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

NPJ Digital Medicine
Arunima RoyZachary A Kaminsky

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...Continue Reading

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Citations

Aug 23, 2020·International Journal of Environmental Research and Public Health·Rebecca A BernertFreddy Abnousi
Mar 6, 2021·Psychological Medicine·Katherine M KeyesJeffrey Shaman

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Software Mentioned

nortest
LIWC
Tweepy
M3Inference
sklearn
Apache
textBlob
Suicide Artificial Intelligence Prediction Heuristic ( SAIPH )
sklearn package
R

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