AI-Enhanced Predictive Modeling for Identifying Depression and Delirium in Cardiovascular Patients Scheduled for Cardiac Surgery

Abstract

Several studies have demonstrated a critical association between cardiovascular disease (CVD) and mental health, revealing that approximately one-third of individuals with CVD also experience depression. This comorbidity significantly increases the risk of cardiac complications and mortality, a risk that persists regardless of traditional factors. Addressing this issue, our study pioneers a straightforward, explainable, and data-driven pipeline for predicting depression in CVD patients. Methods: Our study was conducted at a cardiac surgical intensive care unit. A total of 224 participants who were scheduled for elective coronary artery bypass graft surgery (CABG) were enrolled in the study. Prior to surgery, each patient underwent psychiatric evaluation to identify major depressive disorder (MDD) based on the DSM-5 criteria. An advanced data curation workflow was applied to eliminate outliers and inconsistencies and improve data quality. An explainable AI-empowered pipeline was developed, where sophisticated machine learning techniques, including the AdaBoost, random forest, and XGBoost algorithms, were trained and tested on the curated data based on a stratified cross-validation approach. Results: Our findings identified a significant correlation between the biomarker “sRAGE” and depression (r = 0.32, p = 0.038). Among the applied models, the random forest classifier demonstrated superior accuracy in predicting depression, with notable scores in accuracy (0.62), sensitivity (0.71), specificity (0.53), and area under the curve (0.67). Conclusions: This study provides compelling evidence that depression in CVD patients, particularly those with elevated “sRAGE” levels, can be predicted with a 62% accuracy rate. Our AI-driven approach offers a promising way for early identification and intervention, potentially revolutionizing care strategies in this vulnerable population.

Construction and validation of machine learning algorithm for predicting depression among home-quarantined individuals during the large-scale COVID-19 outbreak: based on Adaboost model

“…Currently, clinical studies have been conducted to predict depression in patients with various diseases [ 18 – 23 ], while the prediction of depression in people quarantined during epidemics is rare. Some researchers have used machine learning algorithms to predict the psychological status of some populations such as healthcare workers, students, and pregnant women during epidemics [ 20 , 27 – 28 ], but depression prediction for home-quarantined people has not been reported from Pubmed searches. In view of this, this study used a machine learning algorithm to predict the depression status of home-quarantined people during the COVID-19 epidemic.…”

“…In recent years, machine learning algorithms have also been used to predict and diagnose depression in various populations under COVID-19 pandemic conditions [ 20 , 27 – 28 ]. Healthcare workers are an important high-risk group for mental health problems during the COVID-19 pandemic.…”

Understanding Mental Health Challenges in Cardiovascular Care

“…Furthermore, it is crucial for healthcare professionals to incorporate AI advancements into their practices. For instance, Nowakowska et al highlighted AI's potential, showing a 62% accuracy in early depression detection among CVD patients [58]. In conclusion, the emerging trends mentioned above, coupled with a greater focus on holistic, patientcentered care, herald a more comprehensive and effective approach to treating cardiovascular diseases in the future.…”