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The Proposition for Bipolar Depression Forecasting Based on Wearable Data Collection

Bipolar depression is treated wrongly as unipolar depression, on average, for 8 years. It is shown that this mismedication affects the occurrence of a manic episode and aggravates the overall condition of patients with bipolar depression. Significant effort was invested in early detection of depress...

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Autores principales: Llamocca, Pavel, López, Victoria, Čukić, Milena
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8821957/
https://www.ncbi.nlm.nih.gov/pubmed/35145422
http://dx.doi.org/10.3389/fphys.2021.777137
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author Llamocca, Pavel
López, Victoria
Čukić, Milena
author_facet Llamocca, Pavel
López, Victoria
Čukić, Milena
author_sort Llamocca, Pavel
collection PubMed
description Bipolar depression is treated wrongly as unipolar depression, on average, for 8 years. It is shown that this mismedication affects the occurrence of a manic episode and aggravates the overall condition of patients with bipolar depression. Significant effort was invested in early detection of depression and forecasting of responses to certain therapeutic approaches using a combination of features extracted from standard and online testing, wearables monitoring, and machine learning. In the case of unipolar depression, this approach yielded evidence that this data-based computational psychiatry approach would be helpful in clinical practice. Following a similar pipeline, we examined the usefulness of this approach to foresee a manic episode in bipolar depression, so that clinicians and family of the patient can help patient navigate through the time of crisis. Our projects combined the results from self-reported daily questionnaires, the data obtained from smart watches, and the data from regular reports from standard psychiatric interviews to feed various machine learning models to predict a crisis in bipolar depression. Contrary to satisfactory predictions in unipolar depression, we found that bipolar depression, having more complex dynamics, requires personalized approach. A previous work on physiological complexity (complex variability) suggests that an inclusion of electrophysiological data, properly quantified, might lead to better solutions, as shown in other projects of our group concerning unipolar depression. Here, we make a comparison of previously performed research in a methodological sense, revisiting and additionally interpreting our own results showing that the methodological approach to mania forecasting may be modified to provide an accurate prediction in bipolar depression.
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spelling pubmed-88219572022-02-09 The Proposition for Bipolar Depression Forecasting Based on Wearable Data Collection Llamocca, Pavel López, Victoria Čukić, Milena Front Physiol Physiology Bipolar depression is treated wrongly as unipolar depression, on average, for 8 years. It is shown that this mismedication affects the occurrence of a manic episode and aggravates the overall condition of patients with bipolar depression. Significant effort was invested in early detection of depression and forecasting of responses to certain therapeutic approaches using a combination of features extracted from standard and online testing, wearables monitoring, and machine learning. In the case of unipolar depression, this approach yielded evidence that this data-based computational psychiatry approach would be helpful in clinical practice. Following a similar pipeline, we examined the usefulness of this approach to foresee a manic episode in bipolar depression, so that clinicians and family of the patient can help patient navigate through the time of crisis. Our projects combined the results from self-reported daily questionnaires, the data obtained from smart watches, and the data from regular reports from standard psychiatric interviews to feed various machine learning models to predict a crisis in bipolar depression. Contrary to satisfactory predictions in unipolar depression, we found that bipolar depression, having more complex dynamics, requires personalized approach. A previous work on physiological complexity (complex variability) suggests that an inclusion of electrophysiological data, properly quantified, might lead to better solutions, as shown in other projects of our group concerning unipolar depression. Here, we make a comparison of previously performed research in a methodological sense, revisiting and additionally interpreting our own results showing that the methodological approach to mania forecasting may be modified to provide an accurate prediction in bipolar depression. Frontiers Media S.A. 2022-01-25 /pmc/articles/PMC8821957/ /pubmed/35145422 http://dx.doi.org/10.3389/fphys.2021.777137 Text en Copyright © 2022 Llamocca, López and Čukić. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Physiology
Llamocca, Pavel
López, Victoria
Čukić, Milena
The Proposition for Bipolar Depression Forecasting Based on Wearable Data Collection
title The Proposition for Bipolar Depression Forecasting Based on Wearable Data Collection
title_full The Proposition for Bipolar Depression Forecasting Based on Wearable Data Collection
title_fullStr The Proposition for Bipolar Depression Forecasting Based on Wearable Data Collection
title_full_unstemmed The Proposition for Bipolar Depression Forecasting Based on Wearable Data Collection
title_short The Proposition for Bipolar Depression Forecasting Based on Wearable Data Collection
title_sort proposition for bipolar depression forecasting based on wearable data collection
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8821957/
https://www.ncbi.nlm.nih.gov/pubmed/35145422
http://dx.doi.org/10.3389/fphys.2021.777137
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