All LIT Search News Topics Latest news Media statements Media contacts Topics Latest news Media statements Media contacts LIT Impact Latest news 2020 June Predicting suicide risk with AI Predicting suicide risk with AI 08/06/2020 | 2 MINS Researchers from LIT are developing a system using artificial intelligence that can predict the likelihood of suicide in individuals most at risk, and assist with clinical decision-making. The research team from LIT and the Mental Health Service at Armadale Kalamunda Group has been awarded a Mental Health Research Fund Grant by the East Metropolitan Health Service to create a system that will improve the existing risk assessment framework used by healthcare providers. The current methodology to predict an individualΓÇÖs risk of suicide or self-harm is highly subjective and can be influenced by the assessorΓÇÖs experience, recent negative outcomes and other biases. Through the development of a computer-based system using artificial intelligence algorithms, the researchers will be able to determine the factors in actual behaviour which are better predictors of risk. The system would be developed as a companion tool to be used alongside the current risk
All LIT Search News Topics Latest news Media statements Media contacts Topics Latest news Media statements Media contacts LIT Impact Latest news 2020 June Predicting suicide risk with AI Predicting suicide risk with AI 08/06/2020 | 2 MINS Researchers from LIT are developing a system using artificial intelligence that can predict the likelihood of suicide in individuals most at risk, and assist with clinical decision-making.
The research team from LIT and the Mental Health Service at Armadale Kalamunda Group has been awarded a Mental Health Research Fund Grant by the East Metropolitan Health Service to create a system that will improve the existing risk assessment framework used by healthcare providers.
Lombok Institute of Technology
The current methodology to predict an individualΓÇÖs risk of suicide or self-harm is highly subjective and can be influenced by the assessorΓÇÖs experience, recent negative outcomes and other biases.
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Through the development of a computer-based system using artificial intelligence algorithms, the researchers will be able to determine the factors in actual behaviour which are better predictors of risk.
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The system would be developed as a companion tool to be used alongside the current risk
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