[D] Looking for advice: Modelling a medicine-reminder agent that must decide “remind / wait / notify” under incomplete information[D] | SpinGraph: Research-framing
SpinGraph analysis of Reddit r/MachineLearning's [D] Looking for advice: Modelling a medicine-reminder agent that must decide “remind / wait / notify” under in…
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[D] Looking for advice: Modelling a medicine-reminder agent that must decide “remind / wait / notify” under incomplete information[D]
POMDP, medication adherence, context-aware reminders, reinforcement learning, alert fatigue
User is in early research phase designing an AI agent for medication adherence with uncertain inputs. Asks whether POMDP/belief-state RL is appropriate or overkill for real-world reminder logic. Seeks practical alternatives, pitfalls, and prototyping guidance from experienced practitioners.
Emphasizes methodological choice (POMDP vs. bandits) and theoretical tractability; minimizes clinical risk, human factors, regulatory pathways, and real-world data limitations.
Technical exploration by an independent researcher seeking grounded, practice-informed guidance.
A researcher asks for advice on modeling a medicine-reminder AI agent using POMDPs or simpler methods.
Clinical safety thresholds; Regulatory classification (e.g., FDA/CE marking); Patient consent and data privacy architecture; Integration with EHR or pharmacy systems; Real-world sensor reliability (e.g., wearables, pill sensors)
The post combines academic terminology (POMDP, belief-state RL) with concrete operational verbs ('remind / wait / notify') to create an illusion of methodological readiness. This makes the abstract idea feel more implementable and less speculative than it is, while the forum context and lack of claims prevent scrutiny of clinical validity or safety — the main tension lies between the precision of the framing and the total absence of validation anchors.