Bellazzi Riccardo, Sacchi Lucia, Concaro Stefano
Dipartimento di Informatica e Sistemistica, University of Pavia, via Ferrata 1, Pavia, Italy.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:5629-32. doi: 10.1109/IEMBS.2009.5333788.
Temporal data mining is becoming an important tool for health care providers and decision makers. The capability of handling and analyzing complex multivariate data may allow to extract useful information coming from the day-by-day activity of health care organizations as well as from patients monitoring. In this paper we review the main approaches presented in the literature to mine biomedical time sequences and we present a novel approach able to deal with "point-like" and "interval-like" events. The methods is described and the results obtained on two clinical data sets are shown.
时态数据挖掘正成为医疗保健提供者和决策者的一项重要工具。处理和分析复杂多变量数据的能力可能有助于从医疗保健组织的日常活动以及患者监测中提取有用信息。在本文中，我们回顾了文献中提出的挖掘生物医学时间序列的主要方法，并提出了一种能够处理“点状”和“区间状”事件的新方法。我们描述了该方法，并展示了在两个临床数据集上获得的结果。
