inCognita Major Depressive Disorder (MDD) 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.

Introduction

inCognita Results: Detecting and Monitoring Depression with I+FLL

Cht MDD Figure 5
Cht MDD Figure 6

Smartphone-based patient monitoring to distinguish between improving and worsening depression trends

Cht MDD Figure 7

Enhancing Depression Detection via Learning from synthetic patients (LSP)

Cht MDD Figure 8

Concluding remarks

InCognita References

References listing for the inCognita product

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