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A Practical Approach to Machine Learning for Clinical Decision Support

Projects at Lucile Packard Children’s Hospital Stanford in Partnership with Stanford Engineering

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Health Care Systems Engineering (ICHCSE 2017)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 210))

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Abstract

Machine learning has produced effective clinical decision support tools. The impact of such work is limited by the difficulty of implementing such tools outside the institution where they were designed. The recent wide-spread adoption of Electronic Medical Record systems (EMRs) makes possible the development and application of tools across institutions. We describe three machine learning projects to develop generalizable, EMR-based clinical decision support tools at the cardiac care units of Lucile Packard Children’s Hospital Stanford: false alarm suppression, detection of critical events, and automated identification and detection of drug-drug interactions. These projects utilize flexible statistical and deep learning frameworks to enable automated, patient-specific care. We focus on the practical challenges of implementing such methodology and describe our progress on producing tools useful for our institution.

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Acknowledgements

Our work on these projects would not have been possible without the motivation, and feedback received from LPCH physicians and staff. In particular, we thank Christopher Almond, David Rosenthal, Shannon Feehan, Andrew Shin, and John Dykes.

Author information

Authors and Affiliations

  1. Stanford University Department of Electrical Engineering, Stanford University Department of Management Science and Engineering, and Lucile Packard Children’s Hospital Stanford, Palo Alto, CA, USA

    Daniel Miller, David Scheinker & Nicholas Bambos

Authors
  1. Daniel Miller
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  2. David Scheinker
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  3. Nicholas Bambos
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Corresponding authors

Correspondence to Daniel Miller , David Scheinker or Nicholas Bambos .

Editor information

Editors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, University of Florence, Florence, Italy

    Paola Cappanera

  2. Department of Industrial and Systems Engineering, University of Wisconsin–Madison, Madison, Wisconsin, USA

    Jingshan Li

  3. Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy

    Andrea Matta

  4. Laboratoire Génie Industriel, École Centrale Paris, Châtenay-Malabry, France

    Evren Sahin

  5. OG Productie en Logistiek Leuven, Katholieke Universiteit Leuven, Leuven, Belgium

    Nico J. Vandaele

  6. Dipartimento di Ingegneria Industriale, University of Florence, Florence, Italy

    Filippo Visintin

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Miller, D., Scheinker, D., Bambos, N. (2017). A Practical Approach to Machine Learning for Clinical Decision Support. In: Cappanera, P., Li, J., Matta, A., Sahin, E., Vandaele, N., Visintin, F. (eds) Health Care Systems Engineering. ICHCSE 2017. Springer Proceedings in Mathematics & Statistics, vol 210. Springer, Cham. https://doi.org/10.1007/978-3-319-66146-9_10

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