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INTREPIBD

Identifying digital biomarkers of illness activity and treatment response in bipolar disorder.
README.md

INTREPIBD

Identifying digital biomarkers of illNess activity and Treatment REsPonse In Bipolar Disorder

Bipolar disorder (BD) is a highly prevalent disorder featuring pathological mood fluctuations, which are still precariously identified with subjective retrospective reports and scales. Despite pharmacological treatments being quite efficacious, variability in response is poorly understood. Biomarkers capable of identifying illness activity and predicting treatment response may enable a better definition of criteria to establish timely and personalized treatments. Digital biomarkers able to provide real-time patient monitoring will help optimize safe and time-effective patient management. Electrodermal activity (EDA) hyporeactivity - denoting autonomic dysfunction- has long been recognized as a strong predictive biomarker for both unipolar and bipolar depression as well as suicidal behavior. However, until a few years ago, capturing EDA was only possible with costly and complex laboratory equipment. Novel research-grade wearables now allow to continuously capture of EDA in addition to actigraphy and heart rate variability (HRV), which are also closely linked to BD illness activity while reporting real-time data of these patients.

Team: Gerard Anmella, Filippo Corponi, Bryan M. Li, Clàudia Valenzuela-Pascual, Ariadna Mas, Miriam Sanabra, Isabella Pacchiarotti, Marc Valentí, Iria Grande, Antoni Benabarre, Anna Giménez-Palomo, Marina Garriga, Isabel Agasi, Anna Bastidas, Myriam Cavero, Tabatha Fernández-Plaza, Nestor Arbelo, Miquel Bioque, Clemente García-Rizo, Norma Verdolini, Santiago Madero, Andrea Murrru, Silvia Amoretti, Anabel Martínez-Aran, Victoria Ruiz, Giovanna Fico, Michele De Prisco, Vincenzo Oliva, Aleix Solanes, Joaquim Radua, Ludovic Samalin, Allan Young, Eduard Vieta, Antonio Vergari, Diego Hidalgo-Mazzei.

Contact: Diego Hidalgo-Mazzei dahidalg@clinic.cat

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  1. TS4H2022 TS4H2022 Public

    Code for NeurIPS2022 TS4H workshop paper "Inferring mood disorder symptoms from multivariate time-series sensory data"

    Python 2 1

  2. JMIR2023 JMIR2023 Public

    Code for JMIR mHealth and uHealth paper "Exploring digital biomarkers of illness activity in mood episodes: hypotheses generating and model development study."

    Python 2 1

  3. wear-your-scales wear-your-scales Public

    Forked from april-tools/wear-your-scales

    Code for paper "Automated mood disorder symptoms monitoring from multivariate time-series sensory data: Getting the full picture beyond a single number".

    Jupyter Notebook 2

  4. e4selflearning e4selflearning Public

    Forked from april-tools/e4selflearning

    Code for paper "Wearable Data From Subjects Playing Super Mario, Taking University Exams, or Performing Physical Exercise Help Detect Acute Mood Disorder Episodes via Self-Supervised Learning: Pros…

    Python

Repositories

Showing 6 of 6 repositories
  • intrepibd.github.io Public
    INTREPIBD/intrepibd.github.io’s past year of commit activity
    SCSS 0 MIT 0 0 0 Updated Oct 17, 2024
  • .github Public
    INTREPIBD/.github’s past year of commit activity
    0 0 0 0 Updated Oct 17, 2024
  • e4selflearning Public Forked from april-tools/e4selflearning

    Code for paper "Wearable Data From Subjects Playing Super Mario, Taking University Exams, or Performing Physical Exercise Help Detect Acute Mood Disorder Episodes via Self-Supervised Learning: Prospective, Exploratory, Observational Study"

    INTREPIBD/e4selflearning’s past year of commit activity
    Python 0 MIT 2 0 0 Updated Jul 18, 2024
  • wear-your-scales Public Forked from april-tools/wear-your-scales

    Code for paper "Automated mood disorder symptoms monitoring from multivariate time-series sensory data: Getting the full picture beyond a single number".

    INTREPIBD/wear-your-scales’s past year of commit activity
    Jupyter Notebook 2 MIT 3 0 0 Updated Mar 28, 2024
  • JMIR2023 Public

    Code for JMIR mHealth and uHealth paper "Exploring digital biomarkers of illness activity in mood episodes: hypotheses generating and model development study."

    INTREPIBD/JMIR2023’s past year of commit activity
    Python 2 GPL-3.0 1 0 0 Updated Jul 26, 2023
  • TS4H2022 Public

    Code for NeurIPS2022 TS4H workshop paper "Inferring mood disorder symptoms from multivariate time-series sensory data"

    INTREPIBD/TS4H2022’s past year of commit activity
    Python 2 AGPL-3.0 1 0 0 Updated Apr 5, 2023

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