Data science in small medical datasets usually meansdoing precision guesswork on unreliable data provided by those with high expectations. The first part of this talk will focus on issues that data scientists and engineers have to address when working with this kind of data (e.g. unreliable labels, the effect of confounding factors, necessity of clinical interpretability, difficulties with fusing more data sets). The second part of the talk will include some real examples of this kind ofdata science in the field of neurology (prediction of motor deficits in Parkinson’s disease based on acoustic analysis of speech, diagnosis of Parkinson’s disease dysgraphia utilising online handwriting, exploring the Mozart effect in epilepsy based on the music information retrieval) and psychology (assessment of graphomotor disabilities in children with developmental dysgraphia).
Jiri Mekyska is the head of the BDALab (Brain Diseases Analysis Laboratory) at the Brno University of Technology, where he leads a multidisciplinary team of researchers (signal processing engineers, data scientists, neurologists, psychologists) with a special focus on the development of new digital endpoints and digital biomarkers enabling to better understand, diagnose and monitor neurodegenerative (e.g. Parkinson’s disease) and neurodevelopmental (e.g. dysgraphia) diseases.