Smartphones, Sensors, and Machine Learning to Advance Real-Time Prediction and Interventions for Suicide Prevention: a Review of Current Progress and Next Steps

@article{Torous2018SmartphonesSA,
  title={Smartphones, Sensors, and Machine Learning to Advance Real-Time Prediction and Interventions for Suicide Prevention: a Review of Current Progress and Next Steps},
  author={John B Torous and Mark Erik Larsen and Colin A. Depp and Theodore D. Cosco and Ian Barnett and Matthew K. Nock and Joseph Firth},
  journal={Current Psychiatry Reports},
  year={2018},
  volume={20},
  pages={1-6}
}
Purpose of ReviewAs rates of suicide continue to rise, there is urgent need for innovative approaches to better understand, predict, and care for those at high risk of suicide. Numerous mobile and sensor technology solutions have already been proposed, are in development, or are already available today. This review seeks to assess their clinical evidence and help the reader understand the current state of the field.Recent FindingsAdvances in smartphone sensing, machine learning methods, and… 
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TLDR
Results suggest that specific combinations of dynamic risk factors assessed in adolescents' daily life have promising utility in predicting next-day suicidal thoughts.
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Predicting the Utilization of Mental Health Treatment with Various Machine Learning Algorithms
— In 2017, about 792 million people (more than 10% of the global population) lived their lives with a mental disorder [24]– 78 million of which committed suicide because of it. In these unprecedented
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