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Forecasting Mood in Bipolar Disorder From Smartphone Self-assessments: Hierarchical Bayesian Approach
- Source :
- JMIR mHealth and uHealth, JMIR mHealth and uHealth, Vol 8, Iss 4, p e15028 (2020), Busk, J, Faurholt-Jepsen, M, Frost, M, Bardram, J E, Vedel Kessing, L & Winther, O 2020, ' Forecasting Mood in Bipolar Disorder From Smartphone Self-assessments : Hierarchical Bayesian Approach ', JMIR mHealth and uHealth, vol. 8, no. 4, e15028 . https://doi.org/10.2196/15028, Busk, J, Faurholt-Jepsen, M, Frost, M, Bardram, J E, Kessing, L V & Winther, O 2020, ' Forecasting Mood in Bipolar Disorder From Smartphone Self-assessments : Hierarchical Bayesian Approach ', JMIR mHealth and uHealth, vol. 8, no. 4, e15028 . https://doi.org/10.2196/15028
- Publication Year :
- 2020
- Publisher :
- JMIR Publications, 2020.
-
Abstract
- Background Bipolar disorder is a prevalent mental health condition that is imposing significant burden on society. Accurate forecasting of symptom scores can be used to improve disease monitoring, enable early intervention, and eventually help prevent costly hospitalizations. Although several studies have examined the use of smartphone data to detect mood, only few studies deal with forecasting mood for one or more days. Objective This study aimed to examine the feasibility of forecasting daily subjective mood scores based on daily self-assessments collected from patients with bipolar disorder via a smartphone-based system in a randomized clinical trial. Methods We applied hierarchical Bayesian regression models, a multi-task learning method, to account for individual differences and forecast mood for up to seven days based on 15,975 smartphone self-assessments from 84 patients with bipolar disorder participating in a randomized clinical trial. We reported the results of two time-series cross-validation 1-day forecast experiments corresponding to two different real-world scenarios and compared the outcomes with commonly used baseline methods. We then applied the best model to evaluate a 7-day forecast. Results The best performing model used a history of 4 days of self-assessment to predict future mood scores with historical mood being the most important predictor variable. The proposed hierarchical Bayesian regression model outperformed pooled and separate models in a 1-day forecast time-series cross-validation experiment and achieved the predicted metrics, R2=0.51 and root mean squared error of 0.32, for mood scores on a scale of −3 to 3. When increasing the forecast horizon, forecast errors also increased and the forecast regressed toward the mean of data distribution. Conclusions Our proposed method can forecast mood for several days with low error compared with common baseline methods. The applicability of a mood forecast in the clinical treatment of bipolar disorder has also been discussed.
- Subjects :
- Mean squared error
Bipolar disorder
Early medical intervention
mood
Bayesian probability
Bayesian analysis
Health Informatics
Digital phenotyping
forecasting
Information technology
law.invention
03 medical and health sciences
0302 clinical medicine
Randomized controlled trial
SDG 3 - Good Health and Well-being
law
Statistics
Machine learning
Mood
medicine
Humans
digital phenotyping
health care economics and organizations
early medical intervention
bipolar disorder
Original Paper
Bayes Theorem
medicine.disease
T58.5-58.64
Mental health
030227 psychiatry
Affect
machine learning
Scale (social sciences)
Smartphone
Public aspects of medicine
RA1-1270
Psychology
Bayesian linear regression
030217 neurology & neurosurgery
Forecasting
Subjects
Details
- Language :
- English
- ISSN :
- 22915222
- Volume :
- 8
- Issue :
- 4
- Database :
- OpenAIRE
- Journal :
- JMIR mHealth and uHealth
- Accession number :
- edsair.doi.dedup.....e50e2465ff8588ecac24a450a4f3abab