Cognitext Insight: NLP-Enhanced AI for Detecting Emerging Mental Health Risks in Routine Clinical Notes

Mental health conditions often develop gradually and can go unnoticed until they
become severe. While many patients are already within the healthcare system, subtle signs of worsening mental health may be hidden in the text notes written by clinicians during routine care. These notes contain valuable insights, but they are rarely analysed systematically due to time constraints. This project aims to explore how artificial intelligence (AI), specifically Natural Language Processing (NLP), can help healthcare professionals identify early signs of mental health deterioration by analysing clinical notes and patient records. It is important to clarify that this system is not designed to detect mental health issues in individuals who are outside the treatment pathway or prevent them from seeking care. Instead, it is intended to support clinicians by providing helpful insights that inform decision-making, leading to earlier recognition of key indicators and enabling timely support for patients who may be at risk of worsening conditions. We are proposing a proof-of-concept research project that seeks to engage with practitioners from Hampshire and Isle of Wight NHS Foundation Trust, which provides mental health and learning disability services across the region. Their input will be sought through planned workshops to gather practitioner perspectives, validate the system’s design, and ensure it aligns with real-world clinical needs. The goal is to build a system that can read and understand text notes (unstructured data) written by clinicians—such as discharge summaries from emergency departments or progress notes from routine patient visits—and highlight potential mental health concerns that may need further attention. The project will use a publicly available dataset called MIMIC-IV, which contains anonymised hospital records, including both structured data (like age, diagnosis, medications) and unstructured data (like written notes from doctors). We will focus on patients who were admitted to the emergency department and later diagnosed with mental health conditions. By training our model on these records, we aim to teach it how to recognise patterns and risk factors that may indicate a developing mental health issue.