Background: In recent years, both suicide and overdose rates have been increasing. Many individuals who struggle with opioid use disorder are prone to suicidal ideation; this may often result in overdose. However, these fatal overdoses are difficult to classify as intentional or unintentional. Intentional overdose is difficult to detect, partially due to the lack of predictors and social stigmas that push individuals away from seeking help. These individuals may instead use web-based means to articulate their concerns. Objective: This study aimed to extract posts of suicidality among opioid users on Reddit using machine learning methods. The performance of the models is derivative of the data purity, and the results will help us to better understand the rationale of these users, providing new insights into individuals who are part of the opioid epidemic. Methods: Reddit posts between June 2017 and June 2018 were collected from r/suicidewatch, r/depression, a set of opioid-related subreddits, and a control subreddit set. We first classified suicidal versus nonsuicidal languages and then classified users with opioid usage versus those without opioid usage. Several traditional baselines and neural network (NN) text classifiers were trained using subreddit names as the labels and combinations of semantic inputs. We then attempted to extract out-of-sample data belonging to the intersection of suicide ideation and opioid abuse. Amazon Mechanical Turk was used to provide labels for the out-of-sample data. Results: Classification results were at least 90% across all models for at least one combination of input; the best classifier was convolutional neural network, which obtained an F1 score of 96.6%. When predicting out-of-sample data for posts containing both suicidal ideation and signs of opioid addiction, NN classifiers produced more false positives and traditional methods produced more false negatives, which is less desirable for predicting suicidal sentiments. Conclusions: Opioid abuse is linked to the risk of unintentional overdose and suicide risk. Social media platforms such as Reddit contain metadata that can aid machine learning and provide information at a personal level that cannot be obtained elsewhere. We demonstrate that it is possible to use NNs as a tool to predict an out-of-sample target with a model built from data sets labeled by characteristics we wish to distinguish in the out-of-sample target. Trial Registration:

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中文翻译：

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Reddit 上阿片类药物使用者自杀倾向的检测：基于机器学习的方法


背景：近年来，自杀率和服药过量率都在上升。许多患有阿片类药物使用障碍的人容易产生自杀意念。这通常可能导致服用过量。然而，这些致命的过量用药很难归类为有意或无意。故意服用过量药物很难被发现，部分原因是缺乏预测因素和社会耻辱，使人们不愿寻求帮助。这些人可能会使用基于网络的方式来表达他们的担忧。目的：本研究旨在使用机器学习方法提取 Reddit 上阿片类药物使用者自杀倾向的帖子。模型的性能是数据纯度的导数，结果将帮助我们更好地理解这些用户的基本原理，为阿片类药物流行的个人提供新的见解。方法：2017 年 6 月至 2018 年 6 月期间的 Reddit 帖子收集自 r/suicidewatch、r/depression、一组与阿片类药物相关的 subreddit 和一组对照 subreddit 集。我们首先对自杀性语言和非自杀性语言进行分类，然后对使用阿片类药物的用户和不使用阿片类药物的用户进行分类。使用 subreddit 名称作为标签和语义输入的组合来训练几个传统的基线和神经网络 (NN) 文本分类器。然后，我们尝试提取属于自杀意念和阿片类药物滥用交叉点的样本外数据。 Amazon Mechanical Turk 用于为样本外数据提供标签。结果：对于至少一种输入组合，所有模型的分类结果至少为 90%；最好的分类器是卷积神经网络，其 F1 分数为 96.6%。 当预测包含自杀意念和阿片类药物成瘾迹象的帖子的样本外数据时，神经网络分类器产生更多的假阳性，而传统方法产生更多的假阴性，这对于预测自杀情绪来说不太理想。结论：阿片类药物滥用与意外过量用药和自杀风险相关。 Reddit 等社交媒体平台包含元数据，可以帮助机器学习并提供在其他地方无法获得的个人信息。我们证明，可以使用神经网络作为工具来预测样本外目标，该模型是根据我们希望在样本外目标中区分的特征标记的数据集构建的。试用注册：


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