scholarly journals Detection of Suicidality Among Opioid Users on Reddit: Machine Learning–Based Approach (Preprint)

2019 ◽  
Author(s):  
Hannah Yao ◽  
Sina Rashidian ◽  
Xinyu Dong ◽  
Hongyi Duanmu ◽  
Richard N Rosenthal ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Suicidal Ideation ◽  
Suicide Ideation ◽  
Opioid Abuse ◽  
Amazon Mechanical Turk ◽  
Opioid Use ◽  
Sample Data ◽  
Out Of Sample ◽  
Opioid Users

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 <i>r/suicidewatch</i>, <i>r/depression</i>, 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 <i>F</i><sub>1</sub> 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. CLINICALTRIAL

scholarly journals Detection of Suicidality Among Opioid Users on Reddit: Machine Learning–Based Approach

10.2196/15293 ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. e15293
Author(s):  
Hannah Yao ◽  
Sina Rashidian ◽  
Xinyu Dong ◽  
Hongyi Duanmu ◽  
Richard N Rosenthal ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Suicidal Ideation ◽  
Suicide Ideation ◽  
Opioid Abuse ◽  
Amazon Mechanical Turk ◽  
Opioid Use ◽  
Sample Data ◽  
Out Of Sample ◽  
Opioid Users

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.

scholarly journals Which PHQ-9 Items Can Effectively Screen for Suicide? Machine Learning Approaches

2021 ◽  
Vol 18 (7) ◽  
pp. 3339
Author(s):  
Sunhae Kim ◽  
Hye-Kyung Lee ◽  
Kounseok Lee
Keyword(s):  
Machine Learning ◽  
Primary Care ◽  
Suicidal Ideation ◽  
Random Forest ◽  
Suicide Ideation ◽  
Area Under The Curve ◽  
Learning Approaches ◽  
K Nearest Neighbors ◽  
Predictive Values ◽  
Linear Discriminant

(1) Background: The Patient Health Questionnaire-9 (PHQ-9) is a tool that screens patients for depression in primary care settings. In this study, we evaluated the efficacy of PHQ-9 in evaluating suicidal ideation (2) Methods: A total of 8760 completed questionnaires collected from college students were analyzed. The PHQ-9 was scored in combination with and evaluated against four categories (PHQ-2, PHQ-8, PHQ-9, and PHQ-10). Suicidal ideations were evaluated using the Mini-International Neuropsychiatric Interview suicidality module. Analyses used suicide ideation as the dependent variable, and machine learning (ML) algorithms, k-nearest neighbors, linear discriminant analysis (LDA), and random forest. (3) Results: Random forest application using the nine items of the PHQ-9 revealed an excellent area under the curve with a value of 0.841, with 94.3% accuracy. The positive and negative predictive values were 84.95% (95% CI = 76.03–91.52) and 95.54% (95% CI = 94.42–96.48), respectively. (4) Conclusion: This study confirmed that ML algorithms using PHQ-9 in the primary care field are reliably accurate in screening individuals with suicidal ideation.

scholarly journals Epigenome-Wide Study of Brain DNA Methylation Among Opioid Users and Controls

2020 ◽  
Author(s):  
Chang Shu ◽  
David W. Sosnowski ◽  
Ran Tao ◽  
Amy Deep-Soboslay ◽  
Joel E. Kleinman ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Multiple Testing ◽  
The United States ◽  
Opioid Abuse ◽  
Current Evidence ◽  
Opioid Use ◽  
Multiple Testing Correction ◽  
Potential Biomarker ◽  
Opioid Intoxication ◽  
Opioid Users

