analiza aiqbench
This is a preprint that applies a hierarchical state-space model to day-by-day clinical notes from about 4,000 MIMIC-III patients to estimate mental-health risk during hospitalization. The authors report better binary risk prediction and different risk trajectories for the two cohorts, but these are preliminary results on a weakly supervised label derived from discharge summaries. For ML readers, the main points to check are how that label was built and whether the gains hold up against strong baselines and external data.
Beyond Physical Illness: Can Clinical Narratives Reveal Emerging Mental Health Risks in Chronically Ill Patients?
arXiv:2610.35761v1 Announce Type: cross Abstract: Mental-health risks such as depression, dementia, and suicidal ideation can emerge during hospitalization among chronically ill patients and may worsen without timely recognition and intervention. However, most existing mental health studies focus on patients with established psychiatric conditions rather than patients hospitalized primarily for physical illness. In this work, we investigate whether latent mental-health risk in chronically ill patients can be inferred from longitudinal clinical narratives. We construct a weakly supervised cohort around 4,000 patients from MIMIC-III using mental-health evidence extracted from discharge summaries. A hierarchical state-space framework derives mental-health representations from day-wise clinical narratives and models their temporal evolution for risk estimation throughout hospitalization. Preliminary results demonstrate improved binary risk prediction using the proposed representations and show distinct model derived risk trajectories between risk and no-risk cohorts, providing a foundation for studying emerging mental-health risks in chronically ill patients.
Title:Beyond Physical Illness: Can Clinical Narratives Reveal Emerging Mental Health Risks in Chronically Ill Patients?
Abstract:Mental-health risks such as depression, dementia, and suicidal ideation can emerge during hospitalization among chronically ill patients and may worsen without timely recognition and intervention. However, most existing mental health studies focus on patients with established psychiatric conditions rather than patients hospitalized primarily for physical illness. In this work, we investigate whether latent mental-health risk in chronically ill patients can be inferred from longitudinal clinical narratives. We construct a weakly supervised cohort around 4,000 patients from MIMIC-III using mental-health evidence extracted from discharge summaries. A hierarchical state-space framework derives mental-health representations from day-wise clinical narratives and models their temporal evolution for risk estimation throughout hospitalization. Preliminary results demonstrate improved binary risk prediction using the proposed representations and show distinct model derived risk trajectories between risk and no-risk cohorts, providing a foundation for studying emerging mental-health risks in chronically ill patients.
Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2610.35761 [cs.AI]
(or arXiv:2610.35761v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.35761 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 252 cuvinte
Subiecte conexe
Auditing Retrieval-Augmented LLM Hypotheses for Longitudinal Cell Painting Morphology
arXiv · 23 iul. 2026
Why so many therapy etc. frameworks think they're The One True Approach
LessWrong · 1 aug. 2026
My experiences with the extremes of post-viral illness and brain retraining/mindbody work
LessWrong · 30 oct. 2026
New standards for masculinity are stacked against men’s health
The Verge · 10 sept. 2026