Mixtures of heterogeneous Poisson processes for the assessment of e-social activity in mental health

Autor: Bonilla Escribano, Pablo, Ramírez García, David, Artés Rodríguez, Antonio
Přispěvatelé: Ministerio de Ciencia, Innovación y Universidades (España), Comunidad de Madrid
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Zdroj: e-Archivo: Repositorio Institucional de la Universidad Carlos III de Madrid
Universidad Carlos III de Madrid (UC3M)
e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid
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Popis: 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).
Databáze: OpenAIRE
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