The Flow has not summarised this story yet — read it at arXiv Machine Learning.
LongCounsel-8 is a new benchmark suite comprising three datasets with 7,749 five‑session counseling dialogues, each grounded in real client profiles, depression trajectories, symptom compositions, and counseling patterns. The benchmark addresses challenges of longitudinal consistency, empirical grounding of symptom progression, and natural expression of controlled depression states without exposing labels. Experiments show that lower single‑session error does not ensure accurate trend detection, methods perform worse on worsening trajectories, and adding more session history can reduce trend prediction accuracy.
LongCounsel-8 is a benchmark suite comprising three independently generated datasets with 7,749 five‑session counseling trajectories, designed to evaluate longitudinal depression tracking. The datasets are grounded in real‑world client profiles, depression trajectories, symptom compositions, and counseling patterns, and they address challenges of longitudinal consistency, empirical grounding, and natural expression of controlled depression states. Experiments show that lower single‑session error does not guarantee accurate trend identification, that methods perform worse on worsening trajectories, and that adding more session history can reduce trend prediction accuracy.
University students experience disproportionately high rates of common mental health conditions, such as depression, which can impair learning, social functioning, and overall well-being. Although lif...
The paper introduces SMOTE‑VAR, an uncertainty‑aware oversampling technique that uses a Gaussian process variance function to filter out invalid synthetic minority samples. Applied to a university student depression dataset, SMOTE‑VAR outperforms existing oversampling methods in predicting remission outcomes. By more accurately identifying students unlikely to remit, the method supports earlier, personalized interventions.
The paper introduces MC-TRCM, a Modality‑Conditioned Temporal Recursive Context Model that treats each feature source from mobile and wearable mental‑health datasets as a separate token and explicitly models missingness. By encoding observed values, summarizing missingness, and using learned absence tokens, the model refines predictions recursively and is conditioned on dataset and task embeddings. Evaluated on six endpoints from DepreST‑CAT and PSYCHE‑D, MC‑TRCM outperforms tabular baselines on PHQ‑9 and GAD‑7 severity prediction and matches or slightly exceeds reference performance on classification tasks, with ablations confirming the importance of feature‑wise modulation, absence tokens, missingness projections, and recursive refinement.
arXiv:2607. 27224v1 Announce Type: cross Abstract: External and synthetic control arms (ECAs) are entering psychiatric drug development, but the field lacks a benchmark that evaluates the properties regulators care about: not only how accurately a method reconstructs untreated trajectories, but whether its uncertainty is calibrated, whether it is robust to the informative observation times common in mental-health records (sicker patients are seen more often), and what false-positive rate it induces in go/no-go trial decisions.