Operations, machine learning and premature babies: An astonishing connection between web ops and medical care. By Mike Loukides.
From the post:
Julie Steele and I recently had lunch with Etsy’s John Allspaw and Kellan Elliott-McCrea. I’m not sure how we got there, but we made a connection that was (to me) astonishing between web operations and medical care for premature infants.
I’ve written several times about IBM’s work in neonatal intensive care at the University of Toronto. In any neonatal intensive care unit (NICU), every baby is connected to dozens of monitors. And each monitor is streaming hundreds of readings per second into various data systems. They can generate alerts if anything goes severely out of spec, but in normal operation, they just generate a summary report for the doctor every half hour or so.
IBM discovered that by applying machine learning to the full data stream, they were able to diagnose some dangerous infections a full day before any symptoms were noticeable to a human. That’s amazing in itself, but what’s more important is what they were looking for. I expected them to be looking for telltale spikes or irregularities in the readings: perhaps not serious enough to generate an alarm on their own, but still, the sort of things you’d intuitively expect of a person about to become ill. But according to Anjul Bhambhri, IBM’s Vice President of Big Data, the telltale signal wasn’t spikes or irregularities, but the opposite. There’s a certain normal variation in heart rate, etc., throughout the day, and babies who were about to become sick didn’t exhibit the variation. Their heart rate was too normal; it didn’t change throughout the day as much as it should.
That observation strikes me as revolutionary. It’s easy to detect problems when something goes out of spec: If you have a fever, you know you’re sick. But how do you detect problems that don’t set off an alarm? How many diseases have early symptoms that are too subtle for a human to notice, and only accessible to a machine learning system that can sift through gigabytes of data?
The post goes on to discuss how our servers may exhibit behaviors that machine learning could recognize but that we can’t specify.
That may be Rumsfeld’s “unknown unknowns,” however we all laughed at the time.
There are “unknown unknown’s” and tireless machine learning may be the only way to identify them.
In topic map lingo, I would say there are subjects that we haven’t yet learned to recognize.