國 立 交 通 大 學
資訊管理研究所
碩 士 論 文
一個無所不在的情境感知式健康照護系統之設計與實作
The Design and Implementation of a Ubiquitous Context-aware
Healthcare Service System
研 究 生：陳 志 華
指導教授：羅 濟 群 教授
一個無所不在的情境感知式健康照護系統之設計與實作
The Design and Implementation of a Ubiquitous Context-aware
Healthcare Service System
研 究 生：陳 志 華 Student: Chi-Hua Chen
指導教授：羅 濟 群 Advisor: Chi-Chun Lo
國立交通大學 資訊管理研究所
碩士論文
A Thesis
Submitted to Institute of Information Management College of Management
National Chiao Tung University in Partial Fulfillment of the Requirements
for the Degree of
Master of Business Administration in
Information Management June 2009
Hsinchu, Taiwan, the Republic of China
一個無所不在的情境感知式健康照護系統之設計與實作
研究生：陳志華
指導教授：羅濟群 老師
國立交通大學資訊管理研究所
摘要
近年來，隨著經濟成長、人民生活水準的提高、醫學藥物進步，民眾的平均 年齡已顯著的延長，銀髮族、樂活族(Lifestyles Of Health And Sustainability, LOHAS)人口逐漸增加，而國人疾病型態及死亡原因，也由原來的急性傳染病和 急性疾病，轉變成慢性疾病，如心臟病、糖尿病、高血壓等。
本論文有鑑於觀光醫療和自然療法的未來需要，提出一個無所不在的情境感 知式健康照護系統之設計與實作(Ubiquitous Context-aware Healthcare Service System, UCHS) ， 主 要 包 含 有 情 境 感 知 式 觀 光 醫 療 服 務 搜 尋 子 系 統
(Situation-Aware Medical Tourism Service Search Subsystem, SAMTS3)、養生地圖
導覽子系統(Healthy-life Map Guiding Subsystem, HMGS)、智慧型飲食治療服務決 策支援子系統(Intelligent Curative Food Decision Support Subsystem, ICFDSS)、4D 路徑緊急醫療救護指派子系統(4D Emergency Indication and Ambulance Dispatch Subsystem, 4DEIADS)，以提供一套整合的自然療法服務平台。此系統結合專家 的醫療知識，同時考量各個療法對病症的正向影響和負向影響，並搭配語意網架 構推論使用者的病徵，經研究實驗證明可有效且快速地推薦給使用者一個合適的 自然療法綜合資訊服務，讓使用者可以達到吃喝玩樂享健康的願景。 關鍵字：觀光醫療、養生地圖、飲食治療、緊急救護、決策支援系統、資訊檢索、 自動摘要
The Design and Implementation of a Ubiquitous
Context-aware Healthcare Service System
Student:
Chi-Hua
Chen
Advisor:
Dr.
Chi-Chun
Lo
Institute of Information Management
Nation Chiao Tung University
Abstract
The rise of the quality of life index together with the improvement of medical technology lead to a longer life expectancy. Thus a better health care program, especially for elderly, is needed. The common health problems facing those senior citizens are changed from acute diseases to chronic diseases, such as diabetes, hypertension, and etc. Along with these changes, medical tourism is becoming the trend of the future.
In this paper, we propose a decision support systems, the Ubiquitous Context-aware Healthcare Service System (UCHS), which uses micro sensors integrate RFID to sense user’s life vital signal, such as electrocardiogram (ECG/EKG), heart rate (HR), respiratory rate (RR), blood pressure (BP), blood sugar (BS), and temperature and light. The UCHS is composted of Situation-Aware Medical Tourism
Service Search Subsystem (SAMTS3), Healthy-life Map Guiding Subsystem (HMGS),
Intelligent Curative Food Decision Support Subsystem (ICFDSS), and 4D Emergency Indication and Ambulance Dispatch Subsystem (4DEIADS) to provide relevant nature medicine recommendations to its user. The UCHS built upon an integrated service platform in which medical experts’ knowledge and all position and negative influence of the proposed therapy are inferred by using semantic network.
Keyword: Medical Tourism, Health-life Guiding, Curative Food, Emergency Medical, Decision Support System, Information Retrieval, Text Summarization.
Acknowledgment
I would first like to express my sincere thanks to my advisor, Dr. Chi-Chun Lo. Thanks also to the oral examiners, Dr. Her-Kun Chang, Dr. Shi-Jen Lin, and Dr. Tian-Shyr Dai. Without my advisor’s supervision and oral examiners’ suggestions, I can not complete this thesis.
Special thanks and appreciation to Ding-Yuan Cheng. Without his ingenious guidance and helpful discussions, this thesis would not have been possible.
I would also like to express my thanks to my master committee members, Yin-Jung Lu, Chang-Ming Chen, Ming-Chia Lee, Chia-Wei Hsu, Chih-Jung Peng, Yuan-Chen Huang, Hsiang-Ting Kao, Chih-Chien Lu, Guan-Ru He, Chih-Heng Wu, Hung-Chun Huang, Shih-Hao Huang, and Jen-Wei Huang for their encouragement and suggestions.
Great appreciation to my friends in National Chiao Tung University who inspire me to have confidence to accomplish this thesis.
Finally, I am grateful to my dear parents and my brothers for their total support and unfailing love in these years.
Chi-Hua Chen
National Chiao Tung University Hsinchu, Taiwan
Table of Contents
中文摘要... II Abstract...III Acknowledgment... IV Table of Contents ... V List of Figures...VIII List of Tables... X Chapter 1 Introduction... 11.1 Research Background and Motivation ... 1
1.2 Approach and Result... 2
1.3 Thesis Outline... 2
Chapter 2 Related Works ... 4
2.1 Semantic Web (SW) ... 4
2.1.1 Semantic Inference... 5
2.1.2 System Performance ... 5
2.2 Requirement Classification Techniques ... 5
2.3 Multiple Document Summarization (MDS)... 6
2.4 Context-aware... 9
Chapter 3 Ubiquitous Context-aware Healthcare Service System... 10
3.1 Overview ... 10
3.2 Situation-Aware Medical Tourism Service Search Subsystem ... 12
3.3 Healthy-life Map Guiding Subsystem... 13
3.4 Intelligent Curative Food Decision Support Subsystem ... 14
Chapter 4 Situation-Aware Medical Tourism Service Search Subsystem . 16
4.1 System Design Principles... 16
4.1.1 Semantic Inference Module ... 17
4.1.2 Adaptive Medical Tourism Service Inference Module ... 23
4.2 System Architecture... 25
4.2.1 Mobile Users (MUs) ... 26
4.2.2 UDDI Registries (UDDIRs)... 28
4.2.3 Medical Tourism Service Providers (MTSPs) ... 28
4.2.4 Medical Tourism Services Server (MTSS) ... 29
4.3 Evaluation and Case Study... 31
4.3.1 Evaluation ... 32
4.3.2 A Case Study ... 34
4.4 Discussion... 37
Chapter 5 Healthy-life Map Guiding Subsystem... 38
5.1 System Design Principles... 38
5.1.1 Blog Content Retrieval Agent... 39
5.1.2 Multiple Document Summarization... 40
5.2 System Architecture and Implementation ... 43
5.2.1 Clients ... 44
5.2.2 Multimedia Application Server (MAS) ... 44
5.3 Discussion... 48
Chapter 6 Intelligent Curative Food Decision Support Subsystem ... 49
6.1 System Design Priciple... 49
6.1.1 Curative Food Service Inference Agent... 49
6.1.2 Semantic Process Module... 50
6.1.3 Adaptive Curative Food Service Inference Module ... 53
6.2 System Architecture... 55
6.2.1 Mobile Medical Monitor (M3)... 55
6.2.2 Mobile Users (MUs) ... 57
6.2.4 Curative Food Service Providers (CFSPs)... 57
6.2.5 Curative Food Services Server (CFSS) ... 57
6.2.6 Active Database Server (ADS) ... 61
6.3 System Evaluation and Case Study ... 61
6.3.1 Evaluation ... 61
6.3.2 Case Study... 62
6.4 Discussion... 67
Chapter 7 4D Emergency Indication and Ambulance Dispatch Subsystem ... 69
7.1 System Design Priciple... 69
7.1.1 Knapsack Problems (KP)... 70
