5Personalization: Suitable for everyone

       “No one can get outside his own individuality.” ¹

      Arthur Schopenhauer, philosopher

      (1778–1860)

      Felix is unique.² For six years, the 10-year-old from California has been continuously sending data into the cloud: pulse, stress level, exercise activity, blood sugar. Every day, tens of thousands of data points are collected by his various devices. Felix is probably the best-measured diabetes patient in the world. He is probably also one of the children whose diabetes is best managed. After all, a computer permanently evaluates all the data it receives from the boy’s smartwatch and his blood sugar monitor. An algorithm uses the information to calculate a therapy tailored to his exact needs. His parents constantly receive precise updates as to when Felix needs a snack or a dose of insulin.

      For Felix, this adds directly to his quality of life. He has Type 1 diabetes, an incurable autoimmune disease in which the body’s insulin-producing cells are destroyed. This causes him to oscillate between two states: hypoglycemia, which makes him restless and unfocused, and hyperglycemia, which makes him tired, weak and listless. To ensure Felix experiences these states as rarely as possible, his blood sugar level must be kept stable, providing the body with the right dose of insulin at the right time.

      Calculating that is the job of an algorithm. And it is pretty good at it: The phases in which Felix is dangerously hyperglycemic have been reduced by almost half. This means not only that Felix is exhausted only half as often as he otherwise would be, but he also has a much lower risk of falling into a diabetic coma. In the six years since his diagnosis, his parents have never had to take him to the emergency room, not once has he lost consciousness because of high blood sugar levels or been in a life-threatening situation.

      Felix owes this personalized medical care not to a doctor, but to his mother. Vivienne Ming loves data. The neuroscientist, who conducted her research at the University of California, Berkeley, is driven by the idea of filtering what is unique out of the mass, recognizing the exceptional and not what is merely average. As lead scientist at the online recruiter Gild, she designed algorithms in Silicon Valley that searched for unrecognized skills in the resumes and digital footprints of 70 million people. With her computer programs, Ming has found jobs for those who are otherwise overlooked by companies because they do not meet the usual criteria.

      Standard therapy was also out of the question for her son. The doctors had suggested exactly that when the diagnosis of diabetes was made. Felix’ parents were asked to measure his blood sugar three times a day for one week and fill in a paper form. This would determine Felix’s average insulin dose. Ming would not accept that; it seemed like a betrayal of what science could do.

      She began to monitor her son. She read the specialist literature, kept meticulous records of when Felix played and when he was lethargic, and noted his meals and their nutritional value on a daily basis. Within four weeks she programmed an algorithm that could derive therapy-relevant patterns and forecasts from her observations and from the collected data on her son’s pulse, blood sugar and physical activity.

      One result particularly amazed the parents. After breakfast, Felix’s pulse and blood sugar levels rose – although not always to the same extent. It regularly went up Mondays to Fridays, with an upward outlier every Tuesday. On weekends his numbers remained lower, without Felix having had anything different to eat at breakfast. The explanation: The boy was feeling stressed. He had just started attending a new preschool and was afraid of what awaited him every morning, especially the math lessons on Tuesdays.

      Vivienne Ming took her findings to the doctors. Her son’s insulin requirements, as the analysis clearly showed, fluctuated greatly. They were so dependent on factors such as the day’s lesson plan that it was impossible to give him the same amount of insulin day in and day out, calculated from the average of 21 random measurements.

      The doctors, however, insisted on using the conventional method. They ignored the tables with hundreds of thousands of data points. Instead, they wanted to treat the child, who had been assessed in minute detail, just based on their own crude average data. Ming decided to ignore the physicians. She rejected the recommended standard procedure in favor of her own expertise, saying: “We have enough data and algorithms that we don’t have to deal with the average.”³

      Felix got an insulin pump that is connected to the Internet and that his mother can use remotely to inject the right dose before his blood sugar spikes or crashes. Tuesday mornings before the math lesson the dose is a bit higher, on weekends a bit lower. Today, Felix is a bright, cheerful child who does not have to do without anything except excess amounts of sweets. However, even if he gets ahold of a bar of chocolate, overriding the programmed amount of insulin, the algorithm quickly notices and alerts his parents, who can make an adjustment if necessary.

      After Vivienne Ming was able to demonstrate how well her son is doing with his personalized therapy and the quality of life he gained from the algorithm she developed, she began to share her findings with academics and pharmaceutical companies. In fact, pharma giant Eli Lilly has now announced a fully automated pump that records and evaluates health data so it can inject the correct amount of insulin. Ming is pleased to have started the ball rolling, but at the same time she is disappointed at the pace at which medical science has embraced algorithmic innovations. “It is amazing how slow progress is,” she says. “It took only one month to personalize Felix’s diabetes therapy. But it has taken ten years to make that kind of treatment available more broadly. I don’t want to substitute doctors. I just want to make them smarter.”⁴

       Math does not have to horrify

      Homogeneity is an illusion – in the treatment of diabetes and in learning. That is something Elke Stuthmann knows well.⁵ She also knows that the subject she teaches has divided generations of pupils. Some love