Predicting outcome for psychotic disorders, using brain connectivity and transcribed speech data
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Psychotic disorders affect approximately 1% of the population (Perälä et al., 2007) and are among the most disabling health conditions. Currently, these disorders are diagnosed solely through clinical assessments of patients’ symptoms, which can be subjective, with no automated, quantitative metrics available to clinicians. The biological mechanisms underlying these disorders are also poorly understood. I will discuss work using both transcribed speech and brain connectivity data to tackle these challenges, drawing on approaches from Network Science, NLP and Machine Learning.
This talk is part of the Artificial Intelligence Research Group Talks (Computer Laboratory) series.
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