Proceeding of: NeurIPS 2019 Workshop: Learning with Temporal Point Processes (part of the 33rd Conference on Neural Information Processing Systems), Vancouver, December 14, 2019 (Visit: https://nips.cc/Conferences/2019/Schedule?showEvent=13166 and https://sites.google.com/view/tpp-neurips-2019)This work introduces a novel method to assess the social activity maintained by psychiatric patients using information and communication technologies. In particular, we jointly model using point processes the e-social activity patterns from two heterogeneous sources: the usage of phone calls and social and communication apps. We propose a nonhomogeneous Poisson mixture model with periodic (circadian) intensity function using a truncated Fourier series expansion, which is inferred using a trust-region algorithm, and it is able 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 164 patients reveals that 25 patterns suffice to characterize their daily behavior.This work was supported by the Ministerio de Ciencia, Innovación y Universidades under grant TEC2017-92552-EXP (aMBITION), by the Ministerio de Ciencia, Innovación y Universidades, jointly with the European Commission (ERDF), under grants TEC2017-86921-C2-2-R (CAIMAN) and RTI2018-099655-BI00 (CLARA), and by The Comunidad de Madrid under grant Y2018/TCS-4705 (PRACTICO-CM)
