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- Triclustering three-way temporal and heterogeneous dataPublication . Soares, Diogo F.; Madeira, Sara; Henriques, RuiTriclustering, targeting the discovery of coherent subspaces within three-way data, is becoming increasingly relevant in data science, especially for pattern discovery and knowledge acquisition from complex datasets in the biomedical field. This technique can reveal hidden patterns such as putative regulatory modules, disease progression profiles, and individuals with coherent behaviors. When applied to labeled data, triclustering aids in class differentiation and supports real-world decision-making. However, learning from 3W biomedical data is typically challenged by the rich temporal and heterogeneous nature, having mixed-type features and different structure compositions. In response to these challenges, this thesis establishes the foundations for pattern-centric 3W data analysis, focusing on triclustering for temporal and heterogeneous three-way data, targeting both descriptive and predictive tasks. In this context, this thesis includes six major contributions. It provides a literature review and comparative study of current triclustering algorithms for temporal data, highlighting the strengths and weaknesses of existing methods. It presents new tools to support the development and assessment of pattern discovery approaches in descriptive and predictive contexts, including a new data generator capable of creating heterogeneous three-way datasets with annotated triclustering solutions and benchmark datasets for comparative evaluation. It proposes a novel approach to capture time-contiguous triclusters, enhancing the search for temporal coherence. It introduces a new triclustering approach able to handle heterogeneous data by applying sequential pattern mining principles to identify relevant patterns and derive triclusters capturing temporal data dynamics. Additionally, it presents a new method for learning pattern-centric predictors. Finally, it proposes an extension and integration of principles for learning from static and temporal data structures. The developed methods were comprehensively validated in concrete real-world clinical scenarios, showing promising results concerning two progressive diseases. They were used to predict clinically relevant endpoints and identify disease-specific progression patterns, supporting medical decisions and identifying significant patient profiles.
- Learning prognostic models using a mixture of biclustering and triclustering: predicting the need for non-invasive ventilation in amyotrophic lateral sclerosisPublication . 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.
- Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: a systematic reviewPublication . Tavazzi, Erica; Longato, Enrico; Vettoretti, Martina; Aidos, Helena; Trescato, Isotta; Roversi, Chiara; Martins, Andreia S.; Castanho, Eduardo N.; Branco, Ruben; Soares, Diogo F.; Guazzo, Alessandro; Birolo, Giovanni; Pala, Daniele; Bosoni, Pietro; Chiò, Adriano; Manera, Umberto; Carvalho, Mamede; André e Silva Miranda, Bruno; Gromicho, Marta; Alves, Inês; Bellazzi, Riccardo; Dagliati, Arianna; Fariselli, Piero; Madeira, Sara C.; Di Camillo, BarbaraBackground: Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disorder characterised by the progressive loss of motor neurons in the brain and spinal cord. The fact that ALS's disease course is highly heterogeneous, and its determinants not fully known, combined with ALS's relatively low prevalence, renders the successful application of artificial intelligence (AI) techniques particularly arduous. Objective: This systematic review aims at identifying areas of agreement and unanswered questions regarding two notable applications of AI in ALS, namely the automatic, data-driven stratification of patients according to their phenotype, and the prediction of ALS progression. Differently from previous works, this review is focused on the methodological landscape of AI in ALS. Methods: We conducted a systematic search of the Scopus and PubMed databases, looking for studies on data-driven stratification methods based on unsupervised techniques resulting in (A) automatic group discovery or (B) a transformation of the feature space allowing patient subgroups to be identified; and for studies on internally or externally validated methods for the prediction of ALS progression. We described the selected studies according to the following characteristics, when applicable: variables used, methodology, splitting criteria and number of groups, prediction outcomes, validation schemes, and metrics. Results: Of the starting 1604 unique reports (2837 combined hits between Scopus and PubMed), 239 were selected for thorough screening, leading to the inclusion of 15 studies on patient stratification, 28 on prediction of ALS progression, and 6 on both stratification and prediction. In terms of variables used, most stratification and prediction studies included demographics and features derived from the ALSFRS or ALSFRS-R scores, which were also the main prediction targets. The most represented stratification methods were K-means, and hierarchical and expectation-maximisation clustering; while random forests, logistic regression, the Cox proportional hazard model, and various flavours of deep learning were the most widely used prediction methods. Predictive model validation was, albeit unexpectedly, quite rarely performed in absolute terms (leading to the exclusion of 78 eligible studies), with the overwhelming majority of included studies resorting to internal validation only. Conclusion: This systematic review highlighted a general agreement in terms of input variable selection for both stratification and prediction of ALS progression, and in terms of prediction targets. A striking lack of validated models emerged, as well as a general difficulty in reproducing many published studies, mainly due to the absence of the corresponding parameter lists. While deep learning seems promising for prediction applications, its superiority with respect to traditional methods has not been established; there is, instead, ample room for its application in the subfield of patient stratification. Finally, an open question remains on the role of new environmental and behavioural variables collected via novel, real-time sensors.