ArticleJournal of affective disorders2019
BP neural network prediction model for suicide attempt among Chinese rural residents.
Article in Journal of affective disorders, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 3 syntheses or guidelines pooled it, 91 citations in OpenAlex.
- Artificial intelligence in mental health care: a systematic review of diagnosis, monitoring, and intervention applications.Psychological medicine · 2025Pooled 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
- Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations.International journal of environmental research and public health · 2020Pooled it
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- Mixed-effects neural network modelling to predict longitudinal trends in fasting plasma glucose.BMC medical research methodology · 2024Article
- Research of Combined ES-BP Model in Predicting Syphilis Incidence 1982-2020 in Mainland China.Iranian journal of public health · 2023Article
- Proof of concept: Predicting distress in cancer patients using back propagation neural network (BPNN).Heliyon · 2023Article
- Forecast of pain degree of lumbar disc herniation based on back propagation neural network.Open life sciences · 2023Article
- Application of back propagation neural network model optimized by particle swarm algorithm in predicting the risk of hypertension.Journal of clinical hypertension (Greenwich, Conn.) · 2022Article
- Reading Wishes from the Lips: Cancer Patients' Need for Psycho-Oncological Support during Inpatient and Outpatient Treatment.Diagnostics (Basel, Switzerland) · 2022Article
- Artificial intelligence and suicide prevention: a systematic review.European psychiatry : the journal of the Association of European Psychiatrists · 2022Review
- Machine learning-based predictive modeling of depression in hypertensive populations.PloS one · 2022Article
- Risk factors and diagnostic prediction models for papillary thyroid carcinoma.Frontiers in endocrinology · 2022Article
- Prediction model for suicide based on back propagation neural network and multilayer perceptron.Frontiers in neuroinformatics · 2022Article
- Emotion Analysis and Happiness Evaluation for Graduates During Employment.Frontiers in psychology · 2022Article
- Prediction of Mumps Incidence Trend in China Based on Difference Grey Model and Artificial Neural Network Learning.Iranian journal of public health · 2021Article
- Multivariable Logistic Regression And Back Propagation Artificial Neural Network To Predict Diabetic Retinopathy.Diabetes, metabolic syndrome and obesity : targets and therapy · 2019Article
- Artificial intelligence in suicide prevention: Utilizing deep learning approach for early detection.Industrial psychiatry journalArticle
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
Abstract
objectiveThis study aimed to establish and assess the Back Propagation Neural Network (BPNN) prediction model for suicide attempt, so as to improve the individual prediction accuracy.
methodData was collected from a wide range case-control suicide attempt survey. 659 serious suicide attempters (case group) were randomly recruited through the hospital emergency and patient registration system from 13 rural counties in China. Each case was matched the control by same community, gender, and similar age (± 2 ages). Face to face interviews were conducted for each subject with structured questionnaire. Logistic regression was applied to preliminarily screen the factors and BPNN was used to establish the prediction model of suicide attempt.
resultsMultivariate logistic regression indicated that family history of suicide (OR = 4.146), mental problem (OR = 3.876) Low education level, poor health, aspiration strain, hopelessness, impulsivity, depression are the risk predictors and social support, coping skills, healthy community are the protect predictors for suicide attempt. Repetitious data simulation process of BPNN indicated that three-layer BPNN with 9 hidden layer neurons is the optimal prediction model. The sensitivity (67.6%), specificity (93.9%), positive predictive value (86.0%), negative predictive value (84.1%), total coincidence rate (84.6%) manifested that it is excellent to distinguish suicide attempt case.
conclusionsThe BPNN method is applicative, feasible, credible and good discriminative effect for suicide attempt. The BPNN established has significant clinical meaning for the clinical psychiatrist and lay theoretical foundation for artificial intelligence expert assisted diagnosis system for suicide attempt in the future.
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