Context inference and prediction modeling in ubiquitous health GIS

Kaffash‐Charandabi Neda; Alesheikh Ali‐Asghar

摘要

Abstract >The ever‐increasing population in cities intensifies environmental pollution that increases the number of asthmatic patients. Other factors that may influence the prevalence of asthma are atmospheric parameters, physiographic elements and personal characteristics. These parameters can be incorporated into a model to monitor and predict the health conditions of asthmatic patients in various contexts. Such a model is the base for any asthma early warning system. This article introduces a novel ubiquitous health system to monitor asthmatic patients. Ubiquitous systems can be effective in monitoring asthmatic patients through the use of intelligent frameworks. They can provide powerful reasoning and prediction engines for analyzing various situations. Our proposed model encapsulates several tools for preprocessing, reasoning and prediction of asthma conditions. In the preprocessing phase, outliers in the atmospheric datasets were detected and missing sensor data were estimated using a Kalman filter, while in the reasoning phase, the required information was inferred from the raw data using some rule‐based inference techniques. The asthmatic conditions of patients were predicted accurately by a Graph‐Based Support Vector Machine in a Context Space (GBSVMCS) which functions anywhere, anytime and with any status. GBSVMCS is an improved version of the common Support Vector Machine algorithm with the addition of unlabeled data and graph‐based rules in a context space. Based on the stored value for a patient's condition and his/her location/time, asthmatic patients can be monitored and appropriate alerts will be given. Our proposed model was assessed in Region 3 of Tehran, Iran for monitoring three different types of asthma: allergic, occupational and seasonal asthma. The input data to our system included air pollution data, the patients’ personal information, patients’ locations, </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> <div class="translation abstracttxt"> <span class="zhankaihshouqi fivelineshidden" id="abstract"> <span>机译：</span><Abstract Type =“Main”XML：Lang =“en”> <标题类型=“main”>摘要</ title> >城市的不断增加的人口加剧了哮喘患者数量的环境污染。可能影响哮喘患病率的其他因素是大气参数，地理学元素和个人特征。这些参数可以掺入模型中以在各种情况下监测和预测哮喘患者的健康状况。这种模型是任何哮喘预警系统的基础。本文介绍了一种新型无处不在的健康系统，用于监测哮喘患者。无处不在的系统通过使用智能框架可以有效地监测哮喘患者。它们可以提供强大的推理和预测发动机，用于分析各种情况。我们所提出的模型封装了几种用于预处理，推理和预测哮喘病症的工具。在预处理阶段，使用Kalman滤波器估计大气数据集中的异常值，并且在推理阶段，在推理阶段，使用基于规则的推导技术从原始数据推断所需信息。基于图形的支持向量机（GBSVMCS）中的基于图形的支持向量机（GBSVMC）中的任何位置和任何状态，通过基于图形的支持向量机准确预测患者的哮喘条件。 GBSVMCS是一个改进的公共支持向量机算法版本，并在上下文空间中添加了未标记的数据和基于图形的规则。基于患者病症的储值和他/她的位置/时间，可以监测哮喘患者，并将提供适当的警报。我们的拟议模型被评估在伊朗德黑兰的第3区，用于监测三种不同类型的哮喘：过敏性，职业和季节性哮喘。我们系统的输入数据包括空气污染数据，患者的个人信息，患者的位置， </span> <span class="z_kbtn z_kbtnclass hoverxs" style="display: none;">展开▼</span> </div> </div> <div class="record"> <h2 class="all_title" id="enpatent33" >著录项</h2> <ul> <li> <span class="lefttit">来源</span> <div style="width: 86%;vertical-align: text-top;display: inline-block;"> <a href='/journal-foreign-31674/'>《Transactions in GIS: TG》</a> <b style="margin: 0 2px;">|</b><span>2017年第6期</span><b style="margin: 0 2px;">|</b><span>共17页</span> </div> </li> <li> <div class="author"> <span class="lefttit">作者</span> <p id="fAuthorthree" class="threelineshidden zhankaihshouqi"> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Kaffash‐Charandabi Neda&option=202" target="_blank" rel="nofollow">Kaffash‐Charandabi Neda;</a> <a href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=Alesheikh Ali‐Asghar&option=202" target="_blank" rel="nofollow">Alesheikh Ali‐Asghar;</a> </p> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zkzz" style="display: none;">展开▼</span> </div> </li> <li> <div style="display: flex;"> <span class="lefttit">作者单位</span> <div style="position: relative;margin-left: 3px;max-width: 639px;"> <div class="threelineshidden zhankaihshouqi" id="fOrgthree"> <p>Department of GIS K.N. Toosi University of Technology Tehran Iran;</p> <p>Department of GIS K.N. Toosi University of Technology Tehran Iran;</p> </div> <span class="z_kbtnclass z_kbtnclassall hoverxs" id="zhdw" style="display: none;">展开▼</span> </div> </div> </li> <li > <span class="lefttit">收录信息</span> <span style="width: 86%;vertical-align: text-top;display: inline-block;"></span> </li> <li> <span class="lefttit">原文格式</span> <span>PDF</span> </li> <li> <span class="lefttit">正文语种</span> <span>eng</span> </li> <li> <span class="lefttit">中图分类</span> <span><a href="https://www.zhangqiaokeyan.com/clc/4940.html" title="测绘数据库与信息系统">测绘数据库与信息系统;</a></span> </li> <li class="antistop"> <span class="lefttit">关键词</span> <p style="width: 86%;vertical-align: text-top;"> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=ubiquitous system&option=203" rel="nofollow">ubiquitous system;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=GIS&option=203" rel="nofollow">GIS;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=asthma&option=203" rel="nofollow">asthma;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=reasoning&option=203" rel="nofollow">reasoning;</a> <a style="color: #3E7FEB;" href="/search.html?doctypes=4_5_6_1-0_4-0_1_2_3_7_9&sertext=prediction&option=203" rel="nofollow">prediction;</a> </p> <div class="translation"> 机译：无处不在的系统;GIS;哮喘;推理;预测; </div> </li> </ul> </div> </div> <div class="literature cardcommon"> <div class="similarity "> <h3 class="all_title" id="enpatent66">相似文献</h3> <div class="similaritytab clearfix"> <ul> <li class="active" >外文文献</li> <li >中文文献</li> <li >专利</li> </ul> </div> <div class="similarity_details"> <ul > <li> <div> <b>1. </b><a class="enjiyixqcontent" href="/journal-foreign-detail/0704024965592.html">Context inference and prediction modeling in ubiquitous health GIS</a> <b>[J]</b> . <span> <a 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