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Sobre
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Sobre

Investigador sénior no INESC TEC desde 1998. É coordenador adjunto do Centro de Sistemas de Informação e Computação Gráfica (CSIG).

Os seus atuais interesses de investigação incluem: plataformas e métodos para investigação colaborativa, computação distribuída para preservação de privacidade, redes semanticas de sensores (IoT) e processamento de Big Data.

De Outubro de 1996 a Dezembro de 1997, foi membro associao do CERN - Laboratório Europeu de Física de Partículas, Divisão de IT, Web Office.

A sua investigação aplica-se atualmente em duas área principais: Investigação em Saúde Personalizada (PHR) e Ciência da Observação da Terra e do Oceano (EOOS).

A área de PHR subdivide-se em: a) tratamentos personalizados baseados na Internet; e b) armazenamento de dados humanos, processamento com preservação de privacidade, e partilha controlada de dados FAIR. Nesta área participa em vários projetos como o ICT4Depression (FP7), E-COMPARED (FP7), STOP Depression (EEA Grant), iCare4Depression (FCT), RECAP Preterm (H2020), EUCAN-Connect (H2020) e iReceptor Plus (H2020). Nestes projetos assume frequentemente o papel de responsável pela arquitetura do sistema, implementação da plataforma ou coordenador técnico.

Na área de EOOS, participa na implementação do Observatório RAIA (projetos Interreg RAIA, RAIA.co, RAIA TEC, MarRisk e RADAR ON RAIA), SeaBioData (EEA Grant), MELOA (H2020) e C4G, que é o nó Português da infraestrutura EPOS (H2020 EPOS-SP).

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Artur Rocha
  • Cluster

    Informática
  • Cargo

    Coordenador Adjunto de Centro
  • Desde

    02 fevereiro 1998
022
Publicações

2020

Evaluation of a temporal causal model for predicting the mood of clients in an online therapy

Autores
Becker, D; Bremer, V; Funk, B; Hoogendoorn, M; Rocha, A; Riper, H;

Publicação
EVIDENCE-BASED MENTAL HEALTH

Abstract
Background Self-reported client assessments during online treatments enable the development of statistical models for the prediction of client improvement and symptom development. Evaluation of these models is mandatory to ensure their validity. Methods For this purpose, we suggest besides a model evaluation based on study data the use of a simulation analysis. The simulation analysis provides insight into the model performance and enables to analyse reasons for a low predictive accuracy. In this study, we evaluate a temporal causal model (TCM) and show that it does not provide reliable predictions of clients' future mood levels. Results Based on the simulation analysis we investigate the potential reasons for the low predictive performance, for example, noisy measurements and sampling frequency. We conclude that the analysed TCM in its current form is not sufficient to describe the underlying psychological processes. Conclusions The results demonstrate the importance of model evaluation and the benefit of a simulation analysis. The current manuscript provides practical guidance for conducting model evaluation including simulation analysis.

2020

The ADC API: A Web API for the Programmatic Query of the AIRR Data Commons

Autores
Christley, S; Aguiar, A; Blanck, G; Breden, F; Chan Bukhari, SA; Busse, CE; Jaglale, J; Harikrishnan, SL; Laserson, U; Peters, B; Rocha, A; Schramm, CA; Taylor, S; Vander Heiden, JA; Zimonja, B; Watson, CT; Corrie, B; Cowell, LG;

Publicação
Frontiers Big Data

Abstract

2020

Improving adherence to an online intervention for low mood with a virtual coach: study protocol of a pilot randomized controlled trial

Autores
Provoost, S; Kleiboer, A; Ornelas, J; Bosse, T; Ruwaard, J; Rocha, A; Cuijpers, P; Riper, H;

Publicação
TRIALS

Abstract
Background: Internet-based cognitive-behavioral therapy (iCBT) is more effective when it is guided by human support than when it is unguided. This may be attributable to higher adherence rates that result from a positive effect of the accompanying support on motivation and on engagement with the intervention. This protocol presents the design of a pilot randomized controlled trial that aims to start bridging the gap between guided and unguided interventions. It will test an intervention that includes automated support delivered by an embodied conversational agent (ECA) in the form of a virtual coach. Methods/design: The study will employ a pilot two-armed randomized controlled trial design. The primary outcomes of the trial will be (1) the effectiveness of iCBT, as supported by a virtual coach, in terms of improved intervention adherence in comparison with unguided iCBT, and (2) the feasibility of a future, larger-scale trial in terms of recruitment, acceptability, and sample size calculation. Secondary aims will be to assess the virtual coach's effect on motivation, users' perceptions of the virtual coach, and general feasibility of the intervention as supported by a virtual coach. We will recruitN = 70 participants from the general population who wish to learn how they can improve their mood by using Moodbuster Lite, a 4-week cognitive-behavioral therapy course. Candidates with symptoms of moderate to severe depression will be excluded from study participation. Included participants will be randomized in a 1:1 ratio to either (1) Moodbuster Lite with automated support delivered by a virtual coach or (2) Moodbuster Lite without automated support. Assessments will be taken at baseline and post-study 4 weeks later. Discussion: The study will assess the preliminary effectiveness of a virtual coach in improving adherence and will determine the feasibility of a larger-scale RCT. It could represent a significant step in bridging the gap between guided and unguided iCBT interventions.

2019

Unraveling the Black Box: Exploring Usage Patterns of a Blended Treatment for Depression in a Multicenter Study

Autores
Kemmeren, LL; van Schaik, DJF; Smit, JH; Ruwaard, J; Rocha, A; Henriques, MR; Ebert, DD; Titzler, I; Hazo, JB; Dorsey, M; Zukowska, K; Riper, H;

Publicação
JMIR Mental Health

Abstract

2019

Empowering Distributed Analysis Across Federated Cohort Data Repositories Adhering to FAIR Principles

Autores
Rocha, A; Ornelas, JP; Lopes, JC; Camacho, R;

Publicação
ERCIM News

Abstract

Teses
supervisionadas

2016

Platform for monitoring and treat depression

Autor
José Pedro Alves Ornelas

Instituição
UP-FCUP