The DOI matches its Crossref record; 60 of the 61 references it cites were found in a registry or an index.
DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection
Open accessAbstract
The increasing use of electronic forms of communication presents new opportunities in the study of mental health, including the ability to investigate the manifestations of psychiatric diseases unobtrusively and in the setting of patients' daily lives. A pilot study to explore the possible connections between bipolar affective disorder and mobile phone usage was conducted. In this study, participants were provided a mobile phone to use as their primary phone. This phone was loaded with a custom keyboard that collected metadata consisting of keypress entry time and accelerometer movement. Individual character data with the exceptions of the backspace key and space bar were not collected due to privacy concerns. We propose an end-to-end deep architecture based on late fusion, named DeepMood, to model the multi-view metadata for the prediction of mood scores. Experimental results show that 90.31% prediction accuracy on the depression score can be achieved based on session-level mobile phone typing dynamics which is typically less than one minute. It demonstrates the feasibility of using mobile phone metadata to infer mood disturbance and severity.
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“Others focused on detecting depression by tracking medication, sleep patterns and actions [20], location [11] or keypress acceleration [165].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [165]
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Cites
Diagnostic and Statistical Manual of Mental Disorders
American Psychiatric Association (2013) · doi:10.1176/appi.books.9780890425596 · Cited by 116,369
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · cited 2×
“y, DeepMood provides a late fusion framework. It first models each view of the time series data separately using Gated Recurrent Unit (GRU) [12], a simplified version of Long Short-Term Memory (LSTM) [24]. It then fuses the output of the GRU from each view. As the GRU extracts a latent feature representation out of ea…”
MethodXGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569 · cited 1×
“• XGB: The implementation of a tree boosting system from XGBoost6 [10] is used.”
MethodLearning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545 · cited 11×
“A typical GRU is formulated as: rk = sigmoid(Wr xk + Ur hk−1) zk = sigmoid(Wzxk + Uzhk−1) h̃k = tanh(Wxk + U(rk ⊙ hk−1)) hk = zk ⊙ hk−1 + (1 − zk ) ⊙ h̃k (3) where ⊙ is the element-wise multiplication operator, a reset gate rk allows the GRU to forget the previously computed state hk−1, and an update gate zk balances…”
BackgroundMethodInfluentialKessler, R. C., Berglund, P., Demler, O., Jin, R., Merikangas, K. R., & Walters, E. E. (2005) · Archives of General Psychiatry · doi:10.1001/archpsyc.62.6.593 · Cited by 20,391 · cited 1×
“There are 7 participants with bipolar I disorder that involves periods of severe mood episodes from mania to depression, 5 participants with bipolar II disorder which is a milder form of mood elevation, involving milder episodes of hypomania that alternate with periods of severe depression, and 8 participants with no…”
BackgroundNeural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213 · cited 1×
“Inspired by the great success of deep RNNs in the applications of other sequence tasks, including speech recognition [20] and natural language processing [4, 34], in this work, we propose a deep architecture to model complex event sequences of mobile phone typing dynamics.”
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008 · cited 1×
“The GRU has been attracting great attentions since it overcomes the vanishing gradient problem in traditional RNNs and is more efficient than the LSTM in some tasks [14].”
MethodTensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893 · cited 1×
“Inspired by the great success of deep RNNs in the applications of other sequence tasks, including speech recognition [20] and natural language processing [4, 34], in this work, we propose a deep architecture to model complex event sequences of mobile phone typing dynamics.”
A Rating Scale for Mania: Reliability, Validity and Sensitivity
Young, R. C., Biggs, J. T., Ziegler, V. E., & Meyer, D. A. (1978) · British Journal of Psychiatry · doi:10.1192/bjp.133.5.429 · Cited by 8,744 · cited 7×
“Based on the Kolmogorov-Smirnov test, for 98.06% in HDRS and 99.52% in YMRS of the distribution pairs, we can reject the null hypothesis that two samples are drawn from the same distribution with significance level α = 0.01.”
MethodInfluential
Research works printed but not linked: 1
- Tijmen Tieleman and Geoffrey Hinton 2012 . Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude . COURSERA: Neural Networks for Machine Learning , Vol. 4 , 2 (2012). Tijmen Tieleman and Geoffrey Hinton 2012. Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning, Vol. 4, 2 (2012).
Cited by
Sorted by citations
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
Deep Learning in Mobile and Wireless Networking: A Survey
Zhang, C., Patras, P., & Haddadi, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2904897 · Cited by 1,913
Self-citationMachine learning in mental health: a scoping review of methods and applications
Shatte, A. B. R., Hutchinson, D. M., & Teague, S. J. (2019) · Psychological Medicine · doi:10.1017/s0033291719000151 · Cited by 1,047
Thieme, A., Belgrave, D., & Doherty, G. (2020) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3398069 · Cited by 406
“Here, success of newly developed models is primarily reported through performance measures of the ‘accuracy of the (best) classifier’ or ‘robustness of clustering’ [2, 4, 6, 23, 27, 33, 44, 45, 50, 57, 61, 62, 63, 64, 67, 86, 89, 112, 122, 128, 133, 134, 135, 136, 139, 141, 144, 145, 152, 153, 155, 165, 168, 176, 177,…”
BackgroundMethodInfluentialDeep neural networks in psychiatry
Durstewitz, D., Koppe, G., & Meyer‐Lindenberg, A. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0365-9 · Cited by 297
“They have also been used to monitor PD based on motor movements [115, 116], or to detect depressive states based on typing dynamics [90].”
BackgroundMethod
Metadata
- Authors
- Cao, B., Zheng, L., Zhang, C., Yu, P. S., Piscitello, A., Zulueta, J., Ajilore, O., Ryan, K., & Leow, A. D.
- Affiliations
- University of Illinois Chicago (US)
- University of Illinoisat Chicago, Chicago, IL, USA
- University of Michigan (US)
- ORCID
- Venue
- Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
- Publisher
- ACM
- Published
- 2017-08-04
- Pages
- 747-755
- OpenAlex
- W2744939564
- Cited by
- 145
- References
- 61
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 11 · 2023: 10 · 2022: 21 · 2021: 28 · 2020: 23 · 2019: 20 · 2018: 17
- FWCI
- 18.87
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (IIS-1526499 and CNS-1626432)
- National Natural Science Foundation of China (61672313)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- mood prediction, mobile phone metadata, bipolar disorder, depression severity, decision fusion, multi-view fusion, mental health monitoring
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