Gaur Vikas, Maggu Gaurav, Bairwa Khushboo, Chaudhury Suprakash, Dhamija Sana, Ali Tahoora
Department of Psychiatry, JNUIMSRC, Jaipur, Rajasthan, India.
Department of Psychiatry, Dr D.Y. Patil Medical College, Dr D Y PatilVidyapeeth, Pimpri, Pune, Maharashta, India.
Ind Psychiatry J. 2024 Jul-Dec;33(2):226-233. doi: 10.4103/ipj.ipj_20_24. Epub 2024 Oct 29.
Suicide among students is increasing in India and is a matter of grave concern. Early identification of students contemplating suicide would facilitate emergency intervention and may save precious lives.
Our primary objective was to construct an artificial intelligence (AI) model employing an artificial neural network (ANN) architecture to predict students at risk of suicidal tendencies. This initiative was prompted by the necessity to implement a proactive and technologically driven strategy for identifying competitive exam-bound students facing heightened vulnerability. The aim was to facilitate timely interventions aimed at reducing the risk of self-harm.
An AI model utilizing ANNs is devised for suicide risk prediction among exam-stressed students. A 33-feature input layer is curated based on literature and expert insights, with binary features assigned weighted values. A rigorous hyperparameter optimization approach using the Optuna library to select the most effective neural network model. Ridge regression was used to determine bias or variance in the dataset. Training and testing of the model are conducted using fictional and simulated profiles, respectively, and model performance is assessed through statistical metrics and the Cohen's Kappa coefficient, benchmarked against expert evaluations.
The AI model demonstrates exceptional predictive capabilities for suicide risk assessment among competitive exam students. Quantitative Metrics: The model's accuracy of 98% aligns predictions with outcomes, distinguishing risk categories. Precision at 100% identifies cases within predicted risks, minimizing false positives. A recall of 97% identifies true risk cases, highlighting sensitivity. F1 Score: The model's F1 score of 98% balances precision and recall, indicating overall performance. Cohen's Kappa: With a coefficient of 1.00, the model's substantial agreement with experts underscores its consistent classifications.
The study introduces an AI model utilizing ANNs for suicide risk prediction among stressed students. High precision, recall, and accuracy align with expert evaluations, highlighting its promise for timely risk identification. The model's efficiency in evaluating large populations swiftly indicates its clinical potential. Refinement and real-world validation remain future considerations.
在印度，学生自杀现象呈上升趋势，这是一个令人严重关切的问题。尽早识别有自杀念头的学生将有助于进行紧急干预，并可能挽救宝贵的生命。
我们的主要目标是构建一个采用人工神经网络（ANN）架构的人工智能（AI）模型，以预测有自杀倾向风险的学生。实施这一举措的原因是有必要为识别面临更高脆弱性的备考竞争性考试的学生，实施一种积极主动且技术驱动的策略。目的是促进及时干预，以降低自我伤害风险。
设计了一个利用人工神经网络的人工智能模型，用于预测考试压力大的学生的自杀风险。基于文献和专家见解精心策划了一个具有33个特征的输入层，为二元特征分配加权值。使用Optuna库采用严格的超参数优化方法来选择最有效的神经网络模型。采用岭回归来确定数据集中的偏差或方差。分别使用虚构和模拟的档案对模型进行训练和测试，并通过统计指标和科恩卡帕系数评估模型性能，以专家评估为基准。
该人工智能模型在评估竞争性考试学生的自杀风险方面展现出卓越的预测能力。定量指标：该模型98%的准确率使预测与结果相符，区分了风险类别。100%的精确率识别出预测风险范围内的案例，将误报降至最低。97%的召回率识别出真正风险案例，突出了敏感性。F1分数：该模型98%的F1分数平衡了精确率和召回率，表明了整体性能。科恩卡帕系数：该模型与专家的一致性系数为1.00，突出了其分类的一致性。
该研究引入了一个利用人工神经网络的人工智能模型，用于预测压力大的学生的自杀风险。高精度、召回率和准确率与专家评估相符，突出了其在及时识别风险方面的前景。该模型在快速评估大量人群方面的效率表明了其临床潜力。进一步完善和进行现实世界验证仍是未来需要考虑的事项。