7.1.2 Adaptable Escape Path (AEP)... 70
7.1.3 Flood/Debris-flow Simulation ... 74
7.2 System Architecture and Implementation ... 76
7.2.1 Mobile Users (MUs) ... 77
7.2.2 Multimedia Server (MS)... 80
7.2.3 Disaster Decision Server (DDS) ... 81
7.3 Discussion... 82
Chapter 8 Conclusions and Future Work... 84
8.1 Summary... 84
8.2 Future Work ... 84
List of Figures
FIGURE.1.SEMANTIC WEB STACK... 4
FIGURE.2.THE PROCESS OF MEAD... 7
FIGURE.3.THE ARCHITECTURE OF UCHS... 11
FIGURE.4.MEDICAL TOURISM KNOWLEDGE TRAINING PROCESS... 17
FIGURE.5.A EXAMPLE OF THERAPY SUGGESTIONS FROM EXPERT... 18
FIGURE.6.THE STRUCTURE OF MEDICAL ONTOLOGY (MO) ... 22
FIGURE.7.THE ARCHITECTURE OF SAMTS3... 26
FIGURE.8.REQUEST SAMTS3 IN PC VERSION... 35
FIGURE.9.REQUEST SAMTS3 IN PDA VERSION... 35
FIGURE.10.RESPONSE SAMTS3 IN PC VERSION... 36
FIGURE.11.RESPONSE SAMTS3 IN PDA VERSION... 36
FIGURE.12.THE PROCESS OF HMGS... 39
FIGURE.13.MEAD’S AUTOMATIC TEXT SUMMARIZATION PROCESS... 41
FIGURE.14.THE ARCHITECTURE OF HMGS... 44
FIGURE.15.THE IMPLEMENTATION OF MDS BY MEAD... 46
FIGURE.16.THE IMPLEMENTATION OF MDS BY LIETU SEARCH... 46
FIGURE.17.THE COMMENTARIES OF HEALTHY-LIFE SCENIC SPOTS... 47
FIGURE.18.CURATIVE FOOD KNOWLEDGE TRAINING PROCESS OF IE ... 50
FIGURE.19.THE ARCHITECTURE OF ICFDSS... 56
FIGURE.20.FOOD’S NAME AND CALORIE... 63
FIGURE.21.COMPUTING BMI VALUE... 63
FIGURE.22.PARAMETERS OF LINE CHART... 64
FIGURE.23.REQUEST ICFDSS... 66
FIGURE.24.RESPONSE ICFDSS... 66
FIGURE.25.DTM OF NPUST... 74
FIGURE.26.THE ARCHITECTURE OF 4DEIADS SYSTEM... 77
FIGURE.27.DISASTER PICTURE ON PDA ... 80
FIGURE.28.MULTICAST MEETING ON PDA ... 80
List of Tables
Table. 1. Accuracy rate of classification by kNN with MTSM, MO, or LSA .. 32
Table. 2. Classification indexes in kNN with MTSM, MO, or LSA ... 33
Table. 3. Classification indexes in kNN with CFSM, MO, or LSA ... 63
Table. 4. The Basis of Level of Service Estimate ... 72
Table. 5. The- Adaptable Escape Path (AEP) ... 73
Chapter 1 Introduction
1.1 Research Background and Motivation
Recently medical tourism is becoming more popular, as more people realize its benefits. The main benefits of health tourism include getting the opportunity to travel to an exotic destination and reaping potentially big monetary savings [46]. In “Industrial Manpower Package for Three-year Head-start Project of Taiwan’s Economic Development Visions for 2015” [19], Ubiquitous Healthcare (U-Health) is meaned it has more commercial potential in taiwan in the future. Therefore, Taiwan Medical Tourism Development Association (TMTDA) was established to research and hold relevant medical tourism activities in August 2007. However, to date, there are only a few decision support systems (DSS) to provide Nature Medicine Services (NMS) recommendation which include Medical Tourism Service (MTS), Health-life Map Service (HMS), Curative Food Service (CFS), and etc. to reach the target “Eat, Drink, and Be Merry with Health”.
Recently there have been many developments on recommendation model in other domains for the semantic web which is an opportune moment to look at the field’s current state and future opportunities. For inference user’s requirement to recommend, the semantic web possibly combines Service-Oriented Architecture (SOA, includes UDDI (Universal Description, Discovery and Integration), SOAP (Simple Object Access Protocol), WSDL (Web Services Description Language)) with RDF (Resource Description Framework), DAML (DARPA Agent Markup Language), DAML-S, DAML+OIL, OWL (Web Ontology Language), OWL-S, or etc. [14, 49, 75].
The need for NMS recommend in semantic web is driven by three demands. (i) To inference user’s requirements by semantic engine.
(ii) To search, compare, reorganize, and integrate relevant web services to be medical tourism service according to medical domain knowledges.
(iii) To reduce query processes and time.
1.2 Approach and Result
This paper provides an overview of the medical tourism service recommend in semantic web, combines the technical application of the SOA, OWL-S, semantic web on information system, the system gives strong auxiliary utility to support users while they have some complex problem. The designed Ubiquitous Context-aware Healthcare Service System (UCHS) is composted of Situation-Aware Medical
Tourism Service Search Subsystem (SAMTS3), Healthy-life Map Guiding Subsystem
(HMGS), Intelligent Curative Food Decision Support Subsystem (ICFDSS), and 4D Emergency Indication and Ambulance Dispatch Subsystem (4DEIADS) to provide relevant nature medicine recommendations to its user [5, 6, 7, 8, 24]. Using SOA, OWL-S to build semantic web environment to inference user’s requirements and search various web services which are published in UDDI through the communication networks include internet and 3G/GPRS/GSM mobile networks [2, 13]. In this paper, we propose the specific Medical Stemming Mechanism (MSM) and Medical Ontologies (MOs) using OWL-S combined Adaptive Medical Service Inference Module (AMSIM) which is composed of Term Frequency – Inverse Document Frequency (TF-IDF), Latent Semantic Analysis (LSA), and k–Nearest Neighbor (kNN) to reference the adaptable NMS to MUs.
1.3 Thesis Outline
The remainder of the thesis is built as follows. In Chapter 2, we provide background knowledge through the description of related technologies, such as the
concept of Semantic Web (SW), Requirement Classification Techniques, Multiple Document Summarization (MDS), and Context-aware, and the discussion about the current methods for NMS searching. In Chapter 3, we proposed the Ubiquitous Context-aware Healthcare Service System (UCHS) combined semantic inference and context-aware techque to provide NMS. The framework which is composted of
Situation-Aware Medical Tourism Service Search Subsystem (SAMTS3), Healthy-life
Map Guiding Subsystem (HMGS), Intelligent Curative Food Decision Support Subsystem (ICFDSS), and 4D Emergency Indication and Ambulance Dispatch Subsystem (4DEIADS) is explained in Chapter 4~7. Finally conclusion and the suggestions are given in Chapter 8.
Chapter 2 Related Works
The Designed Ubiquitous Context-aware Healthcare Service System (UCHS) is to provide (i) user’s requirement inference, (ii) Nature Medicine Services (NMS) decision support, (iii) searching and inference performance. Necessary research background and relevant technology include as follows: (1) Semantic Web (SW), (2) Requirement Classification Techniques, (3) Multiple Document Summarization (MDS), and (4) Context-aware.
