ArticleNeuropsychiatric disease and treatment2020
Identifying Suicidal Ideation Among Chinese Patients with Major Depressive Disorder: Evidence from a Real-World Hospital-Based Study in China.
Article in Neuropsychiatric disease and treatment, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 4 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Role of machine learning algorithms in suicide risk prediction: a systematic review-meta analysis of clinical studies.BMC medical informatics and decision making · 2024Pooled it
- Machine learning and the prediction of suicide in psychiatric populations: a systematic review.Translational psychiatry · 2024Pooled it
- The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review.Frontiers in psychiatry · 2024Pooled it
- Evidence for the biopsychosocial model of suicide: a review of whole person modeling studies using machine learning.Frontiers in psychiatry · 2023Pooled it
- Identification of suicidal ideation in major depressive disorder: A machine learning approach with multimodal digital features.PLOS digital health · 2026Article
- Prediction of self-harm in people with newly-diagnosed depression: development and validation of risk prediction models.Molecular psychiatry · 2026Article
- Identifying past-year self-reported suicidality in outpatients with somatic symptom disorder using an interpretable machine-learning model: a multicenter study with an online calculator.BMC psychiatry · 2026Observational
- Interpretable deep learning for functional MRI-based auxiliary diagnosis of major depressive disorder with suicidal ideation.Frontiers in computational neuroscience · 2026Article
- Gender differences in gray matter volume of the anterior cingulate cortex and suicidal ideation in patients with major depressive disorder: evidence from the REST-meta-MDD project.Translational psychiatry · 2025Article
- Classify the fNIRS signals of first-episode drug-naive MDD patients with or without suicidal ideation using machine learning.BMC psychiatry · 2025Article
- Structural brain changes in the anterior cingulate cortex of major depressive disorder individuals with suicidal ideation: Evidence from the REST-meta-MDD project.Psychological medicine · 2025Article
- Predicting Suicidal Ideation, Planning, and Attempts among the Adolescent Population of the United States.Healthcare (Basel, Switzerland) · 2024Article
- Predictive Models for Suicide Attempts in Major Depressive Disorder and the Contribution ofDepression and anxiety · 2024Observational
- Household air pollution from solid fuel use and depression among adults in rural China: evidence from the China Kadoorie Biobank data.BMC public health · 2023Article
- Can cognition help predict suicide risk in patients with major depressive disorder? A machine learning study.BMC psychiatry · 2022Article
- Alterations of core structural network connectome associated with suicidal ideation in major depressive disorder patients.Translational psychiatry · 2021Article
- Mapping the Presence of Anxiety Symptoms in Adults With Major Depressive Disorder.Frontiers in psychiatry · 2021Article
- Social and Behavioral Determinants of Health in the Era of Artificial Intelligence with Electronic Health Records: A Scoping Review.Health data science · 2021Article
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5 authors.
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Abstract
backgroundA 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.
methodsWe 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).
resultsThe 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).
conclusionRisk 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).
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