Big Data and Healthcare (Infographic)

Health_BigData_Infographic

From Steve Lohr’s @SteveLohr profile of Jeff Hammerbacher  in the New York Times

On the Case at Mount Sinai, It’s Dr. Data

Jeffrey Hammerbacher is a number cruncher — a Harvard math major who went from a job as a Wall Street quant to a key role at Facebook to a founder of a successful data start-up.

But five years ago, he was given a diagnosis of bipolar disorder, a crisis that fueled in him a fierce curiosity in medicine — about how the body and brain work and why they sometimes fail. The more he read and talked to experts, the more he became convinced that medicine needed people like him: skilled practitioners of data science who could guide scientific discovery and decision-making.

Now Mr. Hammerbacher, 32, is on the faculty of the Icahn School of Medicine at Mount Sinai, despite the fact that he has no academic training in medicine or biology. He is there because the school has begun an ambitious, well-funded initiative to apply data science to medicine.  …

Jeffrey Hammerbacher now leads a team that uses quantitative skills to improve medical treatments. His move from the start-up world was inspired by his own health crisis.

“We’re pursuing problems that are computationally and intellectually exciting, and where there is the potential to change how doctors treat patients in two or three years,” Mr. Hammerbacher said.

Eric Schadt, the computational biologist who recruited Mr. Hammerbacher to Mount Sinai, says the goal is to transform medicine into an information science, where data and computing are marshaled to deliver breakthroughs in the treatment of cancer, Alzheimer’s, diabetes and other chronic diseases. Mount Sinai is only one of several major medical schools turning to data science as a big part of the future of medicine and health care.

They are reaching out to people like Mr. Hammerbacher, whose career arc traces the evolution of data science as it has spread across the economy. After a job designing trading models at Bear Stearns, he worked for a few important years at Facebook, where he started the social network’s data team and made his reputation and a tidy sum. Next, he was one of four founders of Cloudera, a fast-growing company that makes software tools for data science. And now he is immersed in medicine.  …

Dr. Schadt had concluded that medicine was ripe for a data-driven revolution. Chronic diseases, Dr. Schadt explained, are not caused by single genes, but are “complex networked disorders” involving genetics, but also patient characteristics such as weight, age, gender, vital signs, tobacco use, toxic exposure and exercise routines — all of which can be captured as data and modeled.

“We are trying to move medicine in the direction of climatology and physics; disciplines that are far more advanced and mature quantitatively,” he said.

That message resonated with Mr. Hammerbacher. By 2013 he was spending most of his time in New York rather than on the West Coast, assembling a research team that now numbers 10 people. Their expertise spans the breadth of data science: machine learning, data visualization, statistics and programming.

His group’s objective is to alter how doctors treat patients someday. For example, Mount Sinai medical researchers have done promising work on personalized cancer treatments. It involves the genetic sequencing of a patient’s healthy cells and cancer tumor. Once the misbehaving gene cluster is identified and analyzed, it is targeted with tailored therapies, drugs or vaccines that stimulate the body’s defenses.

Mr. Hammerbacher’s team does not do the basic science. Other researchers do that. His group works on the “computational pipeline,” he said, with the goal of making personalized cancer treatments more automated and thus more affordable and practical. “It’s ultimately what cancer cures are going to look like,” he said.

The road to technology revolutions is paved with failure, halting progress and hard work. Mr. Hammerbacher and his colleagues are engaged in pathbreaking yet often frustrating data science work.

He is optimistic about his initiative’s prospects, but has come to appreciate that the mysteries of the human body may be more resistant to math than finance or social networks are. Today he speaks less about quants taking over than about their lending a hand. “We’re not the most important people,” he said, “but we can help.”

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I launched the Big Data conversation; writing, research, marketing services; https://whatsthebigdata.com/ & http://infostory.com/
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