The IBM Whiz Kid's Work on Cancer

Watson's foray into oncology is only the first baby step toward applying "big data" to thorny medical problems.

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Can IBM's supercomputer become the ultimate medical assistant?

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Back at Sloan-Kettering, the Watson initiative aims to help physicians arrive at the most effective possible cancer care. Taking into account Yamato's preferences (she has young kids and so prefers a drug regimen that is less time-intensive, and also would like to avoid losing her hair) and the results of the tests (a clear MRI and the identification of the mutation driving her tumor), Watson's confidence in the options changes. After the revelation that she's been coughing up blood, it changes again. Now Watson can identify the best option, a regimen of three particular drugs. It can also suggest clinical trials for which Yamato may be eligible.

Let's be clear: Watson will not be present at your next physical. Training the computer on each disease currently takes many months, though the process is getting faster as the computer learns more. And teaching Watson to make sense of doctor lingo is an ongoing challenge. Time stamps on faxes can throw off the computer, and words can have multiple meanings. "T2" can refer to a stage of cancer, the second thoracic vertebra or one component of an MRI scan, says Kris. It's early days yet for Watson.

But all the "ifs" aside, the potential to spread the specialized knowledge of a major cancer center far and wide is "tremendous" once more advanced commercial versions are widely available, says Kris. The first iteration of an oncology tool, cloud-based and accessible via computer or tablet, is being tried out at two medical practices in Maine and New York; it won't yet have the full capabilities being tested on Yamato. Insurer WellPoint will ultimately market the product.

At some point, too, Watson will assist doctors with diagnosis. Imagine a primary care doctor seeing a patient with a befuddling constellation of symptoms: double vision, dry mouth, slurred speech. A supercomputer could help winnow the list of possible diagnoses and remind her of some she might otherwise overlook (like botulism).

Beyond diagnosis and treatment, plenty of other challenges might be addressed by new kinds of number-crunching. Take disease surveillance. If a kid comes in complaining of a sore throat, "what I'd really like to know is if strep throat is going around," says Kenneth Mandl, a researcher and physician in the Boston Children's Hospital Informatics Program.

In a study published in 2011, Mandl and his colleagues showed that real-time regional patient data from outlets of the retail chain MinuteClinic gave a good indication whether the next person to walk in was more likely to have strep or just a virus. That could cut down on unnecessary antibiotic prescriptions and ensure that people who do have strep get tested and treated.

And there are entirely new data streams to wrangle, thanks to people's constant connection to the Internet, social media and their beloved mobile devices. A platform created by the Cambridge, Mass.-based startup Ginger.io allows researchers and patients to use smartphone data to passively track (and ideally gain insights from) the movements and behaviors of patients with conditions such as diabetes and inflammatory bowel disease. If someone is repeatedly unlocking his phone at 3 a.m., say, it means he's not sleeping and may not be feeling well. The same may be true if he usually moves around a lot during the day but suddenly sticks close to home.

It becomes "a lot easier to actually track symptoms if we can rely on the digital exhaust from your cellphone," says Michael Seid, a professor at Cincinnati Children's Hospital Medical Center who is using the Ginger.io app to improve the care of young IBD patients. By correlating their activity levels and call patterns with reported symptoms, doctors might be able to send an alert when a medication change or a visit seems to be in order. They might even be able to predict an IBD flare-up days in advance and prevent it.

Using past patterns of data as a crystal ball has already showed promise in other settings. A study published in 2009 found that a computer model could rely on previous entries in a patient's medical record, such as substance abuse or poisoning incidents, to predict the risk of a future diagnosis of domestic abuse. IBM and Excel Medical Electronics are working with the UCLA Department of Neurosurgery to study whether real-time analysis of subtle changes in a host of different vital signs of traumatic brain injury patients can predict (and preclude) a dangerous increase in brain pressure.


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