2.1
Semantic Web (SW)
To solve the problem of lacking effective service query mechanism in existing web services, a SW based technology based web services query mechanism was proposed by Tim Berners-Lee whose proposed vision [60] is shown as Fig. 1.
Figure. 1. Semantic Web Stack [60]
In this paper, we focus on (1) semantic inference and (2) system performance de-scribed as follow.
2.1.1 Semantic Inference
For inference user’s requirement semantically, Ora Lassila and James Hendler [49] proposed a architecture of SW applications based on RDF, with patterns in which one component uses another as a data source (via SPARQL) and acts as a data source to yet another component. However, RDF and RDF schema provide properties and syntax not completely to build ontology architecture. In this paper, we use the OWL-S which is an OWL-based Web service ontology that supplies web service providers with a core set of markup language constructs for describing the properties and capabilities of Web ser-vices in unambiguous, computer-interpretable form.
2.1.2 System Performance
For efficient selection of QoS-aware web service, in reference [42], we can know the inquiry API of JUDDI has better performance than JWSDP (Java Web Services Devel-opment Pack). And there were some approaches proposed by reference [44, 68], which used “cache”mechanism for reducing process and queries while service broker infer-ences QoS-aware web services. Therefore, we choice JUDDI to build UCHS with “cache” mechanism to provide SW services.
2.2 Requirement Classification Techniques
To date, there are various requirement classification systems which are proposed and implemented. In general, those systems consist of the following steps: (1) Preproc-essing, (2) Constructing a set of centroid-sentences as training data for each topic cate-gory, and (3) Learning classifier [72].
The main roles of preprocessing are (i) segmenting requirements into sentences and (ii) extracting content word [72]. For Chinese, the CKIP group develops the Chinese segmentation system which includes the methods for resolving unknown
words [66] is very useful. Although, these approaches are serviceable, they have lower power for spe-cial domain such as medical tourism in this case. For example, the sentence “皮膚有類症” which there are four segments “皮膚(Na)”, “有(V_2)”, “類 (Nf)”, and “症(Na)” of is a specific technicality.
In step (2), it focus on (i) generating a keyword list for each category, (ii) extracting keyword sentences, and (iii) measuring word and sentence similarities [72]. For measur-ing similarities, there are two kinds of approaches proposed which are corpus-based se-mantic similarity (CBSS) and ontology-based semantic similarity
(OBSS). The basic idea of CBSS is the similarity of two words w1 and w2 can be
calculated through the two dis-tributions P(C | w1) and P(C | w2), where C is the union
of the w1 and w2 context features, which in the simplest form would be words that
co-occur in a corpus with w1 and w2 [31]. However, CBSS can’t support to
semantically classify ontological concepts, but OBSS. In this paper, UCHS will apply OBSS to use the hierarchy of the ontology to calculate the distance between two concepts.
For classifier, there are many kinds of classification techniques such as k–Nearest Neighbor (kNN) [48], clustering, and association rule. In [48], Naohiro et al. propose a new combining method which for consists of Latent Semantic Analysis (LSA) followed by the kNN the documents classification. Although, this result of combining method is the higher accuracy, the method only considers with the positive influence factors. We will consider the positive and negative influence factors to improve the inference algo-rithm.
2.3 Multiple Document Summarization (MDS)
Blogs increase greatly in recent years because of the rapid development of computer technology and the spread of internet. Users may get a huge amount of
information through the intelligent agency from the blogs. However, it’s not easy to filter the useless and repeated information for the users. To solve this problem, MMESP use the Multiple Document summarization (MDS) to simplify and get rid of the repeated information, so the users can save the searching time and get the important information.
UCHS is combined with MEAD which is a public domain portable multi-document summarization system based on Linux. MEAD whose process is shown as Fig. 2 is implemented by Perl programming language [18].
Preprocess .docsent .cluster Original Document .txt .s Feature Selection .sentFeature Classifier .sentjugde Reranker .sentjudge .extract Summary .summary text2cluster.pl Length.pl default-classifier.pl default-reranker.pl extract-to-summary.pl html2cluster.pl Position.pl Centroid .pl sentjudge-to-extract.pl Program
Figure. 2. The process of MEAD
(1) Preprocess
The intelligent agent to retrieve the contents of the Blog using HTML format to segment the sentences in original document in order to facilitate follow-up to the weight computing [9, 10, 11].
(2) Feature Selection
In this paper, MDS is designed to consider several features to compute the weight of each sentence by words and phrases. The main three features are centrality, sentence length, and position [4, 15, 16, 33, 34].
(3) Classifier
The scores of every sentence are mainly computed through the weight with each feature [37].
(4) Reranker
Because by the Classifier is only carried out in accordance with score of sentence similarity calculation and sorting. It makes the problem that may exist the high similarity between sentences, especially in multi-document summarization. MEAD designs Reranker mechanism to recalculate the sentence with the syntactic similarity and set the threshold to filter out important sentences to reduce the redundancy ratio. Finally, the summary is made by extracting the sentences from original document by the compression ratio [20].
(5) Summarization
Summarization can retrieve and recombine words and phrases in the original document according to the order of the sentences by Reranker sorting.
(6) Evaluation
HMGS is used to measure the performance of text summarization system including the effect of output results as well as users’ satisfaction [54].
2.4 Context-aware
Context awareness is the important interactive system. Researchers add appropriateness and keep with user’s requirement in the system. Dey et al [1] defined that “context is any information that can be used to characterize the situation of an entity. An entity is a person, place or object that is considered relevant to the interaction between a user and an application, including the user and applications themselves [1]”. Dey et al classify location, identity, time, and activity according to context-awareness.
In the above, context-awareness for the concept of location services of the extension service, the parameters take advantage sensors sensing user around environment. The system provides the adaptive services where users are in different environment [1, 23].
Chapter 3 Ubiquitous Context-aware Healthcare Service
System (UCHS)
For reaching the target “Eat, Drink, and Be Merry with Health”, this paper provides an overview of the Nature Medical Service (NMS) recommend in semantic web, combines the technical application of the SOA, OWL-S, semantic web on information system, the system gives strong auxiliary utility to support users while they have some complex problem. The designed Ubiquitous Context-aware Healthcare Service System (UCHS) is composted of Situation-Aware Medical
Tourism Service Search Subsystem (SAMTS3), Healthy-life Map Guiding Subsystem
(HMGS), Intelligent Curative Food Decision Support Subsystem (ICFDSS), and 4D Emergency Indication and Ambulance Dispatch Subsystem (4DEIADS) to provide relevant nature medicine recommendations to its user [5, 6, 7, 8, 24].
3.1
Overview
The architecture of Ubiquitous Context-aware Healthcare Service System
(UCHS) which is composited of SAMTS3, HMGS, ICFDSS, and 4DEIADS enhances
Mobile Users (MUs), Mobile Medical Monitor (M3), External Resource (ER), UDDI
Registries (UDDIRs), Nature Medical Service Providers (NMSPs), and Database Server (DS) shown as Fig. 3.
MUs can utilize various terminal devices that include Personal Computer (PC), notebook, Tablet PC, Personal Digital Assistant (PDA), and smrat phone to access
UCHS to get adaptable NMS. Moreover, MUs will use M3 combined micro sensors to
immediately sense user’s life vital signal, such as electrocardiogram (ECG/EKG), heart rate (HR), respiratory rate (RR), blood pressure (BP), blood sugar (BS),
temperature, and light parameters. Those records will be transmited and stored in DS
for inference by SAMTS3, HMGS, ICFDSS, and 4DEIADS [5, 6, 7, 8, 24].
