  Multiple sclerosis (MS) affects the central nervous system with a wide range
of symptoms. MS can, for example, cause pain, changes in mood and fatigue, and
may impair a person's movement, speech and visual functions. Diagnosis of MS
typically involves a combination of complex clinical assessments and tests to
rule out other diseases with similar symptoms. New technologies, such as
smartphone monitoring in free-living conditions, could potentially aid in
objectively assessing the symptoms of MS by quantifying symptom presence and
intensity over long periods of time. Here, we present a deep-learning approach
to diagnosing MS from smartphone-derived digital biomarkers that uses a novel
combination of a multilayer perceptron with neural soft attention to improve
learning of patterns in long-term smartphone monitoring data. Using data from a
cohort of 774 participants, we demonstrate that our deep-learning models are
able to distinguish between people with and without MS with an area under the
receiver operating characteristic curve of 0.88 (95% CI: 0.70, 0.88). Our
experimental results indicate that digital biomarkers derived from smartphone
data could in the future be used as additional diagnostic criteria for MS.
