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Machine learning in healthcare - a system’s perspective
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Halland Hospital, Region Halland, Halmstad, Sweden.ORCID iD: 0000-0001-5688-0156
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-7796-5201
2019 (English)In: Proceedings of the ACM SIGKDD Workshop on Epidemiology meets Data Mining and Knowledge Discovery (epiDAMIK) / [ed] B. Aditya Prakash, Anil Vullikanti, Shweta Bansal, Adam Sadelik, Arlington, 2019, p. 14-17Conference paper, Published paper (Refereed)
Abstract [en]

A consequence of the fragmented and siloed healthcare landscape is that patient care (and data) is split along multitude of different facilities and computer systems and enabling interoperability between these systems is hard. The lack interoperability not only hinders continuity of care and burdens providers, but also hinders effective application of Machine Learning (ML) algorithms. Thus, most current ML algorithms, designed to understand patient care and facilitate clinical decision-support, are trained on limited datasets. This approach is analogous to the Newtonian paradigm of Reductionism in which a system is broken down into elementary components and a description of the whole is formed by understanding those components individually. A key limitation of the reductionist approach is that it ignores the component-component interactions and dynamics within the system which are often of prime significance in understanding the overall behaviour of complex adaptive systems (CAS). Healthcare is a CAS.

Though the application of ML on health data have shown incremental improvements for clinical decision support, ML has a much a broader potential to restructure care delivery as a whole and maximize care value. However, this ML potential remains largely untapped: primarily due to functional limitations of Electronic Health Records (EHR) and the inability to see the healthcare system as a whole. This viewpoint (i) articulates the healthcare as a complex system which has a biological and an organizational perspective, (ii) motivates with examples, the need of a system's approach when addressing healthcare challenges via ML and, (iii) emphasizes to unleash EHR functionality - while duly respecting all ethical and legal concerns - to reap full benefits of ML.

Place, publisher, year, edition, pages
Arlington, 2019. p. 14-17
Keywords [en]
Machine learning, Healthcare complexity, System's thinking, Electronic health records
National Category
Other Medical Engineering
Identifiers
URN: urn:nbn:se:hh:diva-40395OAI: oai:DiVA.org:hh-40395DiVA, id: diva2:1342677
Conference
25th ACM SIGKDD Workshop on Epidemiology meets Data Mining and Knowledge Discovery (epiDAMIK '19), Anchorage, Alaska, United States, August 5, 2019
Available from: 2019-08-14 Created: 2019-08-14 Last updated: 2019-08-14Bibliographically approved

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Epidamik proceedings(20463 kB)16 downloads
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Type fulltextMimetype application/pdf

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Ashfaq, AwaisNowaczyk, Sławomir

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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