Tablet PC Mobile Users 3G Phone PC Notebook PDA Communication Interface Services Businesses UDDI registries BindingTamplates tModels Communication Interface
Web Services Cache
Database Server
Geographic Information Database Connection Module User Requirement Database
Control Module
Nature Medicine Service Providers
Communication Interface Spring Industries
Tour Industries Restaurant Industries
Hospital Industries
Context-aware Ubiquitous Healthcare Service System
Situation-Aware Medical Tourism Service Search System Healthy-life Map Guiding System
Intelligent Curative Food Decision Support System 4D Emergency Indication and Ambulance Dispatch System Mobile Medical Monitor
Communication Interface
External Resource
Communication Interface Google Search Engine
Sogou Search Engine Tourism Information Curative Food Information
Blog Corpus Emergency Information
Figure. 3. The architecture of Ubiquitous Context-aware Healthcare Service System (UCHS)
The aim of semantic web is to locate services automatically based on the functionalities Web services provide. UDDI is helpful to discovery web services with semantic web. Therefore, we use the JUDDI to build UDDI environment which provides Business Entities, Service Entities, Binding Templates, and tModels to represent the detail of business and its services.
In this paper, NMSPs which can publish the NMS to UDDIRs through heterogeneous networks are Spring Industries, Tour Industries, Curative Food Industries, Hospital Industries, and etc. NMSPs will provide the order ticket service, reservation service, tourism information service, and emergency information service, and etc.
MUs use mobile device to request their medical requirements to UCHS, in order
to carry on the inference of NMS by SAMTS3, HMGS, ICFDSS, and 4DEIADS. In
this paper, UCHS will provide the adaptable package tour services to reach the target
“Eat, Drink, and Be Merry with Health” for MUs. First, SAMTS3 will provide the
adaptable Medical Tourism Service (MTS) by inference the MUs’ medical requirement and their history health records. HMGS will provide relevant tourism information and tourist experience summary according to the adaptable MTS. Finally, ICFDSS will provide the adaptable Curative Food Service (CFS) and reservation automatically by inference the MUs’medical requirement and MTS. On the other hand, UCHS also combines the 4DEIADS to provide Emergency Medical Service (EMS) when accident happened. UCHS will query and invoke the NMS from UDDIRs, NMSPs, and External Resource (ER) frequently to provide the adaptable
NMS. The introduction of SAMTS3, HMGS, ICFDSS, and 4DEIADS will be
discussed as following.
3.2
Situation-Aware Medical Tourism Service Search Subsystem
(SAMTS
3)
Recently medical tourism is becoming more popular, as more people realize its benefits. The main benefits of health tourism include getting the opportunity to travel to an exotic destination and reaping potentially big monetary savings [46]. Therefore, Taiwan Medical Tourism Development Association (TMTDA) was established to research and hold relevant medical tourism activities in August 2007.
In this paper, we propose a new Medical Tourism Service (MTS) recommend system, the designed Situation-Aware Medical Tourism Service Search Subsystem
(SAMTS3), which provides the cooperation web-based platform for all related Mobile
ability of MTS suggestion. SAMTS3 is a five-tier system composed of the MUs, UDDI Registries (UDDIRs), MTSPs, Medical Tourism Services Server (MTSS), and Database Server (DS). Using SOA, OWL-S to build semantic web environment to inference user’s medical requirements and search adaptive MTS and web services which are published in UDDI through the communication networks include internet and 3G/GPRS/GSM mobile networks. In this paper, we propose the specific Medical Tourism Stemming Mechanism (MTSM), Medical Ontology (MO), and Adaptive Medical Tourism Inference Module (AMTIM) combined Term Frequency – Inversed Document Frequency (TF - IDF), Latent Semantic Analysis (LSA), and k-Nearest Neighbor (kNN) to reference the adaptable MTS to MUs [7, 8]. The detailed design
and analysis of SAMTS3 will be discussed in chapter 4.
3.3
Healthy-life Map Guiding Subsystem (HMGS)
The rise of the quality of life index together with the improvement of economic growth lead to increase tourism requirements. Recently tourism which is becoming more popular has become a current hot topic. In addition, as people on the popularity of the concept of Healthy-Life, making more and more people increasingly attach importance to good health and enjoy Healthy-Life services. Healthy-Life tourism services are the trend of the future.
In this paper, we propose an effective decision support system (DSS), the Healthy-life Map Guiding Subsystem (HMGS), which provides the introduction and commentaries of Healthy-Life scenic spots with relevant recommendation. HMGS is a three-tier system composed of the Clients, Multimedia Application Server (MAS), and Database Server (DS) to provide the introduction and commentaries of Healthy-Life scenic spots with relevant recommendation. HMGS is combined with an automatic text summarization technology to provide summary of commentaries. Finally, HMGS
provides the adaptable tourism path in 3D web geographic information system for user reaching the Healthy-Life scenic spots [5]. The detailed design and analysis of HMGS will be discussed in chapter 5.
3.4
Intelligent Curative Food Decision Support Subsystem (ICFDSS)
Recently curative food is becoming more popular, as more people realize its benefits. Based on the theory of Chinese medicine, food itself is medicine. The curative food which is an ideal nutritious food can help to loss weight, increase immunity and is also good for curative effects in patients. While economy and health concept are raises, most food full on markets. How uses food to preserve people's health is a popular topic.
In this paper, we proposed and developed the “Intelligent Curative Food Decision Support Subsystem (ICFDSS)” to record efficiently user’s diet. Using micro sensors integrate RFID to sense user’s life vital signal, such as electrocardiogram (ECG/EKG), heart rate (HR), respiratory rate (RR), blood pressure (BP), blood sugar (BS), and treatment and light. ICFDSS provides the cooperation web-based platform for all related Mobile Users (MUs) and Curative Food Service Providers (CFSPs), could strengthen the ability of CFS suggestion. SCFSRS is a five-tier system composed of the MUs, UDDI Registries (UDDIRs), CFSPs, Curative Food Services Server (CFSS), and Database Server (DS). On the other hand, ICFDSS achieves balance diet that can provide diet exhorting and suggestion by Medical Ontology, Latent Semantic Analysis, and k-Nearest Neighbor etc [6]. The detailed design and analysis of ICFDSS will be discussed in chapter 6.
3.5
4D Emergency Indication and Ambulance Dispatch Subsystem
(4DEIADS)
When the heavy rainfalls or typhoons occurred, many counties were unavoidable to suffer the debris-flow and flood disasters in Taiwan. Therefore, it is urgently required to obtain and inform the real-time disaster information to display the situation, and it is important for people to design an effective disaster information system to assist the disaster protection and alerting works.
To plan and design a 4D Emergency Indication and Ambulance Dispatch Subsystem (4DEIADS), which is a three-tier system composed of the mobile users, multimedia server, and disaster decision server, and the system combines mobile communication technology. 4DEIADS combines RFID technology, GPS, GIS and GPRS/3G to find out the 4D safety paths and roads in the disaster areas, and the disaster decision is packed as a reusable web services which can be used in 4DEIADS or other systems for reducing the cost and speeding up the efficiency of system development in the future. Mobile users use mobile devices with GPS to locate the longitude and latitude, and transmitting these coordinates to the GIS server. According to the longitude and latitude, the GIS will draw VR map of disaster area using GIS relevant database and show the simulated safety way to users. The 4DEIADS can draw the points on the VR map that includes all users’ position and announces the best refuge and escape path. The reasoning engine of 4DEIADS is used Knapsack Problem (KP) and the Adaptive Path Algorithm (APA) which consider distance, security, traffic volume, cost, and refuges where RFID readers were installed to infer reason the 4D safety paths and escape routes [24]. The detailed design and analysis of 4DEIADS will be discussed in chapter 7.
