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Exclusive: Watson is like a 'well-read medical student'

by Brendon Nafziger , DOTmed News Associate Editor

As far as the project with Watson, I initially had the privilege to work with IBM, even before the "Jeopardy" match, using the technology that IBM developed, which they refer to as DeepQA, deep question and answer. What's different and unique about the technology is rather than having a set of rules or situations that are predefined, Watson software is able to take advantage of the system's tremendously parallel, computational speed, to be able to take a database that exists in a location and create hypotheses and mine information from that database on the fly, without having any preexisting rules or suppositions. The advantage of that is as new data become available, you don't have to rewrite new rules, and it's able to discover things that people may not have thought to program into a system.

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I had been doing research with IBM for a number of years on a number of things related to medical imaging, and they asked me if I'd be interested in talking with the team that was working on a "cool" project. So at that point, it hadn't been announced to the public, but they introduced me to the "Jeopardy" team and to some of the strategies they were employing. And I mentioned that I thought it would be a fascinating technology with some fairly profound implications for medicine as well.

What are some of those implications?

Well, even though most of their efforts were homed in on the "Jeopardy" match, we started with a subset of their developers looking at how IBM could enhance the database and the software's ability to be able to answer questions in the medical space. As time has gone on, IBM has tested it against different types of quiz questions, for example, that are posed to internal medicine candidates for certification...And its knowledge in the medical domain has increased. At this point then, it's kind of like a really super smart, well-read medical student, maybe first year, maybe second year, and what it really needs is much more empirical experience.

How does it get that experience?

We've been working with IBM recently on taking deidentified or anonymized patient data with IRB approval, triple-checking to make sure all patient information is stripped, and then having the IBM team begin to look at what would be required to do natural language processing, and to do analysis, not only on structured data, such as the lab data and diagnostic codes, but also to look at unstructured data, such as progress notes, discharge summaries, admission notes, and radiology and pathology reports. So that's where we are with IBM currently. And what I'm hoping to do is connect the VA's database of more than 28 million patients, which is referred to as Vinci, and to essentially bring to bear the computational processing power of the IBM DeepQA technology on that.

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