Machine learning analysis of social media data represents a promising way to capture longitudinal environmental influences contributing to individual risk for suicidal thoughts and behaviors. Our objective was to generate an algorithm termed “Suicide Artificial Intelligence Prediction Heuristic (SAIPH)” capable of predicting future risk to suicidal thought by analyzing publicly available Twitter data. We trained a series of neural networks on Twitter data queried against suicide associated psychological constructs including burden, stress, loneliness, hopelessness, insomnia, depression, and anxiety. Using 512,526 tweets from N = 283 suicidal ideation (SI) cases and 3,518,494 tweets from 2655 controls, we then trained a random forest model using neural network outputs to predict binary SI status. The model predicted N = 830 SI events derived from an independent set of 277 suicidal ideators relative to N = 3159 control events in all non-SI individuals with an AUC of 0.88 (95% CI 0.86–0.90). Using an alternative approach, our model generates temporal prediction of risk such that peak occurrences above an individual specific threshold denote a ~7 fold increased risk for SI within the following 10 days (OR = 6.7 ± 1.1, P = 9 × 10⁻⁷¹). We validated our model using regionally obtained Twitter data and observed significant associations of algorithm SI scores with county-wide suicide death rates across 16 days in August and in October, 2019, most significantly in younger individuals. Algorithmic approaches like SAIPH have the potential to identify individual future SI risk and could be easily adapted as clinical decision tools aiding suicide screening and risk monitoring using available technologies.

对社交媒体数据的机器学习分析是一种很有前途的方法，可以捕获导致个人自杀想法和行为风险的纵向环境影响。我们的目标是生成一种名为“自杀人工智能预测启发式（SAIPH）”的算法，能够通过分析公开的 Twitter 数据来预测未来自杀想法的风险。我们根据 Twitter 数据训练了一系列神经网络，这些数据针对与自杀相关的心理结构（包括负担、压力、孤独、绝望、失眠、抑郁和焦虑）进行查询。然后，我们使用来自N  = 283 个自杀意念 (SI) 案例的 512,526 条推文和来自 2655 个对照的 3,518,494 条推文，然后使用神经网络输出训练随机森林模型来预测二元 SI 状态。该模型预测N  = 830 个 SI 事件源自一组独立的 277 个自杀意念者，相对于 所有非 SI 个体中N = 3159 个对照事件，AUC 为 0.88 (95% CI 0.86–0.90)。使用替代方法，我们的模型生成风险的时间预测，使得高于个体特定阈值的峰值出现表示接下来 10 天内 SI 风险增加约 7 倍（OR = 6.7 ± 1.1，P = 9 ×  10 ⁻⁷¹） 。我们使用从地区获得的 Twitter 数据验证了我们的模型，并观察到算法 SI 评分与 2019 年 8 月和 10 月 16 天内全县自杀死亡率之间存在显着关联，尤其是在年轻人中最为显着。像 SAIPH 这样的算法方法有可能识别个人未来的 SI 风险，并且可以很容易地用作临床决策工具，利用现有技术帮助自杀筛查和风险监测。