Chapter 4 Situation-Aware Medical Tourism Service Search
Subsystem (SAMTS
3)
In this paper, we propose a new Medical Tourism Service (MTS) recommend system, the designed Situation-Aware Medical Tourism Service Search Subsystem
(SAMTS3), which provides the cooperation web-based platform for all related Mobile
Users (MUs) and Medical Tourism Service Providers (MTSPs), could strengthen the
ability of MTS suggestion. SAMTS3 is a five-tier system composed of the MUs,
UDDI Registries (UDDIRs), MTSPs, Medical Tourism Services Server (MTSS), and Database Server (DS). Using SOA, OWL-S to build semantic web environment to inference user’s medical requirements and search adaptive MTS and web services which are published in UDDI through the communication networks include internet and 3G/GPRS/GSM mobile networks. In this paper, we propose the specific Medical Tourism Stemming Mechanism (MTSM), Medical Ontology (MO), and Adaptive Medical Tourism Inference Module (AMTIM) combined Term Frequency – Inversed Document Frequency (TF - IDF), Latent Semantic Analysis (LSA), and k-Nearest Neighbor (kNN) to reference the adaptable MTS to MUs [7, 8].
4.1 System Design Principles
The Situation-Aware Medical Tourism Service Search Subsystem (SAMTS3)
provides Semantic Inference Module (SI) and Adaptive Medical Tourism Service Inference Module (AMTSIM). The SIM is combined specific Medical Tour-ism Stemming Mechanism (MTSM), Medical Ontology (MO), and OWL-S standard to inference and translate the original sentences to be machine readable. And the
AMTSIM uses the TF - IDF, LSA, and kNN to calculate the similarity and inference the adaptive MTS.
4.1.1 Semantic Inference Module (SIM)
The Semantic Inference Module (SIM) exploits MTSM and MO to explain and to represent the data of expert’s suggestions and user’s requirement shown as Fig. 4.
Model Base System
Semantic Inference Module
Medical Tourism Service Inference Agent
OWL-S Standard Experts (b ) Text Preprocessing (d ) (a) Suggestions (Training Data)
Suffix Stripping Algorithms Brute Force Algorithms
Lemmatisation Algorithms Medical Tourism Stemming Mechanism
(c
)
Medical Hierarchical Architecture Medical Classification
Medical Conception Retrieval Medical Ontology
Adaptive Medical Tourism Service Inference Module
(f)
Inversed Indexing File
Term Frequency - Inversed Document Frequency (TF - IDF)
(e)
Matrix Operations and Processes Singular Value Decomposition (SVD)
Latent Semantic Analysis (LSA)
(g ) (h) k - Nearest Neighbor (kNN) (i )
Medical Tourism Class
Figure. 4. Medical tourism knowledge training process
4.1.1.1 Preprocess
Preprocess translate the different expert’s suggestions and user’s requirements to be the vector space model. During training time, the collection of therapy suggestions (am example is shown as Fig. 5.) as a set of documents will be represented by a
infirmity as a word in a document, i.e. A1 = {afin | i∈ ,I n∈N}, where afin is the
frequency of the infirmity i in the therapy n. Let I be the number of the occurrence of all the infirmities in those therapies and N be the number of the occurrence of all the collected therapies.
Figure. 5. A example of therapy suggestions from expert (Wan Fang Hospital) [67]
Where A2 = {awin | i∈ ,I n∈N}, where awin is the influence weight of the
infirmity i in the therapy n and
awin = 1 , if therapy n will influence infirmity i in positively.
awin = 0 , if therapy n won’t influence infirmity i.
4.1.1.2 Medical Tourism Stemming Mechanism (MTSM)
To date, there are many stemming algorithms such as Brute Force Algorithms, Suffix Stripping Algorithms, Lemmatization Algorithms, and etc [63]. In this paper, we propose the Medical Tourism Stemming Mechanism (MTSM) based on CKIP segmentation system to process the special domain sentences referred to statistics and those algorithms. In addition to the affix-rules, a number of special conditions have to be designed to cover some specific medical tourism. The affix-rules which are distributed into three steps have the following general form:
affix → substitution <condition>
Step 1: This step includes several processes of the prefixes removing
“可以” → φ None “可” → φ None Step 1 (a) “能” → φ None “有” → φ None Step1 (b) “患” → φ None “治療” → φ None “治” → φ None Step 1 (c) “改善” → φ None Step 1 (d) “較” → φ None “嚴重” → φ None “重度” → φ None Step 1 (e) “輕度” → φ None
Step 2: The step includes several processes of the infixes removing
Step 2 (a) “的” → φ None
“和” → φ None “及” → φ None “或” → φ None Step 2 (b) “或者” → φ None Step 2 (c) “(-)” → φ None
Note: This process is removing the term between the parts of speech “(PARENTHESISCATEGORY)”.
Step 2 (d) “-期” → φ The part of speech of sentence “-期” is “(Na)”.
Note: This process is a loop until no the same term removed.
Step 3: This step includes several processes of the suffixes removing
“患者” → φ None “者” → φ None Step 3 (a) “人” → φ None Step 3 (b) “效果” → φ None “疾病” → φ None “病” → φ Term length ≥ 4 Step 3 (c) “症狀” → φ None Step 3 (d) “過程” → φ None
Step 3 (f) “障礙” → φ None
Step 3 (g) “系統” → φ None
For example, there are seven segments “可(D)”, “治(VC)”, “輕度(A)”, “的(DE)”, “早期(Nd)”, “心血管(Na)” , “系統(Na)”, “疾病(Na)”, and “患者(Na)” of a sentence “可治輕度的早期心血管系統疾病患者”. Step 1 will remove “可(D)”, “治(VC)”, and “輕度(A)” orderly. And then after removing the segments “的(DE)” and “早期 (Nd)” in step 2. Final, the segments “患者(Na)”, “疾病(Na)”, and “系統(Na)” are ordered to be removed in step 3, and the origin sentence will be replaced with the
segment “心血管”. The matrix A1 and A2 will be decreased their dimension and
became as follow.
S1 = {sfjn | j∈ ,J n∈N}, where sfjn is the frequency of the word j which is
replaced from several infirmities i in the therapy n through stemming. And J is the number of the word j, where J ≤ I.
S2 = {swjn | j∈ ,J n∈N}, where swjn is the influence weight of the word j which
is replaced from several infirmities i in the therapy n through stemming.
4.1.1.3 Medical Ontology (MO) and OWL-S Standard
The Medical Ontology (MO) focuses on medical classification, medical hierarchical architecture, and medical conception retrieval. We use the data of SOHO’s Medical Directory [56] to design MO which includes domain layer, category layer, concept layer, and extended subclass layer shown as Fig. 6. The domain layer represents the domain name (such as “疾病”) of MO and consists of different
categories (such as “內科”, “耳鼻喉科”, “外科”, and so on) defined by domain experts. Each category is made up of several concepts such as “心臟與血管疾病”, “呼吸系與胸部疾病”, “消化系與腹部疾病”, and so on.
We make use of protégé_3_3_beta as the tool for editing MO. The meaning of each slot name resided in MO is defined by OWL-S standard. After building OWL-S document for interpreting the database of medical tourism service, we use the following query in OWLJessKB to tell if the input slot name is the subclass of one class in OWL-S document. For example, based on the OWL-S definition, the input slot name “心絞痛” is the subclass of “心臟疾病”, “心臟疾病” is the subclass of “心 臟與心血管疾病”, and “心臟與心血管疾病” is the concept layer class in “內科”, and “內科” is the category layer class in the domain layer class “疾病”. Those infirmities are presented by hierarchical architecture in OWL-S document.
Figure. 6. The structure of Medical Ontology (MO)
There are multiple layers in MO, and we can get the best through comparing the different results from getting different levels of layer in MO. In this paper, we get the
concept layer in MO to retrieve medical conception to reduce the dimension of matrix.
The matrix S1 and S2 will be decreased their dimension and became as follow.
O1 = {ofkn |k∈ ,K n∈N}, where ofkn is the frequency of the concept k which is
replaced from several words j in the therapy n through MO conception retrieval. And K is the number of the concepts k, where K ≤ J.
