Clinical notes reveal hidden mental health risks in physical patients
Beyond Physical Illness: Can Clinical Narratives Reveal Emerging Mental Health Risks in Chronically Ill Patients?
Artificial IntelligenceMachine Learning
Summary
Mental health problems like depression or suicidal thoughts can develop in patients hospitalized for physical illnesses, but often go unnoticed. This study looks at whether daily hospital notes can help spot these mental health risks early. The researchers used a smart computer model to track changes in patient notes over time and found it could help predict who might develop these problems during their hospital stay. Their approach could lead to better monitoring and care for patients with chronic physical illnesses.
What this means in practice
- •For hospital data teams: Integrate daily clinical notes analysis to identify mental health risks in physically ill patients during hospital stays.
- •For electronic health record developers: Embed temporal models of narrative data to flag emerging mental health issues in chronic patients for clinicians.
Tested on one dataset.
Authors
Sudeshna Jana, Siddhartha Laha, Manjira Sinha, Tirthankar Dasgupta, Pabitra Mitra
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.