Cookies Policy
The website need some cookies and similar means to function. If you permit us, we will use those means to collect data on your visits for aggregated statistics to improve our service. Find out More
Accept Reject
  • Menu
About
Download Photo HD

About

He is a senior researcher at INESC TEC since 1998. He is assistant coordinator of the Centre for Information Systems and Computer Graphics (CSIG)

Current research interests include platforms and methods for collaborative research, privacy-preserving distributed computation, the semantic sensor Web (IoT) and Big Data processing.

From October 1996 to December 1997, he was an associate member of CERN - European Laboratory for High Energy Physics, IT Division/Web Office.

His research is applied in two major areas: Personalized Health Research (PHR) and Earth and Ocean Observation Science (EOOS).

The PHR area currently subdivides in: a) personalized Internet-based treatments; and b) human data storage, privacy-preserving processing and controlled FAIR data sharing. In this area, he participates in several European projects, such as ICT4Depression (FP7), E-COMPARED (FP7), STOP Depression (EEA Grant), iCare4Depression (FCT), RECAP Preterm (H2020), EUCAN-Connect (H2020) and iReceptor Plus (H2020). In these projects, he often undertakes the role of responsible for the system's architecture, platform implementation, or technical coordinator.

In the EOOS area he participates in the implementation of the RAIA Observatory (Interreg projects RAIA, RAIA.co, RAIA TEC, MarRisk and RADAR ON RAIA), SeaBioData(EEA Grant), MELOA (H2020) and C4G which is the Portuguese node of EPOS (H2020 EPOS-SP).

Interest
Topics
Details

Details

  • Name

    Artur Rocha
  • Cluster

    Computer Science
  • Role

    Assistant Centre Coordinator
  • Since

    02nd February 1998
018
Publications

2020

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

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

Publication
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

Authors
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;

Publication
Frontiers Big Data

Abstract

2019

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

Authors
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;

Publication
JMIR Mental Health

Abstract

2019

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

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

Publication
ERCIM News

Abstract

2018

Predicting short term mood developments among depressed patients using adherence and ecological momentary assessment data

Authors
Mikus, A; Hoogendoorn, M; Rocha, A; Gama, J; Ruwaard, J; Riper, H;

Publication
Internet Interventions

Abstract

Supervised
thesis

2016

Platform for monitoring and treat depression

Author
José Pedro Alves Ornelas

Institution
UP-FCUP