Research
Academic Context
Flarelines is developed as a research artifact in the master's studies in Speculative Software Design at the University of Applied Sciences Potsdam. The research explores AI-assisted health journaling for chronic illness patients with a focus on privacy.
The Problem
When people with chronic health conditions like systemic lupus erythematosus (SLE) attend routine medical visits, the practitioner receives only a snapshot of the patient's current state. Symptom flares that occurred in the past are difficult to recall accurately.
Free-text symptom diaries help, but they're often too verbose for clinicians to interpret within limited appointment times. A more structured representation is needed.
The Approach
We use small language models (SLMs) that run entirely on-device to transform free-text journal entries into structured scores:
- 0 = Remission (no symptoms)
- 1 = Mild activity
- 2 = Moderate activity
- 3 = Severe activity
This shifts clinical NLP away from remote API services, which introduce privacy risks. On-device processing keeps health data private while producing clinically useful output.
The Model
We fine-tuned Qwen2.5-3B on lupus diary data using a hybrid approach: autobiographical journal entries from a lupus patient (one of the researchers) combined with synthetic data generated by GPT-5.1.
The model runs locally on iPhone (~1.8 GB). No internet connection required for scoring.
Data Contribution
Users can optionally contribute anonymized journal entries (text + scores only) to improve the model. No identifiers are collected. This is disabled by default and can be enabled in Settings.
Contact
Research inquiries: flarelines@inpyjamas.dev