Instituto de Engenharia de Sistemas e Computadores, Investigação e Desenvolvimento em Lisboa

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Publications

IPOscore: an interactive web-based platform for postoperative surgical complications analysis and prediction in the oncology domain

Publication . Mochão, Hugo; Gonçalves, Daniel; Alexandre, Leonardo; Castro, Carolina; Valério, Duarte; Barahona, Pedro; Moreira-Gonçalves, Daniel; Costa, Paulo M.; Henriques, Rui; Santos, Lúcio L.; Costa, Rafael S.

Background: The performance of traditional risk score systems to predict (post)-operative outcomes is limited. This weakness reduces confidence in its use to support clinical risk mitigation decisions. However, the rapid growth of health data in the last years offers principles to deal with some of these limitations. In this regard, the data allows the extraction of relevant information for both patients stratification and the rigorous identification of associated risk factors. The patients can then be targeted to specific preoperative optimization programs, thus contributing to the reduction of associated morbidity and mortality. Objectives: The main goal of this work is, therefore, to provide a clinical decision support system (CDSS) based on data-driven modeling methods for surgical risk prediction specific for cancer patients in Portugal. Results: The result is IPOscore, a single web-based platform aimed at being an innovative approach to assist clinical decision-making in the surgical oncology domain. This system includes a database to store/manage the clinical data collected in a structured format, data visualization and analysis tools, and predictive machine learning models to predict postoperative outcomes in cancer patients. IPOscore also includes a pattern mining module based on biclustering to assess the discriminative power of a pattern towards postsurgical outcomes. Additionally, a mobile application is provided to this end. Conclusions: The IPOscore platform is a valuable tool for surgical oncologists not only for clinical data management but also as a preventative and predictive healthcare system. Currently, this clinical support tool is being tested at the Portuguese Institute of Oncology (IPO-Porto), and can be accessed online at https://iposcore.org.

2022Journal article

Restricted access

TCox : correlation-based regularization applied to colorectal cancer survival data

Publication . Peixoto, Carolina; Lopes, Marta B.; Martins, Marta; Costa, Luis; Vinga, Susana

Colorectal cancer (CRC) is one of the leading causes of mortality and morbidity in the world. Being a heterogeneous disease, cancer therapy and prognosis represent a significant challenge to medical care. The molecular information improves the accuracy with which patients are classified and treated since similar pathologies may show different clinical outcomes and other responses to treatment. However, the high dimensionality of gene expression data makes the selection of novel genes a problematic task. We propose TCox, a novel penalization function for Cox models, which promotes the selection of genes that have distinct correlation patterns in normal vs. tumor tissues. We compare TCox to other regularized survival models, Elastic Net, HubCox, and OrphanCox. Gene expression and clinical data of CRC and normal (TCGA) patients are used for model evaluation. Each model is tested 100 times. Within a specific run, eighteen of the features selected by TCox are also selected by the other survival regression models tested, therefore undoubtedly being crucial players in the survival of colorectal cancer patients. Moreover, the TCox model exclusively selects genes able to categorize patients into significant risk groups. Our work demonstrates the ability of the proposed weighted regularizer TCox to disclose novel molecular drivers in CRC survival by accounting for correlation-based network information from both tumor and normal tissue. The results presented support the relevance of network information for biomarker identification in high-dimensional gene expression data and foster new directions for the development of network-based feature selection methods in precision oncology.

2020Journal article

Open access

Studying the resiliency of the anchoring bias to locus of control in visualization

Publication . Alves, Tomás; Velhinho, Ricardo; Henriques-Calado, Joana; Gonçalves, Daniel; Gama, Sandra

The anchoring effect is the over-reliance on an initial piece of information when making decisions. It is one of the most pervasive and robust biases. Recently, literature has focused on knowing how influential the anchoring effect is when applied to information visualization, with studies finding its reproducibility in the field. Despite the extensive literature surrounding the anchoring effect’s robustness, there is still a need for research on which individual differences make people more susceptible. We explore how Locus of Control influences visualization’s ubiquitous and resilient anchoring effect. Locus of Control differentiates individuals who believe their life depends on their behavior or actions from those who blame outside factors such as destiny or luck for their life’s outcomes. We focus on the relationship between Locus of Control and the anchoring effect by exposing subjects to an anchor and analyzing their interaction with a complex visualization. Our results show that the anchoring strategies primed individuals and suggest that the Locus of Control plays a role in the susceptibility to the anchoring effect.

2023-11-24Journal article

Open access

Learning prognostic models using a mixture of biclustering and triclustering: predicting the need for non-invasive ventilation in amyotrophic lateral sclerosis

Publication . Soares, Diogo F.; Henriques, Rui; Gromicho, Marta; Carvalho, Mamede; Madeira, Sara C.

Longitudinal cohort studies to study disease progression generally combine temporal features produced under periodic assessments (clinical follow-up) with static features associated with single-time assessments, genetic, psychophysiological, and demographic profiles. Subspace clustering, including biclustering and triclustering stances, enables the discovery of local and discriminative patterns from such multidimensional cohort data. These patterns, highly interpretable, are relevant to identifying groups of patients with similar traits or progression patterns. Despite their potential, their use for improving predictive tasks in clinical domains remains unexplored. In this work, we propose to learn predictive models from static and temporal data using discriminative patterns, obtained via biclustering and triclustering, as features within a state-of-the-art classifier, thus enhancing model interpretation. triCluster is extended to find time-contiguous triclusters in temporal data (temporal patterns) and a biclustering algorithm to discover coherent patterns in static data. The transformed data space, composed of bicluster and tricluster features, capture local and cross-variable associations with discriminative power, yielding unique statistical properties of interest. As a case study, we applied our methodology to follow-up data from Portuguese patients with Amyotrophic Lateral Sclerosis (ALS) to predict the need for non-invasive ventilation (NIV) since the last appointment. The results showed that, in general, our methodology outperformed baseline results using the original features. Furthermore, the bicluster/tricluster-based patterns used by the classifier can be used by clinicians to understand the models by highlighting relevant prognostic patterns.

2022Journal article

Open access

Self-Regulated Learning and Working Memory Determine Problem-Solving Accuracy in Math

Publication . Ferreira, Paula; Ferreira, Aristides I.; Veiga Simão, Ana; Prada, Rui; Paulino, Paula; Rodrigues, Ricardo

This study aims to understand the role of self-regulated learning (SRL) and its different processes in the relationship between working memory (WM) and problem-solving accuracy in math in primary school children. A sample of 269 primary school children (Mage=8.84, SD =0.81, 58% boys) participated in this study. Tasks were used as intervention resources to assess children’s WM (i.e., reading and computation span tasks), SRL (i.e., a digital game), and performance (i.e., the performance in the game, as well as a traditional math problem). Through structural equation modeling, results revealed that WM predicted children’s SRL and their problem-solving accuracy in math, such that those with higher capability for temporary storage attained better accuracy. Accordingly, children’s SRL explained the relationship between WM capacity and problem-solving accuracy in math; such that the indirect effect of WM capacity through SRL was lower on problem-solving accuracy in math. Results indicated that the planning phase was a greater indicator of students’ SRL in problem-solving accuracy in math. These results highlight the importance of SRL competencies in explaining children’s performance in problem-solving in math.

2022Journal article

Restricted access