O2 = {owkn |k∈ ,K n∈N}, where owkn is the influence weight of the concept k
which is replaced from several concepts k in the therapy n through MO conception retrieval.
4.1.2 Adaptive Medical Tourism Service Inference Module
(AMTSIM)
Adaptive Medical Tourism Service Inference Module (AMTSIM) combines Term Frequency – Inversed Document Frequency (TF - IDF), Latent Semantic Analysis (LSA), and k-Nearest Neighbor (kNN) algorithm to inference adaptable MTS.
4.1.2.1 Term Frequency – Inversed Document Frequency (TF - IDF)
Salton and McGill [22] proposed that in order to decide the importance and representation of a term in a document, the TF in this document and the frequency of this term that appears in other documents can be calculated, which is called IDF. In
this paper, we will calculate the TF – IDF values of matrix O1 into the matrix T.
T = {tfkn |k∈ ,K n∈N }, where tfkn is the TFIDF value of the concept k which is
⎟⎟ ⎠ ⎞ ⎜⎜ ⎝ ⎛ × = × = = ) ( log ) ( ) , ( ) , ( k DF N of k IDF n k TF n k TFIDF tfkn kn ,
where TFIDF(k, n) is the weight of concept k in the therapy n , DF(k) is the
frequency of concept k in the therapy set, and k∈ ,K n∈N .
Final, we also consider the negative influence between infirmity and therapy to calculate the matrix L. Where L = T⊗O2 = {lfkn =tfkn×owkn |k∈ ,K n∈N}
4.1.2.2 Latent Semantic Analysis (LSA)
We then perform Singular Value Decomposition (SVD) to L. The SVD of L is
defined as L = UZV , where U is a Τ K× matrix of left singular vectors, Z is a r
r
r× diagonal matrix of singular values, and V is a r×N matrix of right singular
vectors. )r≤min(K,N is rank of L [34].
The process of dimension reduction is applied to Z by deleting a few entries in it,
and the result of dimension reduction is a matrix Z’ which is a p× matrix. Let Z’, p
where p≤ , be the diagonal matrix formed from the top k singular values, and let r
U’ and V’ be the matrices produced by selecting corresponding columns from U and V. A new matrix, L’, is reconstructed by multiplying three component matrixes, in the sense that it minimizes the approximation errors.
L N n K k lf V Z U L'= ' ' 'Τ ={ 'kn| ∈ , ∈ }≈
4.1.2.3 k-Nearest Neighbor (kNN)
IDF, and LSA through different expert’s suggestions into the matrix L’ for inference. When MUs input their requirement, the SIM will translate the requirement to matrix Q on concept layer by MTSM and MO, and the AMTSIM will use the matrix L’ to classify the requirement into the adaptive therapy by k-Nearest Neighbor (kNN). The algorithm for the kNN is as follows,
i) Let the user’s requirement be a matrix Q on concept layer by MTSM, MO, and
TF - IDF, where Q = {qfk |k∈K }, where qfk is the TFIDF value of the infirmity
concept k, and of(k, q) is the term frequency of infirmity requirement in concept k.
⎟⎟ ⎠ ⎞ ⎜⎜ ⎝ ⎛ × = ) ( log ) , ( k DF N of
qfk kq , if user has infirmity i of concept k.
qfk = 0 , otherwise.
ii) We also consider the negative influence between infirmity and therapy to
calculate the similarity of the matrix Q and matrix L’ as follows,
∑
∑
= = × × × × = × × • • = K k k kn kn K k k kn kn n n n lf ow qf lf ow qf L O Q L O Q L Q sim 1 2 2 1 2 2 ] ) ' ( ) [( ] ' ) [( ' ' ) ( ) ' , (where k∈ ,K n∈Nand 10≤sim(Q,L'n)≤
iii) The requirement matrix Q is assigned to the therapy class through kNN of Q.
4.2 System Architecture
The Situation-Aware Medical Tourism Service Search Subsystem (SAMTS3) is a
five-tier system, shown as Fig. 7, Mobile Users (MUs) can utilize various terminal de-vices that include PC, notebook, Tablet PC, Personal Digital Assistant (PDA), and
mo-bile phone to access Medical Tourism Service Server (MTSS) through various web browsers. The UDDI Registries (UDDIRs) such as JUDDI offer UDDI standard APIs which are Inquiry API and Publication API for Medical Tourism Service Providers (MTSPs) and MTSS (as Service Requests). MTSPs are many kinds of various businesses which provide some Medical Tourism Services (MTS) to publish to UDDIRs. There are Intelligent Agents (IAs) and Model Base System (MBS) in the MTSS. There is the collecting of user’s requirement information, geographical information, and web services cache in the database server. Relevant system functions design as follows. (13) Invo ke S ervi ces (11) G et Serv ices (10) F ind Se rvices Di al og Uni t
Figure. 7. The architecture of Situation-Aware Medical Tourism Service Search Subsystem (SAMTS3)
4.2.1 Mobile Users (MUs)
Tourism Service (AMTS), Customized Service (CS), and Heterogeneous Networks (HN).
4.2.1.1 Adaptable Medical Tourism Service (AMTS)
MUs use mobile device to request their medical requirements to SAMTS3, in
order to carry on the inference of MTS by MTSS using MBS and Semantic Inference Module (SIM). In this paper, Offering relevant adaptable MTS to users who can input difference symptoms in accordance with their situation (such as cardiopathy,
inflammation, and etc.), and SAMTS3 makes MTS reservation automatically
according user request. Model of MBS depend on SIM and Adaptive Medical Tourism Service Inference Module (AMTSIM) to estimate. First, the MBS has been trained by several therapies experts proposed. When MUs input their requirements, the SIM will inference and translate the requirement sentences to be machine readable using stemming and ontology techniques. The AMTSIM offers Term Frequency – Inversed Document Frequency (TF - IDF), La-tent Semantic Analysis (LSA), and k–Nearest Neighbor (kNN) to provide adaptable MTS on different user’s situation by similarity between user’s requirement matrix and symptoms matrix.
4.2.1.2 Customized Service (CS)
While system has user's requirements and situation to provide customized services at time, including different requirements to provide different inference by semantic search engine. MUs will be easier to get relevant MTS information for step by step.
4.2.1.3 Heterogeneous Networks (HN)
MUs use in the different network protocol , so the designing of system lets the ter-minal device or mobile equipment can be integrated services such environments as GSM, GPRS, 3G, wired network, IEEE802.11x wireless network, etc.
4.2.2 UDDI Registries (UDDIRs)
The aim of semantic web is to locate services automatically based on the functional-ities Web services provide. UDDI is helpful to discovery web services with semantic web. Therefore, we use the JUDDI to build UDDI environment which provides Business Entities, Service Entities, Binding Templates, and tModels to represent the detail of business and its services. Services in JUDDI can be searched by name, by location, by business, by bindings or by tModels. However, JUDDI doesn’t support any inference based on the taxonomies referred to by the tModels. Integration of semantic web and JUDDI will solve this problem. And then, Service Retrieval Agent (SRA) can retrieve the detail and relationship of those services in JUDDI by UDDI4J APIs for the semantic inference easier.
4.2.3 Medical Tourism Service Providers (MTSPs)
Medical Tourism Service Providers (MTSPs) build SOAP environment such as AXIS2 to provider some services for user invocation. After building services, MTSPs can publish the information of business, services, and binding templates to UDDIRs. For security, we can modify the AXIS2 API (such as upload.jsp) to build the hash code of service by MD5 algorithm. In this paper, MTSPs which can publish the MTS
to UDDIRs through heterogeneous networks are Thermal Spring Industries, Sulfate Spring Industries, Carbon Dioxide Spring Industries, Salt Spring Industries, and etc.
