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Erschienen in:
The Power of the “Pursuit” Learning Paradigm in the Partitioning of Data Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

FISUL: A Framework for Detecting Adverse Drug Events from Heterogeneous Medical Sources Using Feature Importance

verfasst von : Corinne G. Allaart, Lena Mondrejevski, Panagiotis Papapetrou

Erschienen in: Artificial Intelligence Applications and Innovations

Verlag: Springer International Publishing

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Abstract

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.

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Vorheriges Kapitel Emotion Analysis in Hospital Bedside Infotainment Platforms Using Speeded up Robust Features
Nächstes Kapitel A New Topology-Preserving Distance Metric with Applications to Multi-dimensional Data Clustering
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Metadaten
Titel
FISUL: A Framework for Detecting Adverse Drug Events from Heterogeneous Medical Sources Using Feature Importance
verfasst von
Corinne G. Allaart
Lena Mondrejevski
Panagiotis Papapetrou
Copyright-Jahr
2019
Buch
Artificial Intelligence Applications and Innovations

Print ISBN: 978-3-030-19822-0

Electronic ISBN: 978-3-030-19823-7

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-19823-7_11

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