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- W4313703749 abstract "Anomaly detection is an integral component of operating a clinical laboratory. It covers both the recognition of laboratory errors and the rapid reporting of clinically impactful results. Procedures for identifying laboratory errors and highlighting critical results can be improved by applying modern data-driven approaches.This review will prepare the reader to appraise anomaly detection literature, identify common sources of anomalous results in the clinical laboratory, and offer potential solutions for common shortcomings in current laboratory practices.Laboratories should implement data-driven approaches to detect technical anomalies and keep them from entering the medical record, while also using the full array of clinical metadata available in the laboratory information system for context-dependent, patient-centered result interpretations." @default.
- W4313703749 created "2023-01-08" @default.
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- W4313703749 date "2023-01-04" @default.
- W4313703749 modified "2023-09-27" @default.
- W4313703749 title "Data-Driven Anomaly Detection in Laboratory Medicine: Past, Present, and Future" @default.
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- W4313703749 doi "https://doi.org/10.1093/jalm/jfac114" @default.
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