4.2.4 Medical Tourism Services Server (MTSS)
The Medical Tourism Services Server (MTSS) offers the relevant services of MTS semantic search, those services compose of the Intelligent Agents (IAs) and Model Base System (MBS).
4.2.4.1 Intelligent Agents (IAs)
The IAs proceed such function as collection of the materials, searching, classifying, dealing with work, etc., the work can let users get the most MTS automatically. The in-telligent agent system includes User Interface Agent (UIA), User Requirement Agent (URA), and SRA.
(1) User Interface Agent (UIA)
To know that user’s equipment type, when the users login in and give them the proper webpage.
(2) User Requirement Inference Agent (URIA)
To collect the user’s requirement, such as query, operation, search history, and canned query, the information will be transmitted to the Medical Tourism Service Infer-ence Agent (MTSIA) the DS in order to let the inference engine to analyze and
recom-mend in advance.
(3) Service Retrieval Agent (SRA)
In traditional, the semantic web combined UDDI takes a long time to do the hierar-chical queries such as find_business(), find_service(), find_binding(), and find_tModel(). Therefore, we design the SRA to separate service information of huge quantity in UDDI to the Web Services Cache (WSC) in Database Server (DS), in order to save the time for accessing various UDDIRs by complex queries while MBS analyze the user’s require-ments. SRA which is allowed an accelerated lookup process for finding the best match for users and their requirements is powerful to reduce the UDDI query processes to pro-vide a brilliant performance in the MTS inference. When MBS return the result, SRA will recommend MTS and invoke that service after user’s submission.
4.2.4.2 Model Base System (MBS)
The Model Base System (MBS) includes intelligent deduction engine that uses Data Mining technology to produce the inference. First, the MTS are established automatically by the system, and the Medical Tourism Service Inference Agent (MTSIA) will recom-mend information to MUs for relevant services. The MBS provides MTSIA, Semantic Inference Module (SIM), Adaptive Medical Tourism Service Inference Module (AMTSIM), and other extension modules. The SIM is combined specific Medical Tour-ism Stemming Mechanism (MTSM), Medical Ontology (MO), and OWL-S standard to inference and translate the original sentences to be machine readable. And the AMTSIM uses the TF - IDF, LSA, and kNN to
calculate the similarity and inference the adaptive MTS.
First, MTSIA should be trained to find the relation between infirmities and therapies through SIM, AMTSIM, and training data from experts’ suggestions shown as Fig. 3. SIM will translate each expert’s suggestion to be a machine readable matrix and use MTSM, MO, and OWL-S standard to get conception matrix for inference. Second, AMTSIM will combine TF - IDF and LSA, and conception matrix to increase the accu-racy. Final, when MUs request, user‘s requirement will be sent from MTSIA to SIM, and SIM translate the medical requirement to be a conception matrix for calculating the similarity between user’s requirement matrix and symptoms matrix to find the adaptive therapy by kNN.
4.2.5 Database Server (DS)
The database server includes Web Service Caches (WSC), User Requirement Database (URD), Geographic Information Database (GID), connection module, and control module. The server also offers the web services properties and user requirements to store, and it is a powerful application tool to provide information to MTSS for MTS inference.
4.3 Evaluation and Case Study
In this section, we report our experimental results and implement the architecture and approaches for Medical Tourism Service (MTS) as an example.
4.3.1 Evaluation
There are 46 expert’s suggestions (such as therapy 1, therapy 2, … , therapy 46) in the domain of medical tourism, and those data are collected from several web sites which are all provided by hospitals or doctors. We measure the performance of our approach in the way called k-fold cross-validation [28]. In our experiments, training and testing are performed 46 times (i.e., k = 46). In iteration m, therapy m is selected as the test corpus, and the other therapies are collectively used to train the values for each infirmity.
In this experiment, the feasibility of applying Medical Tourism Stemming Mecha-nism (MTSM), Medical Ontology (MO), or Latent Semantic Analysis (LSA) to MTS requirement classification is evaluated. Table 1 and 2 show classification index in kNN combined with MTSM, MO, or LSA.
Table. 1. Accuracy rate of classification indexes in kNN with MTSM, MO, or LSA
kNN kNN+LSA kNN kNN+ MTSM kNN+ MTSM+ MO kNN+LSA kNN+ MTSM+ LSA kNN+ MTSM+M O+LSA Micro-average 55.24% 60.01% 62.12% 66.59% 72.62% 69.54% Macro-average 50.00% 58.70% 67.39% 58.70% 69.57% 73.91%
Table. 2. Classification indexes in kNN with MTSM, MO, or LSA kNN kNN+LSA class kNN kNN+MT SM kNN+ MTSM+ MO kNN+LSA kNN+MT SM+LSA kNN+ MTSM+M O+LSA Air Bath 50.00% 50.00% 50.00% 75.00% 75.00% 75.00% Thermal Spring 75.00% 75.00% 75.00% 75.00% 75.00% 75.00% Sulfate Spring 50.00% 56.25% 75.00% 50.00% 62.50% 81.25% Carbon Dioxide Spring 41.18% 58.82% 70.59% 52.94% 70.59% 76.47% Salt Spring 60.00% 60.00% 40.00% 80.00% 80.00% 40.00%
Consider kNN first; it can be observed that it’s performances of classification are 50.00%, 75.00%, 50.00%, 41.18%, and 60.00% when the classes are “Air Bath”, “Thermal Spring”, “Sulfate Spring”, “Carbon Dioxide Spring”, and “Salt Spring”. The result shows that kNN algorithm combined MTSM, MO, and LSA to improve accuracy rates. Although, MO does not guarantee the obtained the most adaptable matrix always performs well for the MTS recommendations. For example, when kNN + MTSM + MO + LSA are combined, the performance of “Salt Spring” declines. In
view of macro-average, there is a higher accuracy rate about 73.91% from the best combination of approaches which are kNN + MTSM + MO + LSA. Therefore, we
apply kNN with MTSM, MO, and LSA in SAMTS3.
4.3.2 A Case Study
The MTS reservation as one kind of web services is provided by various kinds of
spring industries on the internet. SAMTS3 helps patients or travelers find adaptable
MTS for their remedy and traveling plans. Generally, users want to find MTS reservation services through UDDI or the current matchmaking for web services. In
SAMTS3, the system will recommend adaptable MTS to MUs. The proposed method
which is shown as Fig. 7 is applied to solve this problem according to the following procedures:
Step 1: Many medical tourism industries will provide their MTS reservation services on themselves SOAP site (such as AXIS2) and publish the information of those services which include company name, therapy name, location, price, and etc. to UDDIRs based on JUDDI.
Step 2: When MUs inquire the SRA for the adaptable MTS through UIA, they send their requirements as a part of the request.
Step 3~4: The UIA will send the MUs’ requirement to URIA. For example, MUs input their infirmities “心臟病, 手腳冰冷, 胃腸功能障礙, 風濕症, 神經衰弱, 高 血壓, 腎臟病, 過敏疾病, 慢性疾病, 酸痛, 關節炎” in PC and PDA to request
SAMTS3 shown as Fig. 8. and Fig 9. URIA supported the processes include lexical
analysis will check and store user’s information in URD for inference user’s requirements.
Figure. 8. Request SAMTS3 in PC version Figure. 9. Request SAMTS3 in PDA version
Step 5: The URIA will get user’s requirement from UIA and send it to MTSIA. When MTSIA receives the user’s requirement inference result from URIA, it will control and coordinate various modules in MBS.
Step 6: The SIM will inference user’s affinity information by MTSM and MO according to user’s requirement from URIA. The text preprocess of SIM can segment the requirement to original word-by-document matrix which will be replaced with concept layer matrix Q by MTSM, MO, and Term Frequency – Inversed Document Frequency (TF - IDF). For example, the segment “心臟病” of this requirement will be presented the concept “心臟與血管疾病” and matrix Q is computed the TFIDF value of the concept “心臟與血管疾病”.
