Integrating Loneliness and Social Isolation Insights into Late-Life Suicide Risk Prediction Through the Digital Phenotyping of Electronic Health Records

ABSTRACT
The crisis of loneliness and social isolation, particularly among the older adults, alarmingly correlates with an
increased risk of suicide. Although this crisis is particularly prevalent among older patients, current healthcare
systems face challenges in effectively monitoring late-life loneliness, social isolation and associated suicide risks,
partly due to insufficient and underdeveloped automated surveillance practices. According to recent studies,
increased healthcare utilization is observed among this population prior to suicides, underscoring a critical
window for timely intervention and support that remains unexploited, despite available patient data. Our research
seeks to bridge this gap by innovatively interrogating electronic health records (EHRs) with rigorous, interpretable
artificial intelligence (AI) methods, including those involving large language models (LLMs). Our approach is
aimed at significantly enhancing the precision of suicide risk predictions among older adults grappling with
isolation and loneliness. Contemporary AI models employed in healthcare settings often overlook the rich
narrative data embedded in EHRs, which vividly capture the nuances of patients' experiences with loneliness
and social isolation. This is partially due to the fact that until recently natural language processing (NLP) methods
lacked the capabilities to detect the often variable and distributed lexical expressions used to describe complex
clinical concepts. To address this gap, our project aims to develop advanced prediction models that integrate
social factors, specifically loneliness and social isolation, through longitudinal EHR phenotyping. Our project is
in line with the National Institute of Mental Health's (NIMH) emphasis on computational methods to detect
patterns linked with social isolation and suicidal tendencies in older adults. Utilizing the prowess of LLMs, we will
delve into clinical EHR texts to detect indications of loneliness and social isolation in individuals aged 55 and
above, conducting differential analyses across different age brackets to gain a deeper understanding of how
these factors influence suicide risk in later life. The project comprises three main aims: 1) To develop and validate
a novel tool that identifies linguistic markers of loneliness and social isolation in clinical notes; 2) To integrate
insights derived from the tool into a suicide risk prediction model, optimized for older populations; and 3) To
undertake a comprehensive evaluation/analysis of bias and interpretability of AI models to mitigate the risk of
biases in AI algorithms. The outcomes of our project are expected to enhance the use of EHR data for better
understanding and prediction of risks linked to loneliness, social isolation, and late-life suicide, and support
monitoring efforts through an interactive dashboard. We will release our tools and models as an open-source
toolkit, enabling broad application, validation, customization and deployment. The proposed research will
strategically address a high-risk, high-reward problem, with high-utility deliverables in each aim. The overarching
objective is to foster more responsive and efficient learning healthcare systems, with future research building on
the outcomes and exploring external validations and implementation across institutions nationally.

Grant Number: 5R21MH139049-02
NIH Institute/Center: NIH
Principal Investigator: Selen Bozkurt