AbstractOpioid abuse poses significant risk to individuals in the United States and epigenetic changes are a leading potential biomarker of abuse. Current evidence, however, is mostly limited to candidate gene analysis in whole blood. To clarify the association between opioid abuse and DNA methylation, we conducted an epigenome-wide analysis (EWAS) of DNA methylation in brains of individuals who died from opioid intoxication and controls. Tissue samples were extracted from the dorsolateral prefrontal cortex of 160 deceased individuals (Mage = 35.15, SD = 9.42 years; 62% male; 78% White). The samples included 73 individuals who died of opioid intoxication, 59 group-matched psychiatric controls, and 28 group-matched normal controls. EWAS was implemented using the Illumina Infinium MethylationEPIC BeadChip; analyses adjusted for sociodemographic characteristics, negative control and ancestry principal components, cellular composition, and surrogate variables. Epigenetic age was calculated using the Horvath and Levine clocks, and gene ontology (GO) analyses were performed. No CpG sites were epigenome-wide significant after multiple testing correction, but 13 sites reached nominal significance (p < 1.0 x 10-5). There was a significant association between opioid use and Levine phenotypic age (b = 2.24, se = 1.11, p = .045). Opioid users were approximately two years phenotypically older compared to controls. GO analyses revealed enriched pathways related to cell function and neuron differentiation, but no terms survived multiple testing correction. Results inform our understanding of the neurobiology of opioid use, and future research with larger samples across stages of opioid use will elucidate the complex genomics of opioid abuse.

The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions (Preprint)

2018 ◽  
Author(s):  
Roberto Acuña
Keyword(s):  
Machine Learning ◽  
Suicidal Ideation ◽  
Suicide Ideation ◽  
Machine Learning Techniques ◽  
World Health ◽  
Global Public Health ◽  
Systematic Analysis ◽  
Middle Income ◽  
Learning Techniques ◽  
Health Organization

BACKGROUND According to the World Health Organization (WHO) close to 800,000 people worldwide death by suicidal each year. Many more attempt to do it. In consequence, the WHO recognizes suicide as a global public health priority, which affects not only rich countries, but poor and middle income countries as well. OBJECTIVE The aim of this study is to evaluate several supervised classifiers for detecting messages with suicidal ideation in order to know if these systems can be used in automatic suicide prevention systems. METHODS We used machine learning techniques to make a systematic analysis of 28 supervised classifier algorithms with parameters by defect. The Life Corpus, used in this research, is a bilingual corpus (English and Spanish) oriented to suicide. The corpus was constructed by two annotation experts, retrieving texts from several social networks. The corpus quality was measured using mutual annotation agreement. RESULTS The different experiments determined that the classifier with the best performance was KStar, with the corpus version POS-SYNSETS-NUM; and the cycle with 2 classes Urgent and No Risk was the one that achieved the best results with the PRC-Area metrics of 0,81036 and F-measure of 0,7148. CONCLUSIONS The present research fulfilled the objective of discovering which characteristics are the most suitable for the automatic classification of messages with suicidal ideation, using the Life Corpus. The results of this evaluation demonstrate that the Life Corpus and machine learning techniques could be suitable for detecting suicide ideation messages.

scholarly journals Neural Network-Based Model Reduction of Hydrodynamics Forces on an Airfoil

Fluids ◽  
2021 ◽  
Vol 6 (9) ◽  
pp. 332
Author(s):  
Hamayun Farooq ◽  
Ahmad Saeed ◽  
Imran Akhtar ◽  
Zafar Bangash
Keyword(s):  
Neural Network ◽  
Flow Field ◽  
Surface Pressure ◽  
Numerical Data ◽  
Sample Data ◽  
Out Of Sample ◽  
Temporal Pressure ◽  
Using Data ◽  
Artificial Neural Network Ann ◽  
Lift And Drag

In this paper, an artificial neural network (ANN)-based reduced order model (ROM) is developed for the hydrodynamics forces on an airfoil immersed in the flow field at different angles of attack. The proper orthogonal decomposition (POD) of the flow field data is employed to obtain pressure modes and the temporal coefficients. These temporal pressure coefficients are used to train the ANN using data from three different angles of attack. The trained network then takes the value of angle of attack (AOA) and past POD coefficients as an input and predicts the future temporal coefficients. We also decompose the surface pressure modes into lift and drag components. These surface pressure modes are then employed to calculate the pressure component of lift CLp and drag CDp coefficients. The train model is then tested on the in-sample data and out-of-sample data. The results show good agreement with the true numerical data, thus validating the neural network based model.

