# Clinical Characterization and Prediction of Bipolar Disorder Evolution

## TL;DR

This paper introduces a new method using wearable sensors and mathematical models to predict and diagnose bipolar disorder more objectively.

## Contribution

The novel contribution is the development of Digital Mental Biomarkers combining complexity analysis and stochastic optimization for mental health diagnostics.

## Key findings

- Analytic indexing improves upon traditional diagnostic tools like the Young Mania Rating Scale.
- The framework enables prediction of mental disorder evolution and probability of bipolar episode progression.
- The approach offers a semicontinuous diagnostic tool for bipolar disorder with manic episode indexing.

## Abstract

Background: This paper addresses the possibility of replacing subjective evaluations of mental disorders with analytical tools based on large data provided by wearable sensors in combination with subsequent complexity mesoscale data projection using constitutive mathematical frameworks. Methods: The presented methods are based on the combination of a complexity/fractal approach and stochastic optimization, yielding Digital Mental Biomarkers (DMBs). Results: Analytic indexing can effectively augment the Young Mania Rating Scale, DSM-5 criteria, or structured interview diagnostics. The analytical approach allows us to carry out a prediction of mental disorder evolution as well as a subsequent probability characterization of BD episode progression over time. Conclusions: The presented analytical framework presents a semicontinuous diagnostic tool in the area of mental disorders, specifically applicable to bipolar disorder with corresponding manic episode indexing.