<p>Identifying Suicidal Ideation Among Chinese Patients with Major Depressive Disorder: Evidence from a Real-World Hospital-Based Study in China</p>

Abstract

These authors contributed equally to this work Background: A growing body of research suggests that major depressive disorder (MDD) is one of the most common psychiatric conditions associated with suicide ideation (SI). However, how a combination of easily accessible variables built a utility clinically model to estimate the probability of an individual patient with SI via machine learning is limited. Methods: We used the electronic medical record database from a hospital located in western China. A total of 1916 Chinese patients with MDD were included. Easily accessible data (demographic, clinical, and biological variables) were collected at admission (on the first day of admission) and were used to distinguish SI with MDD from non-SI using a machine learning algorithm (neural network). Results: The neural network algorithm distinguished 1356 out of 1916 patients translating into 70.08% accuracy (70.68% sensitivity and 67.09% specificity) and an area under the curve (AUC) of 0.76. The most relevant predictor variables in identifying SI from non-SI included free thyroxine (FT4), the total scores of Hamilton Depression Scale (HAMD), vocational status, and free triiodothyronine (FT3). Conclusion: Risk for SI among patients with MDD can be identified at an individual subject level by integrating demographic, clinical, and biological variables as possible as early during hospitalization (at admission).

Can cognition help predict suicide risk in patients with major depressive disorder? A machine learning study

“…XGBoost-2 resulted from adding features of cognitive function (SST, IGT) to XGBoost-1. Our ML classifiers presented relatively good performance in the task of classifying suicide attempters in MDD patients in accordance with previous studies that used shallow or deep learning algorithms [ 17 , 36 , 37 ]. According to the statistical indicators, ROC curve, DCA, and NRI, when cognitive function was incorporated into XGBoost model, it exhibited improved model fit and superior predictive accuracy and improved patients net benefits, while maintaining the same level of sensitivity for DSA.…”

“…Some ML algorithms have been developed to build suicide predictive models, using methods such as Decision Tree, Support Vector Machine, Random Forest, Naïve Bayes, and Artificial Neural Network [ 15 , 16 ]. The current suicide attempt predictive models were developed mostly based on demographics, clinical information, and biological variables [ 17 ]. Most models recognized that childhood trauma [ 18 , 19 ], impulsivity, and aggression [ 2 , 20 ] were risk factors for suicide attempts [ 21 ].…”

Alterations of core structural network connectome associated with suicidal ideation in major depressive disorder patients

“…A recent study suggested that the lifetime prevalence of MDD patients related to SI is estimated to be 53.1% 4 . Therefore, SI is regarded as a red alert and maybe the primary predictor for suicide in MDD patients 5 . Although several sensitive brain regions in MDD patients in relation to SI have been identified across several decades of research 6 , 7 , knowledge about whole brain structural network basis associated with SI is still limited.…”

Mapping the Presence of Anxiety Symptoms in Adults With Major Depressive Disorder

“…Eligible patients were those with a diagnosis of MDD, aged between 18 and 65. In this study, we identified MDD patients through recorded primary diagnosis at discharge and based on the International Classification of Disease, Tenth Revision (Clinical Modification Codes F32, single episode major depressive disorder and F33, recurrent major depressive disorder), which has been described in another study ( 35 ). We extracted anonymous clinical-related information.…”