Temporal data mining

Clinics in Laboratory Medicine
Andrew R Post, James H Harrison

Abstract

Large-scale clinical databases provide a detailed perspective on patient phenotype in disease and the characteristics of health care processes. Important information is often contained in the relationships between the values and timestamps of sequences of clinical data. The analysis of clinical time sequence data across entire patient populations may reveal data patterns that enable a more precise understanding of disease presentation, progression, and response to therapy, and thus could be of great value for clinical and translational research. Recent work suggests that the combination of temporal data mining methods with techniques from artificial intelligence research on knowledge-based temporal abstraction may enable the mining of clinically relevant temporal features from these previously problematic general clinical data.

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Citations

Apr 29, 2009·Clinical Chemistry and Laboratory Medicine : CCLM·Karin J VelthoveMadelon Bracke
Dec 12, 2012·Archives of Pathology & Laboratory Medicine·Jorge L Sepulveda, Donald S Young
Apr 5, 2016·Computer Methods and Programs in Biomedicine·Mohcine MadkourCui Tao
Dec 8, 2011·Methods of Information in Medicine·R BellazziA T McCray
Sep 5, 2012·Journal of Biomedical Informatics·Milos HauskrechtGilles Clermont
May 26, 2010·Risk Analysis : an Official Publication of the Society for Risk Analysis·Lianfa LiChengsheng Jiang

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