inCognita Parkinson's Disease (PD) Results

Suppose we are given access to passively-collected smartphone data for a patient and wish to predict whether the individual has a brain disorder of interest. A supervised learning approach to this task entails:

i.) acquiring smartphone sensor traces for a cohort of patients who have been labeled according to whether they have the target disorder (TD), preferably by clinical experts, and

ii.) using these examples to learn a model which recognizes the TD-status of new (unseen) persons [20]. Unfortunately, in medical passive-sensing applications there are rarely enough labeled examples to support induction of good, generalizable models.

inCognita Results: Detecting and Monitoring Parkinson's Disease with I+FLL

Results with US cohort

Cht MDD Figure 9

Results with Turkish Cohort

Cht MDD Figure 10

Concluding remarks

InCognita References

References listing for the inCognita product

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