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FISUL: A Framework ...
FISUL: A Framework for Detecting Adverse Drug Events from Heterogeneous Medical Sources Using Feature Importance
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- Allaart, Corinne (författare)
- Stockholms universitet,Institutionen för data- och systemvetenskap,Karolinska Institute, Sweden
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- Mondrejevski, Lena (författare)
- Stockholms universitet,Institutionen för data- och systemvetenskap,Karolinska Institute, Sweden
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- Papapetrou, Panagiotis (författare)
- Stockholms universitet,Institutionen för data- och systemvetenskap
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(creator_code:org_t)
- 2019-05-12
- 2019
- Engelska.
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Ingår i: Artificial Intelligence Applications and Innovations. - Cham : Springer. - 9783030198220 - 9783030198237 ; , s. 139-151
- 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
- Adverse drug events (ADEs) are considered to be highly important and critical conditions, while accounting for around 3.7% of hospital admissions all over the world. Several studies have applied predictive models for ADE detection; nonetheless, only a restricted number and type of features has been used. In the paper, we propose a framework for identifying ADEs in medical records, by first applying the Boruta feature importance criterion, and then using the top-ranked features for building a predictive model as well as for clustering. We provide an experimental evaluation on the MIMIC-III database by considering 7 types of ADEs illustrating the benefit of the Boruta criterion for the task of ADE detection.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Adverse drug events
- Feature importance
- Predictive models
- Clustering
- Computer and Systems Sciences
- data- och systemvetenskap
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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