We describe the Semantic Analysis of Text to Inform Clinical Action (SemAnTICA) Lab’s system for the MediQA-SYNUR 2026 shared task on extracting structured clinical observations from nurse dictation transcripts. The task requires mapping observations from disfluent conversational text to a large, fixed ontology and producing strictly normalized outputs, where small amounts of concept over-selection severely degrade micro-F1 score. Our approach evolved from a full-schema in-context baseline to a pipeline that explicitly separates concept selection from value extraction. We first preprocess transcripts, then generate transcript-specific concept candidates using hybrid sparse–dense retrieval. The candidates are then pruned with an evidence-based filter. For extraction, we adopt a system-level mixture-of-experts design with an online LLM router that selects a subset of domain-specialized experts per transcript. Each expert operates over a constrained schema partition to reduce spurious predictions. We enhance robustness with agreement-gated ensembling and targeted adjudication for ambiguous cases. Finally, we intersect complementary high-recall and high-precision runs to produce the best submission. Our system ranked first on the official test leaderboard with F1 = 0.814, P = 0.826, R = 0.801.

Social connection and social isolation are associated with depressive symptoms, particularly in adolescents and young adults, but how these concepts are documented in clinical notes is unknown. This pilot study aimed to identify the topics relevant to social connection and isolation by analyzing 145 clinical notes from patients with depression diagnosis. We found that providers, including physicians, nurses, social workers, and psychologists, document descriptions of both social connection and social isolation.

In this paper, we present pilot work on characterising the documentation of electronic cigarettes (e-cigarettes) in the United States Veterans Administration Electronic Health Record. The Veterans Health Administration is the largest health care system in the United States with 1,233 health care facilities nationwide, serving 8.9 million veterans per year. We identified a random sample of 2000 Veterans Administration patients, coded as current tobacco users, from 2008 to 2014. Using simple keyword matching techniques combined with qualitative analysis, we investigated the prevalence and distribution of e-cigarette terms in these clinical notes, discovering that for current smokers, 11.9% of patient records contain an e-cigarette related term.

Major depressive disorder, a debilitating and burdensome disease experienced by individuals worldwide, can be defined by several depressive symptoms (e.g., anhedonia (inability to feel pleasure), depressed mood, difficulty concentrating, etc.). Individuals often discuss their experiences with depression symptoms on public social media platforms like Twitter, providing a potentially useful data source for monitoring population-level mental health risk factors. In a step towards developing an automated method to estimate the prevalence of symptoms associated with major depressive disorder over time in the United States using Twitter, we developed classifiers for discerning whether a Twitter tweet represents no evidence of depression or evidence of depression. If there was evidence of depression, we then classified whether the tweet contained a depressive symptom and if so, which of three subtypes: depressed mood, disturbed sleep, or fatigue or loss of energy. We observed that the most accurate classifiers could predict classes with high-to-moderate F1-score performances for no evidence of depression (85), evidence of depression (52), and depressive symptoms (49). We report moderate F1-scores for depressive symptoms ranging from 75 (fatigue or loss of energy) to 43 (disturbed sleep) to 35 (depressed mood). Our work demonstrates baseline approaches for automatically encoding Twitter data with granular depressive symptoms associated with major depressive disorder.