Assessment of e-Social Activity in Psychiatric Patients

This paper introduces a novel method to assess the social activity maintained by psychiatric patients using information and communication technologies. In particular, we model the daily usage patterns of phone calls and social and communication apps using point processes. We propose a novel nonhomogeneous Poisson process model with periodic (circadian) intensity function using a truncated Fourier series expansion, which is inferred using a trust-region algorithm. We also extend the model using a mixture of periodic intensity functions to cope with the different daily patterns of a person. The analysis of the usage of phone calls and social and communication apps of a cohort of 259 patients reveals common patterns shared among patients with relatively high homogeneity and differences among patient pathologies.

[1]  Jiaxun Chen,et al.  sppmix: Poisson point process modeling using normal mixture models , 2018, Comput. Stat..

[2]  Onno Boxma,et al.  On a generic class of lÉvy-driven vacation models , 2007 .

[3]  B. Svarstad,et al.  Using drug claims data to assess the relationship of medication adherence with hospitalization and costs. , 2001, Psychiatric services.

[4]  J. Angst,et al.  Cost of depression in Europe. , 2006, The journal of mental health policy and economics.

[5]  Susana Faria,et al.  Financial data modeling by Poisson mixture regression , 2013 .

[6]  Padhraic Smyth,et al.  Learning Time-Intensity Profiles of Human Activity using Non-Parametric Bayesian Models , 2006, NIPS.

[7]  Kathryn Moynihan Ramsey,et al.  Circadian rhythms, sleep, and metabolism. , 2011, The Journal of clinical investigation.

[8]  Andrés R. Masegosa,et al.  Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks , 2018, Int. J. Approx. Reason..

[9]  Prabhu Babu,et al.  A Unified Framework for Low Autocorrelation Sequence Design via Majorization–Minimization , 2017, IEEE Transactions on Signal Processing.

[10]  M. Kosinski,et al.  Computer-based personality judgments are more accurate than those made by humans , 2015, Proceedings of the National Academy of Sciences.

[11]  V. Feigin,et al.  The Global Burden of Mental, Neurological and Substance Use Disorders: An Analysis from the Global Burden of Disease Study 2010 , 2015, PloS one.

[12]  J. Magnus,et al.  Matrix Differential Calculus with Applications in Statistics and Econometrics , 1991 .

[13]  Jari Saramäki,et al.  Daily Rhythms in Mobile Telephone Communication , 2015, PloS one.

[14]  D. Mohr,et al.  Harnessing Context Sensing to Develop a Mobile Intervention for Depression , 2011, Journal of medical Internet research.

[15]  Yu Zhu,et al.  PM-Seq: Using Finite Poisson Mixture Models for RNA-Seq Data Analysis and Transcript Expression Level Quantification , 2013 .

[16]  Cliff Lampe,et al.  The Benefits of Facebook "Friends: " Social Capital and College Students' Use of Online Social Network Sites , 2007, J. Comput. Mediat. Commun..

[17]  Rui Wang,et al.  CrossCheck: toward passive sensing and detection of mental health changes in people with schizophrenia , 2016, UbiComp.

[18]  Thomas F. Coleman,et al.  An Interior Trust Region Approach for Nonlinear Minimization Subject to Bounds , 1993, SIAM J. Optim..

[19]  Akane Sano,et al.  Stress Recognition Using Wearable Sensors and Mobile Phones , 2013, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction.

[20]  Winston S. Buckley,et al.  Experience rating with Poisson mixtures , 2010, Annals of Actuarial Science.

[21]  N C Andreasen,et al.  Awareness of illness in schizophrenia and schizoaffective and mood disorders. , 1994, Archives of general psychiatry.

[22]  J. Sayers The world health report 2001 - Mental health: new understanding, new hope , 2001 .

[23]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[24]  Alex 'Sandy' Pentland,et al.  bandicoot: a Python Toolbox for Mobile Phone Metadata , 2016, J. Mach. Learn. Res..

[25]  New York Dover,et al.  ON THE CONVERGENCE PROPERTIES OF THE EM ALGORITHM , 1983 .

[26]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[27]  J. Magnus,et al.  Matrix differential calculus with applications to simple, Hadamard, and Kronecker products. , 1985 .

[28]  A. A. Sorokin,et al.  Human circadian rhythm synchronization by social timers: The role of motivation: IV. Individual features of the free-running 24-hour sleep-wake cycle under simulated conditions of vital activity , 2007, Human Physiology.

[29]  Nuno Vasconcelos,et al.  A Kullback-Leibler Divergence Based Kernel for SVM Classification in Multimedia Applications , 2003, NIPS.

[30]  Cécile Paris,et al.  We Feel: Mapping Emotion on Twitter , 2015, IEEE Journal of Biomedical and Health Informatics.

[31]  Vincent W. S. Tseng,et al.  CrossCheck: Integrating Self-Report, Behavioral Sensing, and Smartphone Use to Identify Digital Indicators of Psychotic Relapse , 2017, Psychiatric rehabilitation journal.

[32]  B. Frey,et al.  Transformation-Invariant Clustering Using the EM Algorithm , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[33]  Brian W. Powers,et al.  The digital phenotype , 2015, Nature Biotechnology.

[34]  Dror Ben-Zeev,et al.  Feasibility and acceptability of post-hospitalization ecological momentary assessment in patients with psychotic-spectrum disorders. , 2017, Comprehensive psychiatry.

[35]  L. Fejér Über trigonometrische Polynome. , 1916 .

[36]  Alex Pentland,et al.  Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits , 2014, ACM Multimedia.

[37]  M. Harding,et al.  A Poisson mixture model of discrete choice , 2011 .