AI4Life - Artificial intelligence as an instrument for the reduction of infant and youth mortality: understanding its determinants and predicting outcomes

The digital transformation of the Public Administration is imperative so that the necessary mechanisms can be developed to reduce the number of years of life lost and to increase the quality of life of the population. The main objective of this project is to leverage existing information in public administration databases and others in order to support decision-makers regarding the best response to emerging diseases, better adaptation of public health intervention programs and improve the capacity of the health systems in the future.


iCare4U - Decision support system for personalized medicine in ICUs

This project proposes a decision support system based on intelligent modelling and patient sub-group analysis, to provide personalized therapy for critically ill patients. It is hypothesized that the “Collective Experience” from large clinical databases, where clinical decisions are linked with patient outcomes, can be used to identify specific patient sub-groups and build personalized therapy models towards a new era of personalized medicine, allowing the improvement of patient outcomes in the ICU.


SusCity – Urban data driven models for creative and resourceful urban transitions

This project is focused on developing and integrating new tools and services to increase urban resource efficiency with minimum environmental impacts while contributing to promote economic development and preserving the actual levels of reliability. Dispersion of agents producing data at urban level (city council, utilities, state agencies and institutes, corporations) lead to mixed results in applying indicators in different environments and sometimes with little gain in urban performance, namely in terms of sustainability.


CancerSys - Multiscale modeling for personalized therapy of bone metastasis


Tasks: | Data based modeling | Feature selection |

Cancer is one of the oldest diseases known to man, being one of the leading causes of death worldwide. Modeling this complex pathology and contributing to therapy optimization using systems and control methods constitutes a great challenge in systems medicine, whose results are expected to have high social and economic impact. The goal of this project is to develop methods for medical treatment optimization, by using the integration of all patient information and features, thus providing quantitative guidelines for personalized bone metastasis treatment, minimizing its toxicity and side effects.


ORQUESTRA - Distributed Optimization and Control of Large Scale Water Delivery Systems


Tasks: | Distributed optimization |

The ORCHESTRA project focus on the research and development of new distributed fault tolerant coordinated optimal MPC controllers based on distributed optimization algorithms, aiming at contributing to the improvement of the water delivery hydro agricultural infrastructure located in the extreme southeast of Alentejo, a region in Portugal where the problem of water scarcity is known to be more severe.


Project with SONAE, comércio e serviços


Tasks: | Feature extraction | Data based modeling |

The work in collaboration with the company Sonae, comércio e serviços, focused on two different problems: Sales forecasting in retail. This problem was approached using soft computing techniques for three different forecasting periods: stationary, stationary with disturbances and non-stationary. Feature extraction was one of the key factors for this work as some of the features have a strong effect on sales and had to be extracted/designed for the problem. Alarm system for retail supply chains.


Combining Soft Computing Techniques and Statistical Methods to Improve Data Analysis Solutions

The main objective of the Action is to strengthen the dialogue between the statistics and soft computing research communities in order to cross-pollinate both fields and generate mutual improvement activities. Soft computing, as an engineering science, and statistics, as a branch of mathematics, emphasize different aspects of data analysis. Soft computing focuses on obtaining working solutions quickly, accepting approximations and unconventional approaches. Its strength lies in its flexibility to create models that suit the needs arising in applications (context of discovery, model generation).


IC4U - Decision support system for preventing ICU readmissions

Patients readmitted to an intensive care unit (ICU) during the same hospitalization have an increased risk of death, length of stay, and are associated with higher costs. Previous studies have demonstrated overall readmission rates of 4-14%, of which nearly a third can be attributed to premature discharge from the ICU. In this project, a prediction algorithm based on intelligent modeling techniques is proposed, considering numerical and textual data. Both sources of information can be complimentary and will be used in a decision support system framework.


ID2Care - Systems redesign to improve the survival of critically ill patients using data based modeling

In the past, healthcare practitioners believed that patient outcomes were dependent almost exclusively on: training, capability and skill of individual clinician; patient characteristics; specifics of the illness or procedure being performed. New insights from the study of complex work environments suggest that clinical outcomes may be strongly influenced by the structure or design of the system in which care is delivered. In this project, we will examine data collected in ICUs of large, hospital-based health systems and consider how to reduce two key adverse outcomes among such patients: death from sepsis, and agitation leading to self-extubation (accidental removal of a breathing tube by a patient, which leads to certain death, if not corrected very quickly).


PEERChain - Design and Planning of Energy Efficient and Resilient Supply Chains


Tasks: | Ant colony optimization |

Supply Chain Management is a necessary cornerstone for any organization that wants to compete in the current global economy. The need of seriously exploring the concept of global resilient sustainable supply chains within a collaborative perspective is seen as a goal to improve companies’ revenue growth and costumer’s recognition. This project aims to develop novel models to help design and planning global supply chains, to make them more environmental friendly and resilient to disruptions across a long time horizon.