Professor Graham Martin is an Emeritus Professor in the Royal Brisbane Clinical Unit, Faculty of Medicine at The University of Queensland
Having been an avid adolescent reader of Isaac Asimov and Robert Heinlein robot stories, I was excited to read ‘Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth’.
The premise is that machine learning may ultimately be better at discrimination of suicidal youth from non-suicidal youth – and attempters from non-attempters – based on emotional reactions to key words, and MRI study of brain areas lighting up in response. The authors hint this may help clinicians struggling to predict which suicidal people may ultimately complete suicide (supposedly necessary for allocation of scant clinical resources). But this mires us in the logical fallacy that past suicidality predicts future suicidality.
In the unlikely possibility that every clinician will have future access to an MRI scanner and machine learning algorithms, the real excitement in the paper is confirmation that several cheap, available questionnaires (the ASIQ, PHQ-9, ASR, Spielberger Anxiety (State) and the CTQ) significantly discriminated between the groups.
Suicidal people and suicide attempters deserve the clinical opportunity to work through past traumas, find solutions to current problems, and plan a positive future. Perhaps we should focus scant mental health funding on more trained available clinicians.
Associate Professor Sarah Whittle is from the Melbourne School of Psychological Sciences at The University of Melbourne
Just and colleagues report in new research that brain imaging techniques can be used to predict suicidal from non-suicidal young adults. The findings contribute to a growing body of research suggesting that “biological markers” can be equally, if not more useful than subjective measures (for example, a patient’s own report of their feelings), in psychiatric decision making.
The research, however, is a long way from having an impact on the actual treatment of suicidal individuals. For one, there were a small number of participants in the study, and most were male. Therefore, we don’t know how reliable the results might be, or if they apply to females. Also, the suicidal young adults were more depressed and anxious than the non-suicidal adults. So, we don’t know if the researchers’ have found biological markers of suicidality, or psychiatric problems more generally.
If future research can show that the results are reliable, and are specific to suicidality, then it’s possible that the brain-based biological markers could be used by healthcare professionals for identification and treatment of people at risk of suicide. However, given that brain scans are costly, these tools are likely only to be used for the most severely mentally-ill patients.