A machine learning approach predicts future risk to suicidal ideation from social media data

Abstract 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 Heuri...

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Autores principales:	Arunima Roy, Katerina Nikolitch, Rachel McGinn, Safiya Jinah, William Klement, Zachary A. Kaminsky
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/003b9572f6104942a46ea1e61f883c2d
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spelling	oai:doaj.org-article:003b9572f6104942a46ea1e61f883c2d2021-12-02T16:53:20ZA machine learning approach predicts future risk to suicidal ideation from social media data10.1038/s41746-020-0287-62398-6352https://doaj.org/article/003b9572f6104942a46ea1e61f883c2d2020-05-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-0287-6https://doaj.org/toc/2398-6352Abstract 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.Arunima RoyKaterina NikolitchRachel McGinnSafiya JinahWilliam KlementZachary A. KaminskyNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Arunima Roy Katerina Nikolitch Rachel McGinn Safiya Jinah William Klement Zachary A. Kaminsky A machine learning approach predicts future risk to suicidal ideation from social media data
description	Abstract 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.
format	article
author	Arunima Roy Katerina Nikolitch Rachel McGinn Safiya Jinah William Klement Zachary A. Kaminsky
author_facet	Arunima Roy Katerina Nikolitch Rachel McGinn Safiya Jinah William Klement Zachary A. Kaminsky
author_sort	Arunima Roy
title	A machine learning approach predicts future risk to suicidal ideation from social media data
title_short	A machine learning approach predicts future risk to suicidal ideation from social media data
title_full	A machine learning approach predicts future risk to suicidal ideation from social media data
title_fullStr	A machine learning approach predicts future risk to suicidal ideation from social media data
title_full_unstemmed	A machine learning approach predicts future risk to suicidal ideation from social media data
title_sort	machine learning approach predicts future risk to suicidal ideation from social media data
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/003b9572f6104942a46ea1e61f883c2d
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A machine learning approach predicts future risk to suicidal ideation from social media data

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