[Maeumjigi Capstone Dev Log #24] Fall Semester Kickoff: Longitudinal Clinical Preprocessing & Informative Missingness (MNAR) Modeling
Welcome to the 24th installment of the Maeumjigi Capstone Project Dev Log.
Following our successful Semester 1 final defense and dual-layer safety wrapper deployment in June, our team officially kicked off Semester 2 (Fall 2026). In the first semester, our primary engineering focus centered on large language model (LLM) fine-tuning (Llama 3.1 8B, Qwen 2.5 32B/7B), synthetic dialogue generation, and prompt safety guardrails.
However, in clinical psychology and psychiatric triage, language is only half of the diagnostic puzzle. Patient risk escalates across time through longitudinal numerical markers: self-reported depression severity (PHQ-9), generalized anxiety (GAD-7), perceived stress, and insomnia scores.
Today's dev log documents our architectural breakthrough in clinical data engineering: moving away from naive imputation methods and engineering a robust 14-Day Last Observation Carried Forward (LOCF) pipeline equipped with Informative Missingness (Missing Not at Random / MNAR) Indicators.