The aim of this study is to maintain up-to-date information about the current state of elderly people that are medically followed for risks of fall. Our proposal consists of an individual information database management system that can provide information on-demand on various variables. Such a system has to deal with several sources of uncertainty: lack of information, evolving information and reliability of the information sources. We consider that the features of the person may evolve with time causing uncertainty due to obsolete information. Our context includes new information received bit by bit, with no possibility to collect all required information at once. This paper establishes a first proposal to manage a set of uncertain observations, in order to reduce erroneous and obsolete information while keeping the benefit of previously collected information. We propose an architecture of the system based on a probabilistic knowledge model about the characteristics of interest, a set of decay functions that help to evaluate the confidence degree in previous observations, and a reasoning module to manage new observations, maintain the compatibility and the quality of the observation set. We detail the algorithms of the reasoning module, and the algorithm to update the confidence degree of the observations.

这项研究的目的是保持有关老年人跌倒风险的医学信息的最新信息。我们的建议包括一个单独的信息数据库管理系统，该系统可以按需提供有关各种变量的信息。这样的系统必须处理不确定性的几种来源：信息的缺乏，信息的不断发展以及信息源的可靠性。我们认为，人的特征可能会随着时间的流逝而发展，由于过时的信息而导致不确定性。我们的上下文包括一点一点接收到的新信息，不可能一次收集所有必需的信息。本文提出了管理一系列不确定观测值的第一个建议，为了减少错误和过时的信息，同时保留先前收集的信息的好处。我们基于有关兴趣特征的概率知识模型，一组有助于评估先前观测值的置信度的衰减函数以及管理新观测值，保持兼容性和质量的推理模块，提出系统的体系结构观察集。我们详细介绍了推理模块的算法，以及更新观测值置信度的算法。保持观测集的兼容性和质量。我们详细介绍了推理模块的算法，以及更新观测值置信度的算法。保持观测集的兼容性和质量。我们详细介绍了推理模块的算法，以及更新观测值置信度的算法。