Name: Using ML to predict insulin use for Type 1 Diabetes (TensorFlow Meets)
Uploaded: 2020-04-13T17:07:52.000Z
Duration: 12 min 48 s
Description: Thousands of YouTube videos with English-Chinese subtitles! Now you can learn to understand native speakers, expand your vocabulary, and improve your pronunciation...

Future continuous

I'm Pete Warden on today we're gonna be talking to Marius Erickson.

So I understand you're using Tensorflow to help your daughter who's a type one diabetic.

Maybe you could tell us a bit more about that.

So it helps have a little bit of background and what's treating Type one diabetes entails.

So when you're a type one diabetic, your packers, this basically nonfunctioning and not secreting insulin into your bloodstream, which is required for yourselves to take up glucose.

Because of this phenomenon, we basically be able to predict what glucose levels should be on based on that prediction.

Deliver sort of a correct amount of insulin or withhold insulin if we predict that blood glucose is going to go low, right?

Ah, and so because of those this reasons, we the approach that I've taken eyes to employ machine learning actually in two ways, so one.

It's a model that uses data that's gathered on a continual basis every five minutes.

Ah, and predicts future blood glucose based on you know that passed and then secondly Ah, uh, a different model or rather, model that builds on the first model tries to figure out what?

It's an optimal insulin delivery schedule that will effectively, ah, you know, keep blood sugar within the sort of Goldilocks zone.

And so that second model knows about these are far mako dynamics of insulin.

Umm and ah, actually use tensorflow for both of those, right?

One of the really nice things about tensorflow is that it's very easy to composed these models together.

Looks like you actually have pumped dead.

So this is Ah, the pump or ah, pump Similar to what?

Um, and this is just something you keep in your pocket or whatever.

Um, and this is a little too being that comes out of others.

Ah, the tubing connects to effectively a small needle that that's inserted into some fat, hairy everybody.

This pump in particular can be controlled remotely.

Ah, And so the first thing that had to do for this project was not machine learning related eyes to effectively reverse it.

Reverse engineer that radio protocol that I can control it from a raspberry pi, which is the second component.

And so this is Ah raspberry pi zero, as we can see And this is sort of where all the magic happens on dso the raspberry pi zero through a custom I some radio with custom firmer that knows how to communicate Thio Instant pump can effectively instructions and pump what to do.

Deliver this much insulin or withholding son or whatever the raspberry piles uses.

Billy Thio communicate with the phone and that serves two purposes.

One is to receive, uh, blood glucose values from this continuous glucose monitoring system that integrates with the phone.

Ah, and the second is to receive, um, instructions from the phone as to, for example, when my daughter eats.

And so whenever she eats, we have to enter all you're eating this many grams of carbs s o.

The model can take that into account, right?

And without that, it wouldn't be able to predict the future very well.

So, um, what does your daughter think of this?

So there's There's two big ways in which makes the difference.

One is, um, it just reduces the liver intensity of of diabetes.

You know, you still have to intervene on occasion.

There's still a lot of things to do, but you can't be a computer in terms of, you know, being able to do the same thing every five minutes over and over and over again, right?

And so literally every five minutes, the system just insulin delivery based on the predicted what it predicts the future.

The second ah, big thing is that, um typically the way diabetes is treated.

Uh, this was something called Basil and bullets therapy.

And what that means is that it's ah, it's a, you know, still fairly complex but far simplified version of what the model does.

Ah, and you have to sort of even with this therapy, you have to every time you eat, you know, check what your blood would goes This, uh, computes using a few ratios how much instant, deliver on, do it and then check your blood sugar again a little bit later.

But again, with using machine learning techniques and other things, we can do far, far better.

We can understand much more deeply the the evolution of insulin in the blood.

We can predict the evolution of carbohydrate of parents in the blood and so on and so forth.

And the way this model works is that it has obviously trained with a lot of historical data.

And so individual variants, right, is also something that the model can pick up very readily.

So, as an example, it's known that individuals respond slightly differently to insulin.

Maybe the pharma code dynamics evolve inside of different ways.

Maybe they Maybe it takes longer for the incident to clear.

With this model, it actually learns what the farmer could.

How did you actually go about getting the training data that you needed to train and test these mortals?

So after I reverse engineer the r F protocol of the pump Ah, I started Basically first it's observing.

And so every five minutes at effectively quarries to pump and ask how much will be delivered in the last five minutes, right?

So we're still administering insulin through the remote control.

But we were controlling it right and then all at the same time, entering every time my daughter had a meal, we would enter our estimate of the number of carps in that meal and our estimate of the dressing make index.

So those things together would capturing the glucose readings from the continuous glucose monitors effectively gives us all the raw data, right?

So we can we can observe blood glucose levels, observed the incident that's been delivered, uh, and all the food that she, uh, vitiate.

And so that's that's basically how we gather the raw data we do that for, you know, a few months before he started, you know, with the exercise of modeling.

And at this point, my daughter's values are almost normal guy scenic meaning that, uh, from that point of view, it's almost as if she doesn't have diabetes.

And so that's another huge benefit of this kind of automation.

You know, if you're willing to, you know, look at your blood sugar every five minutes and do much asking a five year old girl Thio to deal with that when she's more interested in correct running around and playing is doesn't work.

So, um, maybe you could tell me a bit about how you found, uh, in a decided on tensorflow.

And how you use that sort of on speed pie.

So the reason I decided on on TENSORFLOW is initially because it was very easy to sort of switch from experimentation to production, right?

And so I would do all that kind of modeling work and experimentation and your Jupiter notebooks and all that stuff is very, very convenient.

And the controller code eyes actually written and go right.

So all the stuff that deals with the radio transmission and the time serious databases in the last stuff Silverton and Go.

And so with tensorflow, I was able to take the model that a developing distributor notebook save it to protocol buffers basically and then, you know, loaded up and go, and that provides a really nice and clean interface.

Always never have to change the go code that loads the model.

It knows how the feet of the right date on everything.

But I can change completely the behavior of the model just, you know, sort of purely in this high level way, using pipeline, right that was one really big appealing.

The other thing obvious is that it can run in embedded systems, right?

And so, you know, the raspberry pies and arm based system, and I can run the inference and the retraining, uh, you know, without having to worry about the underlying architecture, anything like that.

Ah ah, The third thing I think iss I'm very much using this welcome possibility of the tensorflow compete that computation graphs so assume example.

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Using ML to predict insulin use for Type 1 Diabetes (TensorFlow Meets)

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