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Conference Paper: Beyond volume: The impact of complex healthcare data on the machine learning pipeline

TitleBeyond volume: The impact of complex healthcare data on the machine learning pipeline
Authors
KeywordsHealthcare informatics
Knowledge discovery
Machine learning
Issue Date2017
PublisherSpringer.
Citation
Banff International Research Station (BIRS) Workshop: Advances in Interactive Knowledge Discovery and Data Mining in Complex and Big Data Sets (15w2181), Banff, Canada, 24-26 July 2015. In Towards Integrative Machine Learning and Knowledge Extraction: BIRS Workshop, Banff, AB, Canada, July 24-26, 2015, Revised Selected Papers, p. 150-169. Cham: Springer, 2017 How to Cite?
AbstractFrom 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.
Persistent Identifierhttp://hdl.handle.net/10722/308737
ISBN
ISSN
2023 SCImago Journal Rankings: 0.606
ISI Accession Number ID
Series/Report no.Lecture Notes in Computer Science ; 10344
Lecture Notes in Artificial Intelligence ; 10344

 

DC FieldValueLanguage
dc.contributor.authorFeldman, Keith-
dc.contributor.authorFaust, Louis-
dc.contributor.authorWu, Xian-
dc.contributor.authorHuang, Chao-
dc.contributor.authorChawla, Nitesh V.-
dc.date.accessioned2021-12-08T07:50:01Z-
dc.date.available2021-12-08T07:50:01Z-
dc.date.issued2017-
dc.identifier.citationBanff International Research Station (BIRS) Workshop: Advances in Interactive Knowledge Discovery and Data Mining in Complex and Big Data Sets (15w2181), Banff, Canada, 24-26 July 2015. In Towards Integrative Machine Learning and Knowledge Extraction: BIRS Workshop, Banff, AB, Canada, July 24-26, 2015, Revised Selected Papers, p. 150-169. Cham: Springer, 2017-
dc.identifier.isbn9783319697741-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/308737-
dc.description.abstractFrom 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.-
dc.languageeng-
dc.publisherSpringer.-
dc.relation.ispartofTowards Integrative Machine Learning and Knowledge Extraction: BIRS Workshop, Banff, AB, Canada, July 24-26, 2015, Revised Selected Papers-
dc.relation.ispartofseriesLecture Notes in Computer Science ; 10344-
dc.relation.ispartofseriesLecture Notes in Artificial Intelligence ; 10344-
dc.subjectHealthcare informatics-
dc.subjectKnowledge discovery-
dc.subjectMachine learning-
dc.titleBeyond volume: The impact of complex healthcare data on the machine learning pipeline-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-319-69775-8_9-
dc.identifier.scopuseid_2-s2.0-85033554932-
dc.identifier.spage150-
dc.identifier.epage169-
dc.identifier.eissn1611-3349-
dc.identifier.isiWOS:000542952300009-
dc.publisher.placeCham-

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