What Really Happens When You Mix Medications: Russ Altman (Transcript)

Full text of Stanford big data techno-optimist Russ Altman’s talk titled “What Really Happens When You Mix Medications?” at TED conference. In this fascinating talk, Russ Altman shows how doctors are studying unexpected drug interactions using a surprising resource: search engine queries.

Listen to the MP3 Audio here:


Russ Altman – Professor of Bioengineering, Genetics, Medicine and Computer Science at Stanford University

So you go to the doctor and get some tests. The doctor determines that you have high cholesterol and you would benefit from medication to treat it. So you get a pillbox.

You have some confidence, your physician has some confidence that this is going to work. The company that invented it did a lot of studies, submitted it to the FDA. They studied it very carefully, skeptically, they approved it. They have a rough idea of how it works, they have a rough idea of what the side effects are. It should be okay.

You have a little more of a conversation with your physician, and the physician is a little worried because you’ve been blue, you haven’t felt like yourself, you haven’t been able to enjoy things in life quite as much as you usually do.

Your physician says, “You know, I think you have some depression. I’m going to have to give you another pill.”

So now we’re talking about two medications. This pill also — millions of people have taken it, the company did studies, the FDA looked at it — all good. Think things should go okay.

Think things should go okay.

Well, wait a minute.

How much have we studied these two together?

Well, it’s very hard to do that. In fact, it’s not traditionally done. We totally depend on what we call post-marketing surveillance, after the drugs hit the market.

How can we figure out if bad things are happening between two medications? Three? Five? Seven? Ask your favorite person who has several diagnoses how many medications they’re on.

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So why do I care about this problem?

I care about it deeply. I’m an informatics and data science guy and really, in my opinion, the only hope — only hope — to understand these interactions is to leverage lots of different sources of data in order to figure out when drugs can be used together safely and when it’s not so safe.

So let me tell you a data science story. And it begins with my student Nick. Let’s call him Nick, because that’s his name.

Nick was a young student. And I said, “You know, Nick, we have to understand how drugs work and how they work together and how they work separately, and we don’t have a great understanding.

But the FDA has made available an amazing database. It’s a database of adverse events. They literally put on the web — publicly available, you could all download it right now — hundreds of thousands of adverse event reports from patients, doctors, companies, pharmacists.

And these reports are pretty simple: it has all the diseases that the patient has, all the drugs that they’re on, and all the adverse events, or side effects, that they experience. It is not all of the adverse events that are occurring in America today, but it’s hundreds and hundreds of thousands of drugs.

So I said to Nick, “Let’s think about glucose. Glucose is very important, and we know it’s involved with diabetes. Let’s see if we can understand glucose response.”

I sent Nick off. Nick came back. “Russ,” he said, “I’ve created a classifier that can look at the side effects of a drug based on looking at this database, and can tell you whether that drug is likely to change glucose or not.” He did it. It was very simple, in a way. He took all the drugs that were known to change glucose and he took a bunch of drugs that don’t change glucose, and he said, “What’s the difference in their side effects? Differences in fatigue? In appetite? In urination habits?”

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