2009 · 48 citations · 10 references
ComputingBiomedical InformaticsHealth InformaticsClinical InformaticsData MiningTime Series AnalysisClinical AnalyticsData PreprocessingTemporal Data MiningTime Series AnalyticsClinical Data MiningClinical Time Series AnalysisData Stream MiningHigh-dimensional Time SeriesSpatio-temporal Data MiningStreaming Data MiningTemporal Clinical Data ModelingTemporal Data Abstraction
The growing number of integrated temporal abstraction/data mining systems reveals a limitation in the standard CRISP‑DM process, motivating the development of CRISP‑TDM, which supports clinical investigations on multidimensional time‑series data. The study uses a NICU case to evaluate the CRISP‑DM process for intelligent data analysis systems performing temporal data mining, aiming to summarize the extended CRISP‑TDM methodology, demonstrate its applicability to NICU multidimensional time‑series data, and describe the proposed IDA architecture. The authors present the CRISP‑TDM methodology, apply it to NICU data to address multidimensional time‑series challenges, and outline an IDA architecture for integrated temporal data mining. The NICU case study demonstrates that the standard CRISP‑DM framework is insufficient for clinical systems using data mining and temporal abstraction, underscoring the need for the extended CRISP‑TDM approach.
Using a neonatal intensive care unit (NICU) case study, this work investigates the current cross industry standard process for Data Mining (CRISP-DM) approach for modeling intelligent data analysis (IDA)-based systems that perform temporal data mining (TDM). The case study highlights the need for an extended CRISP-DM approach when modeling clinical systems applying data mining (DM) and temporal abstraction (TA). As the number of such integrated TA/DM systems continues to grow, this limitation becomes significant and motivated our proposal of an extended CRISP-DM methodology to support TDM, known as CRISP-TDM. This approach supports clinical investigations on multi-dimensional time series data. This research paper has three key objectives: 1) Present a summary of the extended CRISP-TDM methodology; 2) Demonstrate the applicability of the proposed model to the NICU data, focusing on the challenges associated with multi-dimensional time series data; and 3) Describe the proposed IDA architecture for applying integrated TDM.
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Gennady Andrienko, Donato Malerba, Michael May et al. · Journal of Intelligent Information Systems · 2006 · 42 citations
Andrew R. Post, James H. Harrison · Clinics in Laboratory Medicine · 2008 · 40 citations