scholarly journals Acoustic and language analysis of speech for suicide ideation among US veterans

2020 ◽  
Author(s):  
Anas Belouali ◽  
Samir Gupta ◽  
Vaibhav Sourirajan ◽  
Jiawei Yu ◽  
Nathaniel Allen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Suicidal Ideation ◽  
High Risk ◽  
Suicide Ideation ◽  
Statistical Significance ◽  
Real Life ◽  
Mobile App ◽  
Machine Learning Algorithms ◽  
Self Report ◽  
Linguistic Features

U.S. veterans are 1.5 times more likely to die by suicide than Americans who never served in the military. Considering such high rates, there is an urgent need to develop innovative approaches for objective and clinically applicable assessments to detect individuals at high risk. We hypothesize that speech in suicidal veterans has a range of distinctive acoustic and linguistic features. The purpose of this work is to build an automated machine learning and natural language processing tool to screen for suicidality. Veterans made 588 narrative audio recordings via a mobile app in a real-life setting. In addition, veterans completed self-report psychiatric scales and questionnaires. Recordings were analyzed to extract voice characteristics including prosodic, phonation, and glottal. The audios were also transcribed to extract textual features for linguistic analysis. We evaluated the acoustic and linguistic features using both statistical significance and ensemble feature selection. We also examined the performance of different machine learning algorithms on multiple combinations of features to classify suicidal and non-suicidal audios. Random Forest classifier correctly identified suicidal ideation in veterans based on the combined set of acoustic and linguistic features of speech with 86% sensitivity, 70% specificity, and an area under the receiver operating characteristic curve (AUC) of 80%. Speech analysis of audios collected from veterans in everyday life settings using smartphones is a promising approach for suicidal ideation detection. A machine learning classifier may eventually help clinicians identify and monitor high-risk veterans.

scholarly journals Deep learning multidimensional projections

2020 ◽  
Vol 19 (3) ◽  
pp. 247-269 ◽  
Author(s):  
Mateus Espadoto ◽  
Nina Sumiko Tomita Hirata ◽  
Alexandru C Telea
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Projection Methods ◽  
Multidimensional Data ◽  
Sample Data ◽  
Out Of Sample ◽  
Reduction Methods ◽  
High Dimensional Datasets ◽  
Sample Set ◽  
Learning Data

Dimensionality reduction methods, also known as projections, are often used to explore multidimensional data in machine learning, data science, and information visualization. However, several such methods, such as the well-known t-distributed stochastic neighbor embedding and its variants, are computationally expensive for large datasets, suffer from stability problems, and cannot directly handle out-of-sample data. We propose a learning approach to construct any such projections. We train a deep neural network based on sample set drawn from a given data universe, and their corresponding two-dimensional projections, compute with any user-chosen technique. Next, we use the network to infer projections of any dataset from the same universe. Our approach generates projections with similar characteristics as the learned ones, is computationally two to four orders of magnitude faster than existing projection methods, has no complex-to-set user parameters, handles out-of-sample data in a stable manner, and can be used to learn any projection technique. We demonstrate our proposal on several real-world high-dimensional datasets from machine learning.

scholarly journals Neural Network Models for Empirical Finance

2020 ◽  
Vol 13 (11) ◽  
pp. 265
Author(s):  
Hector F. Calvo-Pardo ◽  
Tullio Mancini ◽  
Jose Olmo
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Asset Allocation ◽  
Ensemble Methods ◽  
Network Models ◽  
Neural Network Models ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Out Of Sample

