Bayesian Networks for Mood Prediction Using Unobtrusive Ecological Momentary Assessments

Rebolledo, Margarita; Eiben, A. E.; Bartz-Beielstein, Thomas

doi:10.1007/978-3-030-72699-7_24

Cited by 3 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The majority of articles (59%) used smartphone sensing to infer mental health conditions. While six articles examined overall mental health [ 13 , 24 , 25 , 26 , 27 , 68 ], other studies examined specific factors such as mood [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 69 , 70 , 71 , 72 , 73 , 74 , 75 ] and stress [ 76 , 77 , 78 ]. Additionally, studies also examined specific mental health conditions such as depression [ 7 , 8 , 67 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 ], schizophrenia [ 90 , 91 , 92 , 93 ], and bipolar disorder [ 94 , 95 ].…”

Section: Resultsmentioning

confidence: 99%

“…Several studies have utilized smartphones to remotely collect data from users and gain health insights from these data. Studies have explored how smartphone-based sensing can be used to infer mood [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ], physical activity [ 3 , 11 , 22 , 23 ], and mental health [ 13 , 24 , 25 , 26 , 27 ], and also to track the effects of chronic health conditions [ 28 , 29 , 30 , 31 ]. Moreover, software frameworks have also been developed to help researchers use smartphone sensing to conduct their own studies, such as AWARE [ 32 ], Beiwe [ 33 ], and EARS [ 34 ] to name a few.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review

Kulkarni

Kirkham

McNaney

2022

Sensors

View full text Add to dashboard Cite

Recent years have seen significant advances in the sensing capabilities of smartphones, enabling them to collect rich contextual information such as location, device usage, and human activity at a given point in time. Combined with widespread user adoption and the ability to gather user data remotely, smartphone-based sensing has become an appealing choice for health research. Numerous studies over the years have demonstrated the promise of using smartphone-based sensing to monitor a range of health conditions, particularly mental health conditions. However, as research is progressing to develop the predictive capabilities of smartphones, it becomes even more crucial to fully understand the capabilities and limitations of using this technology, given its potential impact on human health. To this end, this paper presents a narrative review of smartphone-sensing literature from the past 5 years, to highlight the opportunities and challenges of this approach in healthcare. It provides an overview of the type of health conditions studied, the types of data collected, tools used, and the challenges encountered in using smartphones for healthcare studies, which aims to serve as a guide for researchers wishing to embark on similar research in the future. Our findings highlight the predominance of mental health studies, discuss the opportunities of using standardized sensing approaches and machine-learning advancements, and present the trends of smartphone sensing in healthcare over the years.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review

Kulkarni

Kirkham

McNaney

2022

Sensors

View full text Add to dashboard Cite

show abstract

Bayesian Networks for Prescreening in Depression: Algorithm Development and Validation

Maekawa,

Grua,

Nakamura

et al. 2024

JMIR Ment Health

View full text Add to dashboard Cite

Background Identifying individuals with depressive symptomatology (DS) promptly and effectively is of paramount importance for providing timely treatment. Machine learning models have shown promise in this area; however, studies often fall short in demonstrating the practical benefits of using these models and fail to provide tangible real-world applications. Objective This study aims to establish a novel methodology for identifying individuals likely to exhibit DS, identify the most influential features in a more explainable way via probabilistic measures, and propose tools that can be used in real-world applications. Methods The study used 3 data sets: PROACTIVE, the Brazilian National Health Survey (Pesquisa Nacional de Saúde [PNS]) 2013, and PNS 2019, comprising sociodemographic and health-related features. A Bayesian network was used for feature selection. Selected features were then used to train machine learning models to predict DS, operationalized as a score of ≥10 on the 9-item Patient Health Questionnaire. The study also analyzed the impact of varying sensitivity rates on the reduction of screening interviews compared to a random approach. Results The methodology allows the users to make an informed trade-off among sensitivity, specificity, and a reduction in the number of interviews. At the thresholds of 0.444, 0.412, and 0.472, determined by maximizing the Youden index, the models achieved sensitivities of 0.717, 0.741, and 0.718, and specificities of 0.644, 0.737, and 0.766 for PROACTIVE, PNS 2013, and PNS 2019, respectively. The area under the receiver operating characteristic curve was 0.736, 0.801, and 0.809 for these 3 data sets, respectively. For the PROACTIVE data set, the most influential features identified were postural balance, shortness of breath, and how old people feel they are. In the PNS 2013 data set, the features were the ability to do usual activities, chest pain, sleep problems, and chronic back problems. The PNS 2019 data set shared 3 of the most influential features with the PNS 2013 data set. However, the difference was the replacement of chronic back problems with verbal abuse. It is important to note that the features contained in the PNS data sets differ from those found in the PROACTIVE data set. An empirical analysis demonstrated that using the proposed model led to a potential reduction in screening interviews of up to 52% while maintaining a sensitivity of 0.80. Conclusions This study developed a novel methodology for identifying individuals with DS, demonstrating the utility of using Bayesian networks to identify the most significant features. Moreover, this approach has the potential to substantially reduce the number of screening interviews while maintaining high sensitivity, thereby facilitating improved early identification and intervention strategies for individuals experiencing DS.

