New AI methods investigate clearer depression and schizophrenia subgroups from small, noisy trials, using model abstention to boost interpretability and improve patient stratification.
Artificial intelligence tools often used in drug development today were not optimal for stratifying psychiatric trial populations, shared Joseph Geraci, PhD. A recent study evaluated how artificial intelligence could characterize patient subpopulations from small clinical trial and trial-like data sets, with 2 goals: to reveal new information about major depression and schizophrenia, and to show that stratifying a patient population and identifying the factors driving each subpopulation was highly nontrivial.1 A cancer data set was also analyzed, and specialized tools provided a measurable lift to standard models.
Standard machine learning algorithms classified and modeled well when objects were relatively simple, such as sports teams or images of cats and dogs, but patient populations presented greater heterogeneity and noise. Labels such as responder, nonresponder, and placebo responder were also less clean than image labels. “The complexity is that the labels themselves are not completely reliable because there’s so much phenomenon loaded behind it,” Geraci said. His mathematical methods were designed so a model could remain honest about which patients in a small data set actually adhered to their labels, allowing researchers to learn from them.
Life event that changes all: Horse riding accident in Zimbabwe in 1993, a fractured skull et al including bipolar anxiety, chronic fatigue …. co-morbidities (Nietzche 'He who has the reason why can deal with any how' details my health history from 1993 to date). 17th 2017 August operation for breast cancer (no indications just an appointment came from BreastCheck through the Post). Trinity College Dublin Business Economics and Social Studies (but no degree) 1997-2003; UCD 1997/1998 night classes) essays, projects, writings. Trinity Horizon Programme 1997/98 (Centre for Women Studies Trinity College Dublin/St. Patrick's Foundation (Professor McKeon) EU Horizon funded: research study of 15 women (I was one of this group and it became the cornerstone of my journey to now 2017) over 9 mth period diagnosed with depression and their reintegration into society, with special emphasis on work, arts, further education; Notes from time at Trinity Horizon Project 1997/98; Articles written for Irishhealth.com 2003/2004; St Patricks Foundation monthly lecture notes for a specific period in time; Selection of Poetry including poems written by people I know; Quotations 1998-2017; other writings mainly with theme of social justice under the heading Citizen Journalism Ireland. Letters written to friends about life in Zimbabwe; Family history including Michael Comyn KC, my grandfather, my grandmother's family, the O'Donnellan ffrench Blake-Forsters; Moral wrong: An acrimonious divorce but the real injustice was the Catholic Church granting an annulment – you can read it and make your own judgment, I have mine. Topics I have written about include annual Brain Awareness week, Mashonaland Irish Associataion in Zimbabwe, Suicide (a life sentence to those left behind); Nostalgia: Tara Hill, Co. Meath.
