A computational model for identifying behavioral patterns in people with neuropsychiatric disorders

Authors

Keywords:

Context-Aware Computing, Context History, Similarity Analysis, Ubiquitous Computing

Abstract

Neuropsychiatric disorders represent a great economic and social burden, whose estimates range from 14% to 32% of the global burden of diseases. One in four people suffer from mental disorders during their lifetime and the estimates of people suffering from depression and some type of neuropsychiatric disorder are about 322 million and 420 million, respectively. Therefore, this paper presents Eigenroutines, a model based on the hypothesis that the ubiquitous availability of mobile devices and the massive usage of these devices have created an opportunity of identifying behavior, supporting the management of neuropsychiatric disorders. The model is also capable of building psychiatric epidemiological profiles, using context histories. The model evaluation occurred in three steps, where the first confirmed that the model has the ability to identify and distinguish neuropsychiatric disorders, analyzing context histories from a group of 55 individuals, composed of 23 people with unipolar or bipolar depression and 32 healthy people. The model presented an accuracy of 78% for classifying the routine of an individual in depressed or healthy. The second step evaluated the extraction of epidemiological profiles based on context histories from 1066 individuals stored by the platform. Finally, the last step evaluated the limitations of the model regarding temporal complexity and scalability.

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Author Biographies

Paulo César Büttenbender, University of Vale do Rio dos Sinos

Graduado em Análise e Desenvolvimento de Sistemas pela Unisinos em 2011, concluiu o curso recebendo a premiação de aluno destaque 2011 pela Sociedade Brasileira de Computação. Concluiu mestrado e doutorado em Computação Aplicada pela Unisinos em 2013 e 2020. Trabalhou de 2005 a 2010 como desenvolvedor de software no Grupo Meta, e a partir de 2010 como engenheiro de software no SAP Labs Latin America na arquitetura e projeto de uma solução para o mercado de Agronegócio desenvolvido em conjunto com times dos EUA, India e Brasil. Desde então foi responsável técnico pela 3ª versão do SAP Agricultural Contract Management liderando um time de 12 desenvolvedores e responsável técnico e funcional do SAP Commodity Management option for Deal Capture liderando um time de 40 desenvolvedores. Trabalhando com indústrias do setor de Óleo e Gás e de Metais e Mineração e clientes localizados nos EUA, Arábia Saudita, Inglaterra, Canadá, Colômbia e Suíça. Atualmente é engenheiro de software no SAP Data Warehouse Cloud.

Eduardo Gonçalvez de Azevedo Neto, University of Vale do Rio dos Sinos

Estudante de Ciência da Computação pela universidade Unisinos. Bolsista da PIBIC do CNPq, trabalhou em um projeto de mapeamento sistemático com o professor Jorge Luis Victória Barbosa e professor João Elison da Rosa Tavares.

Wesllei Felipe Heckler, University of Vale do Rio dos Sinos

Possui graduação em Ciência da Computação pela Universidade Feevale. É mestrando no Programa de Pós-Graduação em Computação Aplicada (PPGCA) da Universidade do Vale do Rio dos Sinos (Unisinos). Suas áreas de interesse são Data Science, Machine Learning e Tecnologia aplicada à Saúde.

Jorge Luis Victória Barbosa, University of Vale do Rio dos Sinos

Possui graduação em Tecnologia em Processamento de Dados (1990) e Engenharia Elétrica (1991) pela Universidade Católica de Pelotas (UCPel). Ele obteve especialização em Engenharia de Software (UCPel, 1993) e concluiu mestrado e doutorado em ciência da computação na Universidade Federal do Rio Grande do Sul (UFRGS, 1996 e 2001). Em 2011 realizou pós-doutorado na Sungkyunkwan University (SKKU, Suwon, Coréia do Sul). Em 2020 realizou pós-doutorado na University of California Irvine (UCI, Irvine, USA) através de uma bolsa do Programa CAPES/PRINT (professor visitante no Exterior Sênior). Jorge é Professor Titular II na Universidade do Vale do Rio dos Sinos (Unisinos). Ele atua professor no Programa de Pós-graduação em Computação Aplicada (PPGCA) e no Mestrado Profissional em Engenharia Elétrica. Jorge coordena o Laboratório de Computação Móvel (Mobilab) e atua como Bolsista de Produtividade em Desenvolvimento Tecnológico e Extensão Inovadora (bolsa DT - atualmente no Nível 1C) do Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq).

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Published

2021-12-21

How to Cite

Büttenbender, P. C., Neto, E. G. de A., Heckler, W. F., & Barbosa, J. L. V. (2021). A computational model for identifying behavioral patterns in people with neuropsychiatric disorders. IEEE Latin America Transactions, 20(4), 582–589. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/5731

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Vol. 20 No. 4 (2022): Ordinary Issue

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