer generated unique identifier for this instance of the term.

Why is it a necessary condition for term IDs that they be computer generated?  What if a human manually creates an identifier for a term?

Furthermore, what does it mean to have different "instances" of a term?  It appears that it means the appearance of a term in a particular health standards document.  So if the same term appears in two different standards with different definitions in those standards, it gets two identifiers.  But then how does one know that it's the same term?  Uniqueness of the string of characters that make it up?  But then how will one compare the definitions of 'arm pain' and 'pain in the arm'?

The definition of 'context' is: Specialisation context

That is like defining 'automobile' as 'blue automobile'.

Hopefully the glossary makers can get their own definitions right before this proposed standard is approved.

Cardiology Society (non) Defines Key Entities for Cardiovascular Care

The American College of Cardiology (ACC) and the American Heart Association (AHA) are to be lauded for their goal of creating standard definitions of key terms for use in collecting scientific data.

However, their execution is--quite frankly--abysmal.

Here are some definitions they generated:

Sex	Indicate the patient's sex at birth as either male or female. Choose 1 of the following: Male Female
Date of birth	Indicate the patient's date of birth.

These are NOT definitions at all.  We have been misled.  These are instructions, however obvious.  What did the ACC and AHA think someone might do when confronted with a data entry form asking for the patient's sex and date of birth?

For real definitions of sex and date of birth, and a standard way of recording them that requires no special data machinery specific to demographics, see a paper by Hogan et al. from the 2011 International Conference on Biomedical Ontology.  These definitions have been captured in the Demographics Application Ontology, available here.

But since the real goal is to capture data elements specific to the field of cardiology, perhaps it is too unfair to criticize their efforts on demographics.   Cardiologists, after all, are more concerned with the heart than the rest of the person.

Alas, although they do move from instructions to definitions, their definitions are poor.  They are circular, meaning that they use the words in the term to define the term:

Prior angina	History of angina before the current admission. “Angina” refers to evidence or knowledge of symptoms before this acute event described as chest pain or pressure, jaw pain, arm pain, or other equivalent discomfort suggestive of cardiac ischemia. Indicate if angina existed >2 wk before admission and/or within 2 wk before admission.
Average number of episodes of angina in the prior week	Average number of distinct episodes of anginal pain that occurred in the last week before hospital admission or this visit

Furthermore, what if the patient is not being admitted to the hospital?  According to this definition, a clinician should not record "prior angina" if the patient had a history of angina prior to a clinic or emergency department visit.

The definition of "angina", alas, although promising, is ruined by the prefix "evidence or knowledge of".   If there is no evidence or knowledge of Mr. Smith's angina (perhaps he has no memory of it due to some mental disorder such as dementia), it still exists.  It also conflicts with the subsequent instruction to record the existence (not knowledge or evidence) of angina.

Furthermore, it really defines two data elements, despite being listed as one.  We are told to record existence (not knowledge or evidence) of angina before 2 weeks prior to admission (data element #1) and the existence (not knowledge or evidence) of angina within 2 weeks prior to admission (data element #2).

Thus the data element "prior angina" is also ambiguous.

The circularity of the definition of "average number of episodes of angina in the prior week" is self evident.  Note that now we are told that by "admission", the ACC and AHA probably in fact do intend to refer to outpatient encounters as well as inpatient ones.

Perhaps they should have stopped to define "admission", "visit", etc. first?  Well, if they had, it wouldn't have taken them long.  They could have adopted the definitions of these terms provided by the Ontology for General Medical Science, available here.

So we see that persons who are experts in a particular field of study, although necessary to the process of defining the terms they use, are not sufficient.

They should have consulted a good ontologist!

Standardizing Ontology a Top Priority for NICHD

The Eunice Kennedy Shriver National Institute for Child Health and Human Development held a series of "vision workshops" to create its vision for the next decade.

In a slide deck here (warning: PDF), the number one recommendation under the heading of "Transdisciplinary Science" is ...Standardize ontology, nomenclature, and data standards across disciplines.

The Director of NICHD, Dr. Alan Guttmacher, reitierated that recommendation today in a presentation to the Clinical and Translational Science Award Consortium Child Health Oversight Committee (CC-CHOC). The CC-CHOC meeting had a theme of "Quality Data from Pediatric Trials".

Speakers throughout the day emphasized the need for development and use of standards for collection of data in pediatric research.