title: Temporal data mining.
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.
authors.affiliation: Division of Clinical Informatics, Department of Public Health Sciences, University of Virginia, Suite 3181 West Complex, 1335 Hospital Drive, Charlottesville, VA 22908-0717, USA. arp4m@virginia.edu
authors.forename: Andrew R
authors.initials: AR
authors.lastname: Post
authors.forename: James H
authors.initials: JH
authors.lastname: Harrison
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019992:Databases as Topic; D006801:Humans; D008490:Medical Informatics / Q000379:methods*; D010363:Pattern Recognition, Automated; D012984:Software; D013997:Time Factors