![]() Individuals with chronic pain and poor mental health are often at risk of suicide however, most current opioid overdose prevention methods neither assess suicide risk nor tailor prevention methods to personal situations. ![]() These numbers may be underestimated because of the stigma associated with suicide. The American Foundation for Suicide Prevention approximates 44,965 Americans die by suicide each year, and for each suicide, 25 people attempt suicide. In 2016, suicide became the second leading cause of death for those aged 10 to 34 years. Since 2008, suicide has been the tenth leading cause of death in the United States. In 2016, the US Centers for Disease Control and Prevention reported 42,000 deaths caused by opioid overdose, resulting from both prescribed and nonprescribed opioids and including intentional (suicide) and unintentional (accidental) deaths. The ongoing opioid crisis is characterized by an increasing number of deaths caused by opioid overdose this number increased to such an extent that in 2017, the US Department of Health and Human Services declared a public health emergency. 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. 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. 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.Ĭonclusions: Opioid abuse is linked to the risk of unintentional overdose and suicide risk. 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 F 1 score of 96.6%. Amazon Mechanical Turk was used to provide labels for the out-of-sample data. We then attempted to extract out-of-sample data belonging to the intersection of suicide ideation and opioid abuse. Several traditional baselines and neural network (NN) text classifiers were trained using subreddit names as the labels and combinations of semantic inputs. We first classified suicidal versus nonsuicidal languages and then classified users with opioid usage versus those without opioid usage. 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. 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. Objective: This study aimed to extract posts of suicidality among opioid users on Reddit using machine learning methods. These individuals may instead use web-based means to articulate their concerns. Intentional overdose is difficult to detect, partially due to the lack of predictors and social stigmas that push individuals away from seeking help. However, these fatal overdoses are difficult to classify as intentional or unintentional. ![]() Many individuals who struggle with opioid use disorder are prone to suicidal ideation this may often result in overdose.
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