Miksch S, Horn W, Popow C, Paky F
Austrian Research Institute for Artificial Intelligence, Vienna, Austria.
Artif Intell Med. 1996 Nov;8(6):543-76. doi: 10.1016/s0933-3657(96)00355-7.
Medical diagnosis and therapy planning at modern intensive care units (ICUs) have been refined by the technical improvement of their equipment. However, the bulk of continuous data arising from complex monitoring systems in combination with discontinuously assessed numerical and qualitative data creates a rising information management problem at neonatal ICUs (NICUs). We developed methods for data validation and therapy planning which incorporate knowledge about point and interval data, as well as expected qualitative trend descriptions to arrive at unified qualitative descriptions of parameters (temporal data abstraction). Our methods are based on schemata for data-point transformation and curve fitting which express the dynamics of and the reactions to different degrees of parameters' abnormalities as well as on smoothing and adjustment mechanisms to keep the qualitative descriptions stable. We show their applicability in detecting anomalous system behavior early, in recommending therapeutic actions, and in assessing the effectiveness of these actions within a certain period. We implemented our methods in VIE-VENT, an open-loop knowledge-based monitoring and therapy planning system for artificially ventilated newborn infants. The applicability and usefulness of our approach are illustrated by examples of VIE-VENT. Finally, we present our first experiences with using VIE-VENT in a real clinical setting.
现代重症监护病房（ICU）的医疗诊断和治疗规划因设备技术的改进而得到了完善。然而，复杂监测系统产生的大量连续数据，再加上间断评估的数值和定性数据，给新生儿重症监护病房（NICU）带来了日益严重的信息管理问题。我们开发了数据验证和治疗规划方法，这些方法纳入了关于点数据和区间数据的知识，以及预期的定性趋势描述，以得出参数的统一定性描述（时间数据抽象）。我们的方法基于数据点转换和曲线拟合的模式，这些模式表达了参数不同程度异常的动态变化和反应，以及基于平滑和调整机制以保持定性描述的稳定性。我们展示了它们在早期检测异常系统行为、推荐治疗行动以及评估这些行动在一定时期内的有效性方面的适用性。我们在VIE-VENT中实现了我们的方法，VIE-VENT是一个用于人工通气新生儿的基于知识的开环监测和治疗规划系统。通过VIE-VENT的示例说明了我们方法的适用性和实用性。最后，我们介绍了在实际临床环境中使用VIE-VENT的初步经验。