Step 7: To search the adaptable MTS, the AMTSIM will find the adaptable MTS class by LSA and kNN. The kNN will combine the negative influence weight value to calculate the similarity between matrix Q and trained matrix L’ to get the most similar class such as “Carbon Dioxide Spring”. We can find the adaptable MTS class through
those processes above and MTSIA will sent this message to SRA for retrieval related MTS.
Step 8~11: The SRA holds up-to-date information on offers currently available for MTS which have been requested recently. To keep offer lists up-to-date, the SRA inquires the one or more UDDIRs periodically regularly in order to check, find, and get for new offers. When SRA receives the MTS class from MTSIA, SRA will query the Web Service Cache (WSC) and Geographic Information Database (GID) to get adaptive MTS.
Step 12~13: SAMTS3 returns the result and recommends the adaptable MTS to
MUs shown as Fig. 10. and Fig 11. If MUs agree this suggestion, SAMTS3 will make
those MTS reservation automatically.
Figure. 10. Response SAMTS3 in PC version Figure. 11. Response SAMTS3 in PDA version
Step 14: MUs will pay the money to get MTS reservation which are booked MTS tickets and get those tickets and bills.
4.4 Discussion
Recently there has a few medical tourism systems developed which mostly focus on location-aware service but no medical tourism service recommendation in Taiwan. In this research, we proposed a Situation-Aware Medical Tourism Service Search Subsystem, which provides user’s requirements inference and relative services search by semantic inference engine and find the most adaptive Medical Tourism Service. We discover the accuracy of the MTS inference is higher by combining Medical Tourism Stemming Mechanism, Medical Ontology, Term Frequency – Inversed Document Frequency, Latent Semantic Analysis, and k-Nearest Neighbor. Mobile users can conveniently obtain customized MTS and decision to get and use those
services according to their health requirement in advance by SAMTS3.
In the future, the MO can be modified to store more levels of medical tourism and other domain knowledge. For requirement classification, the similarity is computed by different level of MO to get adaptable medical tourism service from
different industries. Otherwise, SAMTS3 only focus now on Medical Hot Spring and
can be integrated with more MTS inference for Psychotherapy, Mini Beauty Surgery, Premium Health exam, Dentistry, Ophthalmology Laser, and etc.
Chapter 5 Healthy-life Map Guiding Subsystem (HMGS)
Healthy-Life tourism services are the trend of the future. In this paper, we propose an effective decision support system (DSS), the Healthy-life Map Guiding Subsystem (HMGS), which provides the introduction and commentaries of Healthy-Life scenic spots with relevant recommendation. HMGS is a three-tier system composed of the Clients, Multimedia Application Server (MAS), and Database Server (DS) to provide the introduction and commentaries of Healthy-Life scenic spots with relevant recommendation. HMGS is combined with an automatic text summarization technology to provide summary of commentaries. Finally, HMGS provides the adaptable tourism path in 3D web geographic information system for user reaching the Healthy-Life scenic spots [5].5.1 System Design Principles
In this paper, the design of the Healthy-life Map Guiding Subsystem (HMGS) provides functions which are Blog Content Retrieval Agent (BCRA), Multiple Document Summarization (MDS), and etc.
BCRA searches the blog information from Google Blog Search and Yahoo Blog Search, and it finds the comment about the merchandise in blogs and store the Crawl and Parse into Blog Corpus. Finally, HMGS use Multiple Document Summarization technology which provides the introduction and commentaries of Healthy-Life scenic spots with relevant recommendation. Users can use the system interface to query relevant information. Overall system processes are shown as Fig. 12.
Figure. 12. The Process of Healthy-life Map Guiding Subsystem (HMGS)
5.1.1 Blog Content Retrieval Agent
The Blog Content Retrieval Agent (BCRA) provides functions which are Fuzzy Search, HTML Crawler, HTML Parser, and etc. The functions are shown as follows.
(1) Fuzzy Search
Fuzzy search provides fuzzy computing and judge. It establishes the keywords corpus and uses the terms in corpus to search the articles in blog by Google Blog Search or Yahoo Blog Search.
(2) HTML Crawler
HTML Crawler is used to create a copy of all the visited web pages for later processing by a fuzzy search. In this paper, HMGS uses the results of Google Blog Search in various Blogs and track related page link that HTML content will be saved.
(3) HTML Parser
The HTML Parser analyses the HTML tag generated from HTML Crawler to get the key information. After that, it would remove the relevant special characters (such as single quotes and double quotes), and avoid attacks. Finally, we would establish Blog Corpus to get the summarization of multiple documents which is providing relevant recommendation.
5.1.2 Multiple Document Summarization
Healthy-Life Map Guiding is combined Multiple Document Summarization technology to summarize automatically the various health-related spot blog comments in real-time and reduces the amount of information effectively. So that users can quickly browse the tourist of consumers’ point of view and the past experience.
The Multiple Document Summarization uses MEAD package in our system. The relevant good comments in the Blog corpus is inputted into the MEAD modules which are (1) Preprocess, (2) Feature Selection, (3) Classifier, (4) Reranker, and (5) Summary to get text summarization automatically. The processes are shown as Fig. 13.
5.1.2.1 Preprocess
In first step, preprocess would transfer the format of original HTML documents from blog. And then, set the documents ID and Sentence ID sequentially in order to carries on the weight of sentences in each document and the summary production.
Figure. 13. MEAD’s automatic text summarization process
5.1.2.2 Feature Selected
After that, HMGS uses several features which are (i) Centrality, (ii) Sentence Length, and (iii) Position to calculate the weight of each sentence.
(1) Centrality
I use the Vector Space Model (VSM) to carry out the similarity calculation. The maximum cosine value represents the centroid vector of the cluster. The following expression used to calculate the value of the sentence s.
sentences other in keywords s in keywords sentences other in keywords s in keywords s ScoreCentrality( ) ∪ ∩ = (2) Sentence Length
If the length of the sentence is above a given threshold to be 1. Otherwise, the sentence length is 0. The following expression used to calculate the value of the sentence length. ⎩ ⎨ ⎧ ≤ > = n s Length n s Length s ScoreLength ) ( , ) ( , 0 1 ) ( (3) Position
Position is to give the weight by the position of the sentence in the document. For the weight is divided into 10 levels: 0-9. 0: the sentence does not belong to a summary; 1-9: the sentence belongs to a summary. The importance: 1 is weakest, and 9 is strongest. The following expression used to calculate the value of the position of the sentence in the document.
0 . 9 ) | ( ) ( i i Position Position of Average Position S s P s Score = ∈ ×
5.1.2.3 Classifier
In the third steps, we select some important features to set the different weight according to those features. We summarize those features and their weight to calculate the score of each sentence.
If Centrality weight is w1, Position weight is w2. The expression shows as follow. ) ( )] ( ) ( [ )
(s w1 Score s w2 Score s Score s
ScoreOverall = × Centrality + × Position × Length
5.1.2.4 Reranker
In this step, we would judge the correlation in sentences which is decreased the redundancy. Next, we would set the threshold for filtering, and set the compression to extract.
5.1.2.5 Summery
After that, we get the extract from Reranker and map the extract to summarize from Document ID and Sentence ID in this step of preprocess. Finally, Multiple Document Summarization technology provide summary of commentaries for users.
5.2 System Architecture and Implementation
Healthy-life Map Guiding Subsystem (HMGS) is three-tier architecture. Fig. 14 shows that user can access the data in the Multimedia Application Server (MAS) and the Database Server (DS) via personal computer, laptop, and tablet PC. MAS provides intelligent agents, multiple document summarization, real-time multimedia stream service and 3D web geographic information system. DS includes the blog corpus database, Healthy-Life scenic spots