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Dynamic Outcomes-Based Clustering of Disease Trajectory in Mechanically Ventilated Patients

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Artificial Intelligence for Personalized Medicine (W3PHAI 2023)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1106))

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Abstract

The advancement of Electronic Health Records (EHRs) and machine learning have enabled a data-driven and personalised approach to healthcare. One step in this direction is to uncover patient sub-types with similar disease trajectories in a heterogeneous population. This is especially important in the context of mechanical ventilation in intensive care, where mortality is high and there is no consensus on treatment. In this work, we present a new approach to clustering mechanical ventilation episodes, using a multi-task combination of supervised, self-supervised and unsupervised learning techniques. Our dynamic clustering assignment is explicitly guided to reflect the phenotype, trajectory and outcomes of the patient. Experimentation on a real-world dataset is encouraging, and we hope that we could someday translate this into actionable insights in guiding future clinical research.

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Notes

  1. 1.

    A tracheostomy is a procedure designed for long term mechanical ventilation of a patient.

  2. 2.

    Preliminary experiments revealed that k-means were more likely to produce small clusters which lay far away from the rest of the data, because it is more affected by outliers. This made the clustering process less reliable and reproducible.

  3. 3.

    There is a 5 h gap between these predictions, therefore this time difference needs to be removed from the first prediction.

  4. 4.

    This is because younger patients can mask a problem by compensating deceptively well, until they reach a point where the homeostatic mechanisms can no longer cope.

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Acknowledgements

The authors would like to thank Petar Veličković, Sophie Xhonneux, Stephanie Hyland and Mihaela van der Schaar for helpful discussions and advice. We also thank the Armstrong Fund, the Frank Edward Elmore Fund, and the School of Clinical Medicine at the University of Cambridge for their generous funding.

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Authors and Affiliations

  1. University of Cambridge, Cambridge, UK

    Emma Rocheteau, Pietro Liò & Ari Ercole

  2. University of Oxford, Oxford, UK

    Ioana Bica

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  1. Emma Rocheteau
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  2. Ioana Bica
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Correspondence to Emma Rocheteau .

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Editors and Affiliations

  1. Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, The University of Tennessee Health Science Center–Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics, Memphis, TN, USA

    Arash Shaban-Nejad

  2. School of Nursing, University of Minnesota, Minneapolis, MN, USA

    Martin Michalowski

  3. Altos Labs - Bay Area Institute of Science, Redwood City, CA, USA

    Simone Bianco

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Rocheteau, E., Bica, I., Liò, P., Ercole, A. (2023). Dynamic Outcomes-Based Clustering of Disease Trajectory in Mechanically Ventilated Patients. In: Shaban-Nejad, A., Michalowski, M., Bianco, S. (eds) Artificial Intelligence for Personalized Medicine. W3PHAI 2023. Studies in Computational Intelligence, vol 1106. Springer, Cham. https://doi.org/10.1007/978-3-031-36938-4_6

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