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Towards Triclustering-Based Classification of Three-Way Clinical Data: A Case Study on Predicting Non-invasive Ventilation in ALS

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1240)

Abstract

The importance to learn disease progression patterns from longitudinal clinical data and use them effectively to improve prognosis, triggers the need for new approaches for three-way data analysis. In this context, triclustering has been widely researched for its potential in biomedical problems, showing promising results in the discovery of putative biological modules, patient profiles, and disease progression patterns. In this work, we propose a triclustering-based approach for three-way data classification, resulting from a combination of triclustering with random forests, and use it to predict the need for non-invasive ventilation in ALS patients. We analyse ALSFRS-R functional scores together with respiratory function tests collected from patient follow-up. The results are promising, enabling to understand the potential of triclustering and pinpointing improvements towards an effective triclustering-based classifier for clinical domains, taking advantage of the benefits of exploring disease progression patterns mined from three-way clinical data.

Keywords

  • Triclustering
  • Three-dimensional data
  • Three-way clinical data
  • Amyotrophic lateral sclerosis
  • Prognostic prediction

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Acknowledgements

This work was partially supported by FCT funding to Neuroclinomics2 (PTDC/EEI-SII/1937/2014) and iCare4U (LISBOA-01-0145-FEDER-031474 + PTDC/EME-SIS/31474/2017) research projects, and LASIGE Research Unit (UIDB/00408/2020).

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Correspondence to Diogo Soares .

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Soares, D., Henriques, R., Gromicho, M., Pinto, S., de Carvalho, M., Madeira, S.C. (2021). Towards Triclustering-Based Classification of Three-Way Clinical Data: A Case Study on Predicting Non-invasive Ventilation in ALS. In: Panuccio, G., Rocha, M., Fdez-Riverola, F., Mohamad, M., Casado-Vara, R. (eds) Practical Applications of Computational Biology & Bioinformatics, 14th International Conference (PACBB 2020). PACBB 2020. Advances in Intelligent Systems and Computing, vol 1240. Springer, Cham. https://doi.org/10.1007/978-3-030-54568-0_12

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