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Researchers from the University of Johannesburg (UJ) have published a promising approach to identifying people at risk of suicide-related behavior (SRB) in the journal Scientific Reports.
The study describes using an AI algorithm, routine patient intake data and lightweight computing to estimate SRB risk following admission to a hospital emergency room (ER). The AI model was built with historical open-source patient data from the U.S. It is intended to assist ER staff rather than replace them.
Resource constraints in South Africa are a major reason to develop an AI model such as this, says lead researcher Tsholofelo Mokheleli.
Late at night and on weekends, some South African hospital emergency rooms can have one ER staff member with 30 or more patients to triage, he adds.
Mokheleli is an assistant lecturer in the Department of Applied Information Systems within the UJ College of Business and Economics (CBE). The study is part of his Ph.D. research.
During busy ER shifts, an objective, consistent measure to help staff rapidly screen patients could be helpful for early detection of SRB risk.
"The AI model will flag that a patient is at risk and provide reasons. A health care professional still needs to make the final decision, however; the clinician must do a severity scaling to determine if the person is really suicidal or not," says Mokheleli.
The two current screening methods for SRB used in South African ERs require much time and effort from clinicians, says Nompumelelo Ndlovu.
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