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Machine learning in healthcare - a system’s perspective
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab). Halland Hospital, Region Halland, Halmstad, Sweden.ORCID-id: 0000-0001-5688-0156
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0002-7796-5201
2019 (Engelska)Ingår i: 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, s. 14-17Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
Arlington, 2019. s. 14-17
Nyckelord [en]
Machine learning, Healthcare complexity, System's thinking, Electronic health records
Nationell ämneskategori
Annan medicinteknik
Identifikatorer
URN: urn:nbn:se:hh:diva-40395OAI: oai:DiVA.org:hh-40395DiVA, id: diva2:1342677
Konferens
25th ACM SIGKDD Workshop on Epidemiology meets Data Mining and Knowledge Discovery (epiDAMIK '19), Anchorage, Alaska, United States, August 5, 2019
Tillgänglig från: 2019-08-14 Skapad: 2019-08-14 Senast uppdaterad: 2019-08-14Bibliografiskt granskad

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Epidamik proceedings(20463 kB)46 nedladdningar
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