This paper presents an overview of the procedures that are involved in prediction with machine learning models with special emphasis on deep learning. We study suitable objective functions for prediction in high-dimensional settings and discuss the role of regularization methods in order to alleviate the problem of overfitting. We also review other features of machine learning methods, such as the selection of hyperparameters, the role of the architecture of a deep neural network for model prediction, or the importance of using different optimization routines for model selection. The review also considers the issue of model uncertainty and presents state-of-the-art methods for constructing prediction intervals using ensemble methods, such as bootstrap and Monte Carlo dropout. These methods are illustrated in an out-of-sample empirical forecasting exercise that compares the performance of machine learning methods against conventional time series models for different financial indices. These results are confirmed in an asset allocation context.

Deep Learning Techniques and Optimization Strategies in Big Data Analytics

2020 ◽  
pp. 142-153 ◽  
Author(s):  
Murugan Krishnamoorthy ◽  
Bazeer Ahamed B. ◽  
Sailakshmi Suresh ◽  
Solaiappan Alagappan
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Learning Algorithm ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Learning Techniques ◽  
Sample Data ◽  
Increase Productivity ◽  
The Given

Construction of a neural network is the cardinal step to any machine learning algorithm. It requires profound knowledge for the developer in assigning the weights and biases to construct it. And the construction should be done for multiple epochs to obtain an optimal neural network. This makes it cumbersome for an inexperienced machine learning aspirant to develop it with ease. So, an automated neural network construction would be of great use and provide the developer with incredible speed to program and run the machine learning algorithm. This is a crucial assist from the developer's perspective. The developer can now focus only on the logical portion of the algorithm and hence increase productivity. The use of Enas algorithm aids in performing the automated transfer learning to construct the complete neural network from the given sample data. This algorithm proliferates on the incoming data. Hence, it is very important to inculcate it with the existing machine learning algorithms.

The association of psychedelic use and opioid use disorders among illicit users in the United States

2017 ◽  
Vol 31 (5) ◽  
pp. 606-613 ◽  
Author(s):  
Vincent D Pisano ◽  
Nathaniel P Putnam ◽  
Hannah M Kramer ◽  
Kevin J Franciotti ◽  
John H Halpern ◽  
...  
Keyword(s):  
Drug Use ◽  
Risk Ratio ◽  
Opioid Dependence ◽  
Substance Misuse ◽  
Illicit Drug ◽  
Opioid Abuse ◽  
Opioid Use ◽  
Opioid Use Disorders ◽  
Opioid Users ◽  
Reduced Risk

Background: Preliminary studies show psychedelic compounds administered with psychotherapy are potentially effective and durable substance misuse interventions. However, little is known about the association between psychedelic use and substance misuse in the general population. This study investigated the association between psychedelic use and past year opioid use disorders within illicit opioid users. Methods: While controlling for socio-demographic covariates and the use of other substances, the relationship between classic psychedelic use and past year opioid use disorders was analyzed within 44,000 illicit opioid users who completed the National Survey on Drug Use and Health from 2008 to 2013. Results: Among respondents with a history of illicit opioid use, psychedelic drug use is associated with 27% reduced risk of past year opioid dependence (weighted risk ratio = 0.73 (0.60–0.89) p = 0.002) and 40% reduced risk of past year opioid abuse (weighted risk ratio = 0.60 (0.41–0.86) p = 0.006). Other than marijuana use, which was associated with 55% reduced risk of past year opioid abuse (weighted risk ratio = 0.45 (0.30–0.66) p < 0.001), no other illicit drug was associated with reduced risk of past year opioid dependence or abuse. Conclusion: Experience with psychedelic drugs is associated with decreased risk of opioid abuse and dependence. Conversely, other illicit drug use history is largely associated with increased risk of opioid abuse and dependence. These findings suggest that psychedelics are associated with positive psychological characteristics and are consistent with prior reports suggesting efficacy in treatment of substance use disorders.

Sign in / Sign up

Export Citation Format

Share Document