独立环境下风险预测与防范的推理支持 活生生的

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摘要翻译：
近年来，由于老龄化人口的大量增加，为老年人提供临床护理的解决方案引起了越来越多的兴趣。在家庭环境中监测病人是必要的，以确保家庭环境中护理的连续性，但为了有用，这项活动不能对病人太有侵入性，也不能给护理人员带来负担。我们设计了一个名为SINDI（Secure and INDependent lIving）的系统，其重点是：i）通过无线传感器网络(WSN)收集有限数量的关于人和环境的数据；ii）从这些数据中推断足够的信息，以支持护理人员理解患者的健康状况并预测他们健康的可能演变。我们基于层次逻辑的健康模型将来自不同来源的数据、传感器数据、测试结果、常识知识和患者的临床概况在较低层次上结合起来，并将健康状况之间的关联规则在较高层次上结合起来。逻辑形式化和推理过程基于答案集编程。这种逻辑编程范式的表达能力使得即使在可用信息不完整和可能不一致的情况下，也有可能对健康进化进行推理，而声明性简化了护理者的规则规范，并允许知识的自动编码。本文描述了如何在SINDI系统的应用场景中解决这些问题。
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英文标题：
《Reasoning Support for Risk Prediction and Prevention in Independent
  Living》
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作者：
A. Mileo, D. Merico, R. Bisiani
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最新提交年份：
2010
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  In recent years there has been growing interest in solutions for the delivery of clinical care for the elderly, due to the large increase in aging population. Monitoring a patient in his home environment is necessary to ensure continuity of care in home settings, but, to be useful, this activity must not be too invasive for patients and a burden for caregivers. We prototyped a system called SINDI (Secure and INDependent lIving), focused on i) collecting a limited amount of data about the person and the environment through Wireless Sensor Networks (WSN), and ii) inferring from these data enough information to support caregivers in understanding patients' well being and in predicting possible evolutions of their health. Our hierarchical logic-based model of health combines data from different sources, sensor data, tests results, common-sense knowledge and patient's clinical profile at the lower level, and correlation rules between health conditions across upper levels. The logical formalization and the reasoning process are based on Answer Set Programming. The expressive power of this logic programming paradigm makes it possible to reason about health evolution even when the available information is incomplete and potentially incoherent, while declarativity simplifies rules specification by caregivers and allows automatic encoding of knowledge. This paper describes how these issues have been targeted in the application scenario of the SINDI system.
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PDF链接：
https://arxiv.org/pdf/1006.5657

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关键词：风险预测 活生生的 Hierarchical Presentation Intelligence 编程 系统 data 病人 预测