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Using Machine-Learning to Diagnose Perception, Feeling, and Action: A Study of Neuroimaging, Psychometric, and Insurance Claims Data


I explored the capability of multivariate machine learning on low-signal and bioinformatic data to distinguish between different perceptual, emotional, and behavioral outcomes. In Chapter II, I designed and executed an experiment to evoke differences in empathy on the basis of religious identities as well as on general notions of ingroup and outgroup. I discovered that the act of simply learning about another’s religious identity correlated with a detectable modulation in one’s brain activity—most notably in regions related to empathy and sympathy—which identified subjects’ religious ingroups from outgroups with 70% accuracy. In Chapter III, I used multimodal data to distinguish between control participants and patients with one of two body-image disorders: anorexia nervosa and body dysmorphic disorder. These data included psychometric scales of obsessive-compulsive behaviors, depression, anxiety, and insight; white matter connectivity patterns; and neural haemodynamic activity. My models achieved 72% accuracy in distinguishing healthy controls from patients with anorexia nervosa or body dysmorphic disorder, and 76% accuracy in distinguishing patients with anorexia nervosa from those with body dysmorphic disorder. In Chapter IV, I extended these techniques to insurance claims data to predict the actions of patients and doctors. I discovered that the pattern of care delivered by doctors predicted the hospitalizations and use of biologic drugs in patients with inflammatory bowel disease. My diagnostic model may be used by healthcare alongside various interventions to reduce hospitalizations, decrease expenditures of insurance companies, and improve patient quality of life.

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