AI And Machine Learning For Healthcare, Precision Wellness, Genomic Data Visualization

May 4, 2018 | James Hendler of the Rensselaer Polytechnic Institute is featured on this podcast from Cambridge Healthtech Institute for the Bio-IT World Conference & Expo. Hendler speaks about precision wellness as part of his research on artificial intelligence, agent-based computing, high-performance processing, and the Semantic Web, among other topics. Here is a sample of the discussion that takes place. Podcast

CHI: What role might AI-driven technologies have in enhancing data visualization in genomics, drug discovery or clinical developments?

James Hendler: Well that's another area that I think is really up and coming.

. . . if we move down to the researcher end, the person who's really trying to do the biomedical research, the life science or even just the predicting healthcare, what we see is that many, many of the hard problems sort of look like, if you've ever seen a social network grid, this person knows this person, this person, this person and this person bought this and this person likes that. . . those are the kind of information that has been getting Facebook in trouble with Cambridge Analytica and things like that where our ability to do these large network analyses, much of which is done using data analytics then presented in some kind of network visualization or something which is where the exploration can happen. . . people can look at that data and start to say, here's what we think is happening, or we may see a problem over here and then you sort of drill down into that cluster to start asking questions.

So the term data exploration is used when we say, taking this kind of data, pulling it together, looking at it in the early stages, forming some hypotheses and then going out and testing them.

So most of the machine learning work is essentially correlative. We see X and Y occurring together. Now, you know, it's a truism we all learned as kids that correlation doesn't imply causality. On the other hand, if you can't find correlation, the causal explanation may be pretty hard to defend. So what we can see is that we can rule out many possibilities. . . . But right now humans are still better at looking at this combination of data after it's been processed by the machine and saying, you know, I think something interesting is happening there.