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A Big-Data Variational Bayesian Framework for Supporting the Prediction of Functional Outcomes in Wake-Up Stroke Patients

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Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12249))

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Abstract

Prognosis in Wake-up ischemic stroke (WUS) is important for guiding treatment and rehabilitation strategies, in order to improve recovery and minimize disability. For this reason, there is growing interest on models to predict functional recovery after acute ischemic events in order to personalize the therapeutic intervention and improve the final functional outcome. The aim of this preliminary study is to evaluate the possibility to predict a good functional outcome, in terms of modified Rankin Scale (mRS ≤ 2), in thrombolysis treated WUS patients by Bayesian analysis of clinical, demographic and neuroimaging data at admission. The study was conducted on 54 thrombolysis treated WUS patients. The Variational Bayesian logistic regression with Automatic Relevance Determination (VB-ARD) was used to produce model and select informative features to predict a good functional outcome (mRS ≤ 2) at discharge. The produced model showed moderately high 10 × 5-fold cross validation accuracy of 71% to predict outcome. The sparse model highlighted the relevance of NIHSS at admission, age, TACI stroke syndrome, ASPECTs, ischemic core CT Perfusion volume, hypertension and diabetes mellitus. In conclusion, in this preliminary study we assess the possibility to model the prognosis in thrombolysis treated WUS patients by using VB ARD. The identified features related to initial neurological deficit, history of diabetes and hypertension, together with necrotic tissue relate ASPECT and CTP core volume neuroimaging features, were able to predict outcome with moderately high accuracy.

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Acknowledgements

This study was partially supported by Master in Clinical Engineering, University of Trieste. A. Miladinović is supported by the European Social Fund (ESF) and Autonomous Region of Friuli Venezia Giulia (FVG).

Author information

Authors and Affiliations

  1. Department of Engineering and Architecture, University of Trieste, Trieste, Italy

    Miloš Ajčević, Aleksandar Miladinović, Giulia Silveri & Agostino Accardo

  2. Clinical Unit of Neurology, Department of Medicine, Surgery and Health Sciences, University Hospital of Trieste, University of Trieste, Trieste, Italy

    Giovanni Furlanis, Tommaso Cilotto, Alex Buoite Stella, Paola Caruso, Marcello Naccarato & Paolo Manganotti

  3. Radiology Unit, Department of Medicine, Surgery and Health Sciences, University Hospital of Trieste, University of Trieste, Trieste, Italy

    Maja Ukmar

  4. iDEA Lab, University of Calabria, Rende, Italy

    Alfredo Cuzzocrea

Authors
  1. Miloš Ajčević
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  2. Aleksandar Miladinović
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Corresponding author

Correspondence to Alfredo Cuzzocrea .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

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Ajčević, M. et al. (2020). A Big-Data Variational Bayesian Framework for Supporting the Prediction of Functional Outcomes in Wake-Up Stroke Patients. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12249. Springer, Cham. https://doi.org/10.1007/978-3-030-58799-4_71

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  • DOI: https://doi.org/10.1007/978-3-030-58799-4_71

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