Comprehensive Summary
The study being conducted by Ogasawaraa et al aims to explore how machine learning (ML) can be used to streamline mental health diagnosis and suicide risk analysis. During this experiment, researchers utilized 900 anonymous online chat conversations to assess basic health and psychological information using a medical questionnaire (MQ), consultation text (CT), and suicide-risk-level assessments. Researchers measured these results using only one modality, and then aggregated these to determine suicide risk factors using a combination of MQ and CT. Researchers found that a combination of these two modalities - MQ and CT - provides the greatest accuracy in results based on classification. Among the five models surveyed in the research, M3 was found to have the greatest predictive accuracy by utilizing data from intermediate results of MQ and CT testing, later aggregating these responses into a second-layer model. Among the surveyed topics in MQ, the greatest predictor of high-risk behaviors was found in "suicidal ideation.” While there were concerns about the accuracy of predictability, the research was shown to effectively prioritize high-risk individuals.
Outcomes and Implications
The prevalence of suicidal ideation and suicide itself has increased, however, the amount of clinicians that are available to see and diagnose patients has not improved, particularly in Japan, where this research is being conducted. Now, counselors have turned to AI to develop services available for people who are requesting help for mental health concerns. Machine learning (ML) is being utilized to classify users’ symptom severity. By increasing the accessibility of ML technology to help people with depression and anxiety get the assistance they need, there should be a marked decrease in suicide incidence. Although there are still issues to troubleshoot, there has been immense progress in the conglomeration of MQ and CT methods to predict suicidal ideation and depression, making this technology extremely forward-thinking.