The risk to benefits ratio of introducing a language model to interpret so clear signals is nowhere near justified.
Monitoring and analytics is important, but it is a solved problem. A language model will only be able to hallucinate about the relationship between meals and glycemic response. At best it does no harm, at worst it can directly misinform.
My experience is completely the opposite, of using LLMs to pattern match and cast diagnostic nets.
Is your perspective based on, say, opinionated principle?, or experience?
The benefits are enormous.
The risks; What risks? No diabetic with baseline adult competence is going to drive their insulin-delivery vehicle off a cliff because some app said so.
Yep. The oref1 algorithm is amazing and proven to make diabetic's quality of life better, AND SAFE. I don't understand why would you need to add AI to that mix.
But I will check this algo out. Maybe it has some interesting bits.
Went through pregnancy with the mother having recently-diagnosed T1 diabetes – just barely not killed by grave neglect on behalf of healthcare due to how badly they missed the diagnosis to begin with.
I think the only thing that could be made better is tuning the I:C/ISF/Basal values automatically. And ISF is already handled by DynamicISF, while not perfect it reduces the variables you have to tweak.
Otherwise, when tuned correctly, oref1 et.al. provide amazing results and are safe. Hard to understand where I would use LLMs in this.
I'm a T1D and tbh it's not that hard to manage, I just wouldn't need that. But for kids or the elderly, I see a use case.
The hardest to learn was that an unhealthy lifestyle resulted in a diabetes that was harder to manage. Too much carbs, not enough exercise, etc. After adjusting my lifestyle, it became quite easy.
The most pain, in my experience, comes from the discrepancy between the CGM - measured value and the prick-test value, even when accounting for time lag. I've used several CGMs and they've all been wildly off sometimes. I have a few T1D acquaintances who relied on their CGM alone and have significantly improved their HbA1c after accounting for that.
The risk to benefits ratio of introducing a language model to interpret so clear signals is nowhere near justified.
Monitoring and analytics is important, but it is a solved problem. A language model will only be able to hallucinate about the relationship between meals and glycemic response. At best it does no harm, at worst it can directly misinform.
My experience is completely the opposite, of using LLMs to pattern match and cast diagnostic nets.
Is your perspective based on, say, opinionated principle?, or experience?
The benefits are enormous.
The risks; What risks? No diabetic with baseline adult competence is going to drive their insulin-delivery vehicle off a cliff because some app said so.
Yep. The oref1 algorithm is amazing and proven to make diabetic's quality of life better, AND SAFE. I don't understand why would you need to add AI to that mix.
But I will check this algo out. Maybe it has some interesting bits.
Went through pregnancy with the mother having recently-diagnosed T1 diabetes – just barely not killed by grave neglect on behalf of healthcare due to how badly they missed the diagnosis to begin with.
On your work:
this is legit
it is appreciated
Hats off, I salute this, thank you
I'm a T1D who has an insulin pump looping with AndroidAPS and NightScout, what does this give you that Nightscout and Autotune doesn't give you?
And how do you deal with AI hallucinations?
I think the only thing that could be made better is tuning the I:C/ISF/Basal values automatically. And ISF is already handled by DynamicISF, while not perfect it reduces the variables you have to tweak.
Otherwise, when tuned correctly, oref1 et.al. provide amazing results and are safe. Hard to understand where I would use LLMs in this.
I'm just happy to see a GPL project.
I'm a T1D and tbh it's not that hard to manage, I just wouldn't need that. But for kids or the elderly, I see a use case.
The hardest to learn was that an unhealthy lifestyle resulted in a diabetes that was harder to manage. Too much carbs, not enough exercise, etc. After adjusting my lifestyle, it became quite easy.
The most pain, in my experience, comes from the discrepancy between the CGM - measured value and the prick-test value, even when accounting for time lag. I've used several CGMs and they've all been wildly off sometimes. I have a few T1D acquaintances who relied on their CGM alone and have significantly improved their HbA1c after accounting for that.
Maybe that information is useful to you.
What’s the limit on badges in a README
"This will all end in tears, I just know it"
Marvin
FDA approved?
Life imitates comedy...
The alerts system and sharing with caregivers is a solved problem already (e.g. Dexcom's Follow, Abbot's LibreLinkUp).
Do you find the analytics actually helps? I.e. a lot of this will depend on what you ate and whether or not you logged it?
This is THE ONE domain where you would want to use classical machine learning and not unreliable LLMs. Unless you want to kill yourself, that is.
Yes, language has nothing to do with it and is complete overkill.
Probably something like SVM for warnings.
Unless the whole purpose is just daily reports.