Ives Cavalcante Passos
HCPA-UFRGS - Brazil
I am a professor of psychiatry and researcher trained in
(1) neuroscience and mental health with a focus on bipolar disorders and suicide, and (2) artificial intelligence, mainly looking at machine learning algorithms and their application in medicine, and particularly in complex mental health disorders. My commitment to the advancement of knowledge has resulted in the publication of 90 articles in top peer-reviewed journals on mental health, including The Lancet Psychiatry, JAMA Psychiatry, and Molecular Psychiatry. Among these articles 15 are first-author papers and 20 are last-author papers. It is worth mentioning that 24 of these studies are focused on the characterization of variables associated to suicide among psychiatric disorder, and 28 present the use of machine learning techniques to predict clinical outcomes, including suicide attempts, in the field of mental health.
• I have an h-index of 18 according to Publons/Web of Science; 22 according to Google Scholar; and 19 according to Scopus.
• In 2016 I was selected as one of the 20 Young Physician Leaders worldwide by The InterAcademy Medical Panel and the M8 Alliance of Academic Health Centers and represented my country at the 2016 World Health Summit in Berlin.
• In the same year, I was selected as a Young Physician Leader of the Brazilian Academy of Medicine.
My work has pioneered the field of using machine learning techniques to analyze big data in mental health. By applying these techniques, I have built clinically useful predictive models able to: accurately identify which patients are likely to attempt suicide; improve diagnosis and conversion to a specific disorder in mental health; and identify cognitive impairment in patients with mood disorders. I further authored several editorials on the impact of artificial intelligence in medicine and mental health, proposing that groundbreaking discoveries and changes at the population level would involve data integration focused on a person-centered approach. Of note, my postdoctoral training was in machine learning algorithms to analyze large amounts of data from multiple biological levels, such as neuroimaging, neurocognition, and genetics, to address complex mental health disorders. In this regard, I recently contributed to and co-edited a book entitled Personalized Psychiatry: Big Data Analytics in Mental Health published by Springer Nature in 2019.
1. Machado CDS, Ballester PL, Cao B, Mwangi B, Caldieraro MA, Kapczinski F, Passos IC. Prediction of suicide attempts in a prospective cohort study with a nationally representative sample of the US population. Psychol Med. 2021 Jan 14:1-12. doi: 10.1017/S0033291720004997. Epub ahead of print. PMID: 33441206.
2. Passos IC, Ballester P. Positive predictive values and potential success of suicide prediction models. JAMA Psychiatry, 2019 Jun 26. doi:10.1001/jamapsychiatry.2019.1507
3. Passos IC. Could an algorithm help prevent suicides? J Affect Disord. 2021 Aug 1;291:252-253. doi: 10.1016/j.jad.2021.05.016. Epub 2021 May 23. PMID: 34052747.
4. Passos IC, Ballester PL, Barros RC, Librenza-Garcia D, Mwangi B, Birmaher B, Brietzke E, Hajek T, Lopez Jaramillo C, Mansur RB, Alda M, Haarman BCM, Isometsa E, Lam RW, McIntyre RS, Minuzzi L, Kessing LV, Yatham LN, Duffy A, Kapczinski F. Machine learning and big data analytics in bipolar disorder: A position paper from the International Society for Bipolar Disorders Big Data Task Force. Bipolar Disord. 2019 Nov;21(7):582-594. doi: 10.1111/bdi.12828. Epub 2019 Sep 18. PMID: 31465619.
5. Passos IC, Mwangi B. Machine learning-guided intervention trials to predict treatment response at an individual patient level: an important second step following randomized clinical trials. Molecular Psychiatry, 2018 Sep 21. doi:10.1038/s41380-018-0250-y
6. Passos IC, et al. Big data analytics and machine learning: 2015 and beyond. The Lancet Psychiatry, 2016;3(1):13–15.