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Towards Integrative Machine Learning and Knowledge Extraction pp 150–169Cite as

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10344))

Abstract

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.

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Author information

Authors and Affiliations

  1. University of Notre Dame, Notre Dame, IN, USA

    Keith Feldman, Louis Faust, Xian Wu, Chao Huang & Nitesh V. Chawla

  2. Indiana Biosciences Research Institute, Indianapolis, IN, 46202, USA

    Nitesh V. Chawla

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  1. Keith Feldman
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  2. Louis Faust
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  3. Xian Wu
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  4. Chao Huang
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  5. Nitesh V. Chawla
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Corresponding author

Correspondence to Nitesh V. Chawla .

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Editors and Affiliations

  1. Medical University Graz, Graz, Austria

    Andreas Holzinger

  2. University of Alberta, Edmonton, Alberta, Canada

    Randy Goebel

  3. Bologna University, Bologna, Italy

    Massimo Ferri

  4. Coventry University, Coventry, United Kingdom

    Vasile Palade

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Feldman, K., Faust, L., Wu, X., Huang, C., Chawla, N.V. (2017). Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline. In: Holzinger, A., Goebel, R., Ferri, M., Palade, V. (eds) Towards Integrative Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science(), vol 10344. Springer, Cham. https://doi.org/10.1007/978-3-319-69775-8_9

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  • DOI: https://doi.org/10.1007/978-3-319-69775-8_9

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