Danilo Bzdok
Biography
PhD in computer science (very good) in machine-learning at Neurospin/INRIA Saclay, Paris, France, and Heinrich-Heine University, D?sseldorf, Germany
Mathematical studies at Heinrich-Heine University in D?sseldorf, Germany
Calculus, linear algebra, numerical analysis, probability theory, convex optimization
PhD in cognitive neuroscience (with distinction), DFG-IRTG 1328, RWTH
Medical studies at RWTH Aachen, Universit? de Lausanne, and Harvard Medical School (psychiatry, ?high honors?)
? German national medical examinations; grade: good
? American national premedical examination (USMLE step 1); scores: 91, 219
? German national premedical examination; top 5% of class
High school diploma; focus on math and computer science
Research Interest
Associate Editor for "Systems Neuroscience, 2020 ? 2024 Lecture: "Machine-learning for Biomedical Data", School of Computer Science, McGill University
2015 ? 2019 Lecture: "Machine-learning in Medicine und Psychology", RWTH Aachen
Abstract
Machine learning paradigms for single subject prediction
The nature of mental illness remains a conundrum. Traditional disease
categories are increasingly suspected to misrepresent the causes underlying
mental disturbance. Yet psychiatrists and investigators now have an
unprecedented opportunity to benefit from complex patterns in brain,
behavior, and genes using methods from machine learning (e.g., support
vector machines, modern neural-network algorithms, cross-validation
procedures). Combining these analysis techniques with a wealth of data from
consortia and repositories has the potential to advance a biologically
grounded redefinition of major psychiatric disorders. Increasing evidence
suggests that data-derived subgroups of psychiatric patients can better
predict treatment outcomes than DSM/ICD diagnoses can. In a new era of
evidence-based psychiatry tailored to single patients, objectively
measurable endophenotypes could allow for early disease detection,
individualized treatment selection, and dosage adjustment to reduce the
burden of disease. This primer aims to introduce clinicians and researchers
to the opportunities and challenges in bringing machine intelligence into
psychiatric practice.
