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

Overview of attention for article published in BMC Medical Research Methodology, May 2023

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Unsupervised anomaly detection can significantly reduce the manual effort of domain experts to find implausible electronic health records in cancer registries, as reported by @PhilippRoechner &al. in #BMCMedResMethodol: https://t.co/8INOHqu7Bt @uni_ma

20 Jul 2023

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Read our latest #research on unsupervised anomaly detection of implausible electronic health records, published with @SpringerNature in @BioMedCentral Medical Research Methodology: https://t.co/veGOPpqwLR #health #medical

06 Jun 2023

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