yeah I know why I was such in a bad mood this afternoon...I had dealt with insurance issues and I didn't eat lunch and slept for only like 4 hours last night...
On top of that, I get mail late, so mail comes around at like 5:30 and I got mail from HR regarding my health insurance over an issue that me and my family had contacted both HR and the health insurance company over multiple times, and what do you know? HR and health insurance both screwed up.
Like it's regarding termination of benefits which shouldn't have happened, at the end of the month...and we were trying to prevent it from happening for a couple of years now for me.
Like I seriously apologized to my mom for getting so upset...happy mother's day!
all you have to do is print out a letter size (full page) print out of one of the euglycemic glucose clamp studies from the Germans in particular that are by Heinemann, Rave, Becker (look specifically for those authors)
and just interpolate the x and y axes and fit the dat
This message was deleted
This message was deleted
but it's gold
This message was deleted
much simpler than mathematical modeling
just print out a full size of one of those glucose infusion rate graphs from a euglycemic clamp study from Heinemann, Becker, Rave for like Lispro, Aspart, or Glulisine
same amount of work as getting the data for the Medtronic Animas and Tandem t:slim pumps
just less boring :P
but that's how you derive IOB properly
instructions are in that article
it's the only article that goes over it properly. In English at least
like it's the only English source...the rest of it is in German...from the paper... Waldhäusl  (German Only!) suggested the onset of action be defined as the point of time at which—after subtraction of the basal rate—5% of the total AUC has been reached and the end of action as the point of time at which 95% has been reached.
This is what they cited In: Waldhäusel W, Gries FA, eds. Diabetes in der Praxis. Berlin: Springer-Verlag, 1993: 150–172.
No English pub...that article is the best we got for properly deriving IOB from Euglycemic clamp studies
but the DIA decreases with the age of an infusion site and so many other factors, including exercise. You can be as precise as you want, but it is impossible to control so many factors. The best you can do is design the system to be safe and robust to perturbations and estimate DIA as one of the random variables
@mgranberry is right: we don't need a precise method to estimate IOB curves for the whole population. We need an easy method to calculate a reasonably accurate curve with limited data for a single individual over a short timeframe.
right, but the IOB function will be variable. I was high earlier, so I injected a couple units into my calf and went for a jog. An hour later I was in the normal range and didn't drop any more because that's what happens when you inject into muscle. There will be significant differences in how things behave from person to person.
I know that clamp studies are flawed, but there is danger in assuming that there is some perfect model to fit things to. It's better to have a simple well-behaved function to work with for optimization problems, which is what control reduces to
but the food absorption will still be unpredictable. You can nail down insulin absorption as closely as you want (which I maintain is still highly variable), but gastroparesis will severely limit how much insulin prediction will help because you need to have an essentially reactive system to prevent severe hypos in that case
A stochastic differential equation (SDE) is a differential equation in which one or more of the terms is a stochastic process, resulting in a solution which is itself a stochastic process. SDEs are used to model diverse phenomena such as fluctuating stock prices or physical systems subject to thermal fluctuations. Typically, SDEs incorporate random white noise which can be thought of as the derivative of Brownian motion (or the Wiener process); however, it should be mentioned that other types of random fluctuations are possible, such as jump processes.
nonlinear model predictive controller = life is good
yeah man, that's what the controller is for...I mean, I have severe gastroparesis...sometimes I empty normally, sometimes I regurgitate food hours old and you can smell it like something is rotting...I hope you get the idea
a robust controller should be able to handle some incorrect model parameters, because there will be errors from all sides coming in. Don't design it to be too reliant on things actually working. Nothing actually works in the real world
@bewest FYI I wasn't arguing...at least I didn't think so. I just felt like this conversation was super awkward. Like I have ~30 articles on euglycemic clamps and that paper had the most solid information for the work I did before by hand
no look I will admit it straight up because it will make all of the dudes uncomfortable and yes I am a b**** tonight....I'm not gonna lie. I'm PMSing and I am not usually this way. TMI but there you go guys.