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We address the personalized policy learning problem using longitudinal mobile health application usage data.
Qian, T., Klasnja, P., and Murphy, S. A. (2019) · 1902
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The conjugate gradient method and trust regions in large scale optimization
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A cautionary note on inference for marginal regression models with longitudinal data and general correlated response data
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Nash, S. G. (2000) · 2000
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Analysis of longitudinal data
Diggle, P., Heagerty, P., Liang, K.-Y., and Zeger, S. (2002) · 2002
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Optimal dynamic treatment regimes
Murphy, S. A. (2003) · 2003
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Yuan, M. and Lin, Y. (2006) · 2006
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Adherence in internet interventions for anxiety and depression
Christmann, C. A., Hoffmann, A., and Bleser, G. (2009) · 2009
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Mobile interventions for severe mental illness: design and preliminary data from three approaches
Depp, C. A., Mausbach, B., Granholm, E., Cardenas, V., Ben-Zeev, D., Patterson, T. L., Lebowitz, B. D., and Jeste, D. V. (2010) · 2010
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Ecological momentary interventions: incorporating mobile technology into psychosocial and health behaviour treatments
Heron, K. E. and Smyth, J. M. (2010) · 2010
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Falling asleep with angry birds, facebook and kindle - a large scale study on mobile application usage
Bohmer, M., Hecht, B., Schoning, J., Kruger, A., and Bauer, G. (2011) · 2011
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The n-of-1 clinical trial: the ultimate strategy for individualizing medicine
Lillie, E., Patay, B., Diamant, J., Issell, B., Topol, E., and Schork, N. (2011) · 2011
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Performance guarantees for individualized treatment rules
Qian, M. and Murphy, S. A. (2011) · 2011
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Health behavior models in the age of mobile interventions: are our theories up to the task?
Riley, W. T., Rivera, D. E., Atienza, A. A., Nilsen, W., Allison, S. M., and Mermelstein, R. (2011) · 2011
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A robust method for estimating optimal treatment regimes
Zhang, B., Tsiatis, A. A., Laber, E. B., and Davidian, M. (2012) · 2012
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Estimating individualized treatment rules using outcome weighted learning
Zhao, Y., Zeng, D., Rush, A. J., and Kosorok, M. R. (2012) · 2012
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New statistical learning methods for estimating optimal dynamic treatment regimes
Zhao, Y., Zeng, D., Laber, E. B., and Kosorok, M. R. (2015) · 2015
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Personalize treatment for longitudinal data using unspecified random-effects model
Cho, H., Wang, P., and Qu, A. (2017) · 2017
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Joint selection in mixed models using regularized pql
Hui, F. K., Müller, S., and Welsh, A. (2017) · 2017
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An actor-critic contextual bandit algorithm for personalized interventions using mobile devices
Lei, H., Tewari, A., and Murphy, S. (2017) · 2017
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Intellicare: an eclectic, skills-based app suite for the treatment of depression and anxiety
Mohr, D. C., Tomasino, K. N., Lattie, E. G., Palac, H. L., Kwasny, M. J., Weingardt, K., Karr, C. J., Kaiser, S. M., Rossom, R. C., Bardsley, L. R., et al. (2017) · 2017
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Continuous evaluation of evolving behavioral intervention technologies
Mohr, D. C., Cheung, K., Schueller, S. M., Brown, C. H., and Duan, N. (2013) · 2013
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Fitting linear mixed-effects models using lme4
Bates, D., Mächler, M., Bolker, B., and Walker, S. (2014) · 2014
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Factors related to sustained use of a free mobile app for dietary self-monitoring with photography and peer feedback: retrospective cohort study
Helander, E., Kaipainen, K., Korhonen, I., and Wansink, B. (2014) · 2014
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The rise of consumer health wearables: promises and barriers
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Assessing time-varying causal effect moderation in mobile health
Boruvka, A., Almirall, D., Witkiewitz, K., and Murphy, S. A. (2018) · 2018
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Evaluation of a recommender app for apps for the treatment of depression and anxiety: an analysis of longitudinal user engagement
Cheung, K., Ling, W., Kar, r. C. J., Weingardt, K., Schueller, S. M., and Mohr, D. C. (2018) · 2018
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Constructing dynamic treatment regimes over indefinite time horizons
Ertefaie, A. and Strawderman, R. L. (2018) · 2018
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Estimating dynamic treatment regimes in mobile health using v-learning
Luckett, D. J., Laber, E. B., Kahkoska, A. R., Maahs, D. M., Mayer-Davis, E., and Kosorok, M. R. (2019) · 2019
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