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A clustering framew...
A clustering framework for patient phenotyping with application to adverse drug events
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- Bampa, Maria (författare)
- Stockholms universitet,Institutionen för data- och systemvetenskap
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- Papapetrou, Panagiotis (författare)
- Stockholms universitet,Institutionen för data- och systemvetenskap
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- Hollmén, Jaakko (författare)
- Stockholms universitet,Institutionen för data- och systemvetenskap
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(creator_code:org_t)
- IEEE, 2020
- 2020
- Engelska.
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Ingår i: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS). - : IEEE. - 9781728194295 - 9781728194301 ; , s. 177-182
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- We present a clustering framework for identifying patient groups with Adverse Drug Reactions from Electronic Health Records (EHRs). The increased adoption of EHRs has brought changes in the way drug safety surveillance is carried out and plays an important role in effective drug regulation. Unsupervised machine learning methods using EHRs as their input can identify patients that share common meaningful information, without the need for expert input. In this work, we propose a generalized framework that exploits the strengths of different clustering algorithms and via clustering aggregation identifies consensus patient cluster profiles. Moreover, the inherent hierarchical structure of diagnoses and medication codes is exploited. We assess the statistical significance of the produced clusterings by applying a randomization technique that keeps the data distribution margins fixed, as we are interested in evaluating information that is not conveyed by the marginal distributions. The experimental findings suggest that the framework produces medically meaningful patient groups with regard to adverse drug events by investigating two use-cases, i.e., aplastic anaemia and drug-induced skin eruption.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- data- och systemvetenskap
- Computer and Systems Sciences
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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