Towards an explainable mortality prediction model

Jacob R. Epifano, Ravi P. Ramachandran, Sharad Patel, Ghulam Rasool

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Influence functions are analytical tools from robust statistics that can help interpret the decisions of black-box machine learning models. Influence functions can be used to attribute changes in the loss function due to small perturbations in the input features. The current work on using influence functions is limited to the features available before the last layer of deep neural networks (DNNs). We extend the influence function approximation to DNNs by computing gradients in an end-to-end manner and relate changes in the loss function to individual input features using an efficient algorithm. We propose an accurate mortality prediction neural network and show the effectiveness of extended influence functions on the eICU dataset. The features chosen by proposed extended influence functions were more like those selected by human experts than those chosen by other traditional methods.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020
    PublisherIEEE Computer Society
    ISBN (Electronic)9781728166629
    DOIs
    StatePublished - Sep 2020
    Event30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020 - Virtual, Espoo, Finland
    Duration: Sep 21 2020Sep 24 2020

    Publication series

    NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
    Volume2020-September
    ISSN (Print)2161-0363
    ISSN (Electronic)2161-0371

    Conference

    Conference30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020
    Country/TerritoryFinland
    CityVirtual, Espoo
    Period9/21/209/24/20

    All Science Journal Classification (ASJC) codes

    • Human-Computer Interaction
    • Signal Processing

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