Which is caused by?
Several drug manufacturers flooded the market with pills exposing way too many people to opioids in a casual way. Once folks are addicted it’s very very difficult to break the addiction.
The Washington post has an excellent series on the topic if you are interested.
I have lost 30 lbs over 3 months. Chronic pain sucks but I despise opioids. They are seductive at first until one realizes their body is literally rotting away..
I was lifting 125lb dumbells before I got on morphine. Now I am feeble and haven't worked out in months. It kills your endogenous drive. If you care about your future self, please don't use opiates more than sparingly...
Which makes me wonder...there's common regimens like the Ashton scale and whatnot for benzos but it's very boilerplate.
Unicorn Idea: Quantitative Withdrawal
User enters dosage each time they use. Datetime is auto filled, but can be altered for dosages that are entered belatedly.
ML is used to show charts with sliders based on speed of taper and severity of side effects. A time series showing the reduction in withdrawal effects over time with an ETA and other statistics. With labeled sections for certain parts of the withdrawal that are more severe (think a phase change diagram.) Seizure/epilespy zone would be clearly large on a configuration where the user chooses a ridiculously fast taper. The app would show a color, red in this case, warning of these symptoms and recommending against it. Baseline taper recommendations could be based on the medical literature out there with clinical trials. There is plenty of labeled data especially from the NIH.
The user can log their current symptoms to help the model learn their individual brain chemistry.
And vitals like HR, pulse, and o2 that are easily measured via pleasant APIs like Healthkit on iOS and Android. (Would be by proxy optionally compatible with iWatch, FitBit and other such sensors.)
These vitals are great features that the model can learn from.
The user can answer questions regarding the current state of their withdrawal symptoms, providing the model with labeled data to learn from.
Models can be pretrained on an individual in close proximity to the MLE on the distribution of human neurochemistry. And thus would work out of the box pretty well before the users input and vitals start to vastly improve it until it helps the user maintain AND gain :-)
just ideas..