%0期刊文章%@ 1438- 8871% I JMIR出版物%V 22卡塔尔世界杯8强波胆分析% N 11% P e15293 %T在Reddit上阿片类药物使用者的自杀性检测:基于机器学习的方法%A Yao,Hannah %A Rashidian,Sina %A Dong,Xinyu %A Duanmu,Hongyi %A Rosenthal,Richard N %A Wang,Fusheng %+石溪大学，2313D计算机科学，Stony Brook, NY, 11794，美国，1 631 632 2594,fusheng.wang@stonybrook.edu %K阿片类流行病%K阿片类相关疾病%K自杀%K社交媒体%K机器学习%K深度学习%K自然语言处理%D 2020 %7 27.11.2020 %9原创论文%J J Med Internet Res %G英语%X背景:近年来，自杀率和吸毒过量率都在上升。许多与阿片类药物使用障碍作斗争的人容易产生自杀意念;这通常会导致用药过量。然而，这些致命的过量服用很难归类为有意或无意。故意过量服用很难被发现，部分原因是缺乏预测因素和社会污名，导致个人不愿寻求帮助。这些人可能会转而使用网络手段来表达他们的担忧。目的:本研究旨在利用机器学习方法提取阿片类药物使用者在Reddit上的自杀帖子。模型的性能是数据纯度的衍生品，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一部分提供新的见解。 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. %M 33245287 %R 10.2196/15293 %U //www.mybigtv.com/2020/11/e15293/ %U https://doi.org/10.2196/15293 %U http://www.ncbi.nlm.nih.gov/pubmed/33245287