多参数时间过程预测是基于案例推理和数据时间抽象的方法。

R Schmidt, B Heindl, B Pollwein, L Gierl
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引用次数: 12

摘要

在本文中,我们描述了一种利用基于案例的推理方法对医疗问题进行趋势预测的方法。由于在没有典型课程模式的医学知识的情况下,使用传统方法进行时间推理不适合课程预测,因此我们开发了适合集成到基于案例的推理系统图标中的抽象方法。这些方法结合了医学经验和多参数病程的预后。我们选择在重症监护病房(ICU)设置肾功能监测作为诊断问题的一个例子。在ICU,监测系统NIMON根据当前测量和计算的肾功能参数提供每日报告。我们将这些参数抽象为日常肾功能状态。随后,我们使用这些状态来生成随时间变化的肾功能的病程特征趋势描述。使用基于案例的推理检索方法,我们在案例库中搜索与当前趋势描述相似的课程。最后,我们将当前的课程与类似的课程一起呈现给用户,作为比较和尽可能的预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multiparametric time course prognoses by means of case-based reasoning and abstractions of data and time.

In this paper we describe an approach to utilize Case-Based Reasoning methods for trend prognoses for medical problems. Since using conventional methods for reasoning over time does not fit for course predictions without medical knowledge of typical course pattern, we have developed abstraction methods suitable for integration into our Case-Based Reasoning system ICONS. These methods combine medical experience with prognoses of multiparametric courses. We have chosen the monitoring of the kidney function in an Intensive Care Unit (ICU) setting as an example for diagnostic problems. On the ICU, the monitoring system NIMON provides a daily report based on current measured and calculated kidney function parameters. We abstract these parameters to a daily kidney function state. Subsequently, we use these states to generate course-characteristic trend descriptions of the renal function over the course of time. Using Case-Based Reasoning retrieval methods, we search in the case base for courses similar to the current trend descriptions. Finally, we present the current course together with similar courses as comparisons and as possible prognoses to the user.

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