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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Orange3 Data Mining Toolkit. https://orange.biolab.si
Andersena, S.A., Borasioc, G.D., de Carvalho, M., Chioe, A., Van Dammef, P., Hardimang, O., Kolleweh, K., Morrisoni, K.E., et al.: Efns guidelines on the clinical management of amyotrophic lateral sclerosis (MALS)-revised report of an EFNS task force. Eur. J. Neurol. 19, 360–375 (2011)
Bourke, S.C., Tomlinson, M., Williams, T.L., Bullock, R.E., Shaw, P.J., Gibson, G.J.: Effects of non-invasive ventilation on survival and quality of life in patients with amyotrophic lateral sclerosis: a randomised controlled trial. Lancet Neurol. 5(2), 140–147 (2006)
Carreiro, A.V., Amaral, P.M., Pinto, S., Tomás, P., de Carvalho, M., Madeira, S.C.: Prognostic models based on patient snapshots and time windows: predicting disease progression to assisted ventilation in amyotrophic lateral sclerosis. J. Biomed. Inform. 58, 133–144 (2015)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Chiò, A., Logroscino, G., Traynor, B., Collins, J., Simeone, J., Goldstein, L., White, L.: Global epidemiology of amyotrophic lateral sclerosis: a systematic review of the published literature. Neuroepidemiology 41(2), 118–130 (2013)
Conde, B., Winck, J.C., Azevedo, L.F.: Estimating amyotrophic lateral sclerosis and motor neuron disease prevalence in Portugal using a pharmaco-epidemiological approach and a bayesian multiparameter evidence synthesis model. Neuroepidemiology 53(1–2), 73–83 (2019)
ENCALS: ALS functional rating scale revised (ALS-FRS-R). version (May 2015)
Heffernan, C., Jenkinson, C., Holmes, T., Macleod, H., Kinnear, W., Oliver, D., Leigh, N., Ampong, M.: Management of respiration in mnd/als patients: an evidence based review. Amyotroph. Lateral Scler. 7(1), 5–15 (2006)
Henriques, R., Madeira, S.C.: Triclustering algorithms for three-dimensional data analysis: a comprehensive survey. ACM Comput. Surv. 51(5), 95 (2019)
Matos, J.: Biclustering electronic health records to unravel disease presentation patterns. MSc Thesis (2019)
Pires, S., Gromicho, M., Pinto, S., Carvalho, M., Madeira, S.C.: Predicting non-invasive ventilation in ALS patients using stratified disease progression groups. In: 2018 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 748–757. IEEE (2018)
Zhao, L., Zaki, M.J.: Tricluster: an effective algorithm for mining coherent clusters in 3D microarray data, pp. 694–705 (2005)
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-54568-0_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-54567-3
Online ISBN: 978-3-030-54568-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)