A survey on big data-driven digital phenotyping of mental health

Liang, Yunji; Zheng, Xiaolong; Zeng, Daniel

doi:10.1016/j.inffus.2019.04.001

Cited by 121 publications

(67 citation statements)

References 139 publications

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“…Document-level analysis, for example, classifies a whole document as generally positive, negative or neutral, whereas sentence-level analysis does the same for a sentence [29]. This process helps to reveal the intensity of the writers' opinions on a topic of interest (e.g., [30][31][32][33]). Chen, Zhu, Kifer, and Lee [34], for example, used sentiment analysis to compare the opinions of Republican and Democratic senators with respect to a variety of topics, focusing on similarities and dissimilarities between the two groups' opinions.…”

Section: Sentiment Analysismentioning

confidence: 99%

Sexual harassment in academe is underreported, especially by students in the life and physical sciences

Aguilar

Baek

2020

PLoS ONE

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What factors predict the underreporting of sexual harassment in academe? We used logistic regression and sentiment analysis to examine 2,343 reports of sexual harassment involving members of university communities. Results indicate students were 1.6 times likely to not report their experiences when compared to faculty. Respondents in the life and physical sciences were 1.7 times more likely to not report their experiences when compared to respondents in other disciplines. Men represented 90% of the reported perpetrators of sexual harassment. Analysis of respondents' written accounts show variation of overall sentiment based on discipline, student type, and the type of institution attended, particularly with regard to mental health. Our results suggest that institutional and departmental barriers driven by power asymmetries play a large role in the underreporting sexual harassment among students-especially those in STEM disciplines.

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Section: Sentiment Analysismentioning

confidence: 99%

Sexual harassment in academe is underreported, especially by students in the life and physical sciences

Aguilar

Baek

2020

PLoS ONE

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“…This would probably require establishing a huge cohort of patients, something that current projects are far from being able to build. Although, there are many portable systems and mobile applications available to characterize, analyze, and monitor mental health conditions, they have not been clinically evaluated on patients on a large scale for their accuracy and effectiveness (Liang et al, 2019). Finally, it seems the term digital phenotyping engages the expectations of some researchers more than the research potential resulting from the consideration of smartphone data for clinical observation.…”

Section: From Phenotype To Case Formulationmentioning

confidence: 99%

“…Among its benefits is the promise that ontology-based knowledge modeling encapsulates large data sets for digital cataloguing. Recently, ontology has been widely adopted to integrate and analyze large amounts of heterogeneous data in the field of public health and facilitate improved medical diagnosis and treatment (Liang et al, 2019).…”

Section: From Phenotype To Case Formulationmentioning

confidence: 99%

The Digital Phenotyping Project: A Psychoanalytical and Network Theory Perspective

Potier

2020

Front. Psychol.

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“…Currently, ubiquitous devices (e.g., smartphones, smartwatches, smart bands, and fitness bracelets) represent a promising means of mitigating those limitations [ 13 ]. The pervasive nature of these devices combined with a large amount of behavioral data from their sensors make ubiquitous computing a natural option to incorporate new system proposals for monitoring social behaviors related to mental health.…”

Section: Introductionmentioning

confidence: 99%

“…The term Digital Phenotyping refers to “moment-by-moment quantification of the individual-level human phenotype in-situ using data from smartphones and other personal digital devices” [ 14 ]. The goal of digital phenotyping is to learn and monitor patterns overtime that characterize behaviors of individuals (e.g., physical activities performed, their social interactions and mobility), based on context data derived from mobile, wearable, and Internet of Things (IoT) computing devices [ 13 ]. By using this concept, it is possible to create computational mechanisms able to perform continuous and discrete detection of individuals’ social behaviors [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Recognizing Context-Aware Human Sociability Patterns Using Pervasive Monitoring for Supporting Mental Health Professionals

Moura

Teles

Endler

et al. 2020

Sensors

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Traditionally, mental health specialists monitor their patients’ social behavior by applying subjective self-report questionnaires in face-to-face meetings. Usually, the application of the self-report questionnaire is limited by cognitive biases (e.g., memory bias and social desirability). As an alternative, we present a solution to detect context-aware sociability patterns and behavioral changes based on social situations inferred from ubiquitous device data. This solution does not focus on the diagnosis of mental states, but works on identifying situations of interest to specialized professionals. The proposed solution consists of an algorithm based on frequent pattern mining and complex event processing to detect periods of the day in which the individual usually socializes. Social routine recognition is performed under different context conditions to differentiate abnormal social behaviors from the variation of usual social habits. The proposed solution also can detect abnormal behavior and routine changes. This solution uses fuzzy logic to model the knowledge of the mental health specialist necessary to identify the occurrence of behavioral change. Evaluation results show that the prediction performance of the identified context-aware sociability patterns has strong positive relation (Pearson’s correlation coefficient >70%) with individuals’ social routine. Finally, the evaluation conducted recognized that the proposed solution leading to the identification of abnormal social behaviors and social routine changes consistently.

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A survey on big data-driven digital phenotyping of mental health

Cited by 121 publications

References 139 publications

Sexual harassment in academe is underreported, especially by students in the life and physical sciences

Sexual harassment in academe is underreported, especially by students in the life and physical sciences

The Digital Phenotyping Project: A Psychoanalytical and Network Theory Perspective

Recognizing Context-Aware Human Sociability Patterns Using Pervasive Monitoring for Supporting Mental Health Professionals

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