show abstract

Bayesian Networks for Prescreening in Depression: Algorithm Development and Validation (Preprint)

Maekawa,

Grua,

Nakamura

et al. 2023

Preprint

View full text Add to dashboard Cite

BACKGROUND Identifying individuals with depressive symptomatology (DS) promptly and effectively is of paramount importance for providing timely treatment. Machine learning models have shown promise in this area, yet studies often fall short in demonstrating the practical benefits of utilizing these models and fail to provide tangible real-world applications. OBJECTIVE The objectives of this study were: 1) to establish a novel methodology for identifying individuals likely to exhibit DS; 2) to identify the most influential features in a more explainable way via probabilistic measures; 3) to propose tools that can be used in real-world applications. METHODS Three datasets were utilized in this study: the PROACTIVE dataset, along with the Brazilian National Health Survey (PNS) datasets from 2013 and 2019, comprising socio-demographic and health-related features. A Bayesian Network was used for feature selection. Selected features were then employed to train machine learning models to predict DS, operationalized as a score of 10 or higher on the 9-item Patient Health Questionnaire (PHQ-9). Furthermore, an analysis was conducted to evaluate the influence of different sensitivities on the reduction of screening process time achieved through the utilization of the model compared with a random approach. RESULTS With a threshold of 0.5, the methodology achieved a sensitivity of 0.640, 0.659, and 0.694, and an area under the receiver operating characteristic curve (AUC) of 0.736, 0.801, and 0.809, for PROACTIVE, PNS 2013, and PNS 2019, respectively. For the PROACTIVE dataset, the most influential features identified were postural balance, shortness of breath, and how old people feel they are. In the PNS 2013 dataset, the features were: the ability to do usual activities, chest pain, sleep problems, and chronic back problems. The PNS 2019 dataset shared three of the most influential features with the PNS 2013 dataset. However, the difference was the replacement of chronic back problems with verbal abuse. It is important to note that the features contained in the PNS datasets differ from those found in the PROACTIVE dataset. An empirical analysis demonstrated that utilizing the proposed model led to a reduction in screening time of up to 52% while maintaining a sensitivity of 0.80. CONCLUSIONS This study developed a novel methodology for identifying individuals with DS by demonstrating the practical benefits of employing Bayesian networks to identify the most significant features to be used in a machine learning model for the prediction of DS in three general health and socio-economic datasets. Moreover, simulations indicated that the utilization of this approach has the potential to substantially reduce the time required for identifying people with DS while maintaining a high sensitivity. These findings pave the way for improved early identification and intervention strategies for individuals experiencing depressive symptomatology.

show abstract

Bayesian Networks for Mood Prediction Using Unobtrusive Ecological Momentary Assessments

Cited by 3 publications

References 18 publications

Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review

Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review

Bayesian Networks for Prescreening in Depression: Algorithm Development and Validation

Bayesian Networks for Prescreening in Depression: Algorithm Development and Validation (Preprint)

Contact Info

Product

Resources

About