Despite the shortage of evidence in favor of traditional suicide risk assessments, their administration remains a standard practice in health care settings to determine a patient's level of care and support. Those identified as having a high risk typically receive the highest level of care, while those identified as low-risk are discharged.
"Using this approach, unfortunately, the high-level interventions aren't being given to the people who really need help. So we must look to reform the process and explore ways we can improve suicide prevention," Ms. Kusuma says.
Machine learning suicide screening
Ms. Kusuma says there is a need for more innovation in suicidology and a re-evaluation of standard suicide risk prediction models. Efforts to improve risk prediction have led to her research using artificial intelligence (AI) to develop suicide risk algorithms.
"Having AI that could take in a lot more data than a clinician would be able to better recognize which patterns are associated with suicide risk," Ms. Kusuma says.
In the meta-analysis study, machine learning models outperformed the benchmarks set previously by traditional clinical, theoretical and statistical suicide risk prediction models. They correctly predicted 66% of people who would experience a suicide outcome and correctly predicted 87% of people who would not experience a suicide outcome.
"Machine learning models can predict suicide deaths well relative to traditional prediction models and could become an efficient and effective alternative to conventional risk assessments," Ms. Kusuma says.
The strict assumptions of traditional statistical models do not bind machine learning models. Instead, they can be flexibly applied to large datasets to model complex relationships between many risk factors and suicidal outcomes. They can also incorporate responsive data sources, including social media, to identify peaks of suicide risk and flag times where interventions are most needed.
"Over time, machine learning models could be configured to take in more complex and larger data to better identify patterns associated with suicide risk," Ms. Kusuma says.
The use of machine learning algorithms to predict suicide-related outcomes is still an emerging research area, with 80% of the identified studies published in the past five years. Ms. Kusuma says future research will also help address the risk of aggregation bias found in algorithmic models to date.
"More research is necessary to improve and validate these algorithms, which will then help progress the application of machine learning in suicidology," Ms. Kusuma says. "While we're still a way off implementation in a clinical setting, research suggests this is a promising avenue for improving suicide risk screening accuracy in the future."
