Gutenberg Open Science: Unsupervised anomaly detection of implausible electronic health records : a real-world evaluation in cancer registries

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-9639

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DC Field	Value	Language
dc.contributor.author	Röchner, Philipp	-
dc.contributor.author	Rothlauf, Franz	-
dc.date.accessioned	2023-10-24T10:33:54Z	-
dc.date.available	2023-10-24T10:33:54Z	-
dc.date.issued	2023	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/9657	-
dc.description.abstract	Background: Cancer registries collect patient-specific information about cancer diseases. The collected information is verified and made available to clinical researchers, physicians, and patients. When processing information, cancer registries verify that the patient-specific records they collect are plausible. This means that the collected information about a particular patient makes medical sense.	en_GB
dc.description.abstract	Methods: Unsupervised machine learning approaches can detect implausible electronic health records without human guidance. Therefore, this article investigates two unsupervised anomaly detection approaches, a pattern-based approach (FindFPOF) and a compression-based approach (autoencoder), to identify implausible electronic health records in cancer registries. Unlike most existing work that analyzes synthetic anomalies, we compare the performance of both approaches and a baseline (random selection of records) on a real-world dataset. The dataset contains 21,104 electronic health records of patients with breast, colorectal, and prostate tumors. Each record consists of 16 categorical variables describing the disease, the patient, and the diagnostic procedure. The samples identified by FindFPOF, the autoencoder, and a random selection—a total of 785 different records—are evaluated in a real-world scenario by medical domain experts.	en_GB
dc.description.abstract	Results: Both anomaly detection methods are good at detecting implausible electronic health records. First, domain experts identified 8% of 300 randomly selected records as implausible. With FindFPOF and the autoencoder, 28% of the proposed 300 records in each sample were implausible. This corresponds to a precision of 28% for FindFPOF and the autoencoder. Second, for 300 randomly selected records that were labeled by domain experts, the sensitivity of the autoencoder was 22% and the sensitivity of FindFPOF was 26%. Both anomaly detection methods had a specificity of 94%. Third, FindFPOF and the autoencoder suggested samples with a different distribution of values than the overall dataset. For example, both anomaly detection methods suggested a higher proportion of colorectal records, the tumor localization with the highest percentage of implausible records in a randomly selected sample.	en_GB
dc.description.sponsorship	Deutsche Forschungsgemeinschaft (DFG)\|491381577\|Open-Access-Publikationskosten 2022–2024 Universität Mainz - Universitätsmedizin	-
dc.language.iso	eng	de
dc.rights	CC BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.ddc	004 Informatik	de_DE
dc.subject.ddc	004 Data processing	en_GB
dc.subject.ddc	330 Wirtschaft	de_DE
dc.subject.ddc	330 Economics	en_GB
dc.title	Unsupervised anomaly detection of implausible electronic health records : a real-world evaluation in cancer registries	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-9639	-
jgu.type.contenttype	Scientific article	de
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 03 Rechts- und Wirtschaftswissenschaften	de
jgu.organisation.number	2300	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	BMC Medical Research Methodology	de
jgu.journal.volume	23	de
jgu.pages.alternative	125	de
jgu.publisher.year	2023	-
jgu.publisher.name	Springer Nature	de
jgu.publisher.place	London	de
jgu.publisher.issn	1471-2288	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	004	de
jgu.subject.ddccode	330	de
jgu.publisher.doi	10.1186/s12874-023-01946-0	de
jgu.organisation.ror	https://ror.org/023b0x485	-
jgu.subject.dfg	Geistes- und Sozialwissenschaften	de
Appears in collections:	DFG-491381577-G

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