Khalid S, Clifton D, Clifton L, Tarassenko L
IEEE Trans Inf Technol Biomed. 2012 Nov;16(6):1231-8. doi: 10.1109/TITB.2012.2212202. Epub 2012 Aug 7.
Hospital patient outcomes can be improved by the early identification of physiological deterioration. Automatic methods of detecting patient deterioration in vital-sign data typically attempt to identify deviations from assumed normal physiological conditions, which is a one-class approach to classification. This paper investigates the use of a two-class approach, in which abnormal physiology is modelled explicitly. The success of such a method relies on the accuracy of data labels provided by clinical experts, which may be incomplete (due to large dataset size) or imprecise (due to clinical labels covering intervals, rather than each data point within those intervals). We propose a novel method of refining clinical labels such that the two-class classification approach may be adopted for identifying patient deterioration. We demonstrate the effectiveness of the proposed methods using a large dataset acquired in a 24-bed hospital step-down unit.
通过早期识别生理状况恶化可改善医院患者的治疗结果。检测生命体征数据中患者病情恶化的自动方法通常试图识别与假定正常生理状况的偏差，这是一种一类分类方法。本文研究使用二类分类方法，其中明确对异常生理状况进行建模。这种方法的成功依赖于临床专家提供的数据标签的准确性，这些标签可能不完整（由于数据集规模大）或不精确（由于临床标签涵盖区间，而非这些区间内的每个数据点）。我们提出一种改进临床标签的新方法，以便采用二类分类方法来识别患者病情恶化。我们使用在一家拥有24张床位的医院逐步降低护理级别病房获取的大型数据集证明了所提方法的有效性。
