The paper “Triclustering-based classification of longitudinal data for prognostic prediction: targeting relevant clinical endpoints in amyotrophic lateral sclerosis”, authored by LASIGE’s PhD student Diogo F. Soares and Integrated Researcher Sara C. Madeira, has been published in Scientific Reports a top-ranked journal (h5-index 206; Scimago Q1). The paper is co-authored by Rui Henriques (INESC-ID and Instituto Superior Técnico – Universidade de Lisboa), Marta Gromicho, and Mamede de Carvalho (Instituto de Medicina Molecular and Faculdade de Medicina – Universidade de Lisboa).

This work proposes a new class of explainable prognostic models for longitudinal data classification using triclusters. A new temporally constrained triclustering algorithm, termed TCtriCluster, is proposed to comprehensively find informative temporal patterns common to a subset of patients in a subset of features (triclusters), and use them as discriminative features within a state-of-the-art classifier with guarantees of interpretability. The proposed approach further enhances prediction with the potentialities of model explainability by revealing clinically relevant disease progression patterns underlying prognostics, and describing features used for classification.

The proposed methodology is used in the Amyotrophic Lateral Sclerosis (ALS) Portuguese cohort (N = 1321), providing the first comprehensive assessment of the prognostic limits of five notable clinical endpoints: need for non-invasive ventilation (NIV); need for an auxiliary communication device; need for percutaneous endoscopic gastrostomy (PEG); need for a caregiver; and need for a wheelchair. Triclustering-based predictors outperform state-of-the-art alternatives, being able to predict the need for auxiliary communication device (within 180 days) and the need for PEG (within 90 days) with an AUC above 90%. The approach was validated in clinical practice, supporting healthcare professionals in understanding the link between the highly heterogeneous patterns of ALS disease progression and the prognosis.

This work is the part of the research carried out in the framework of the EU-funded Brainteaser project and FCT-funded AIpALS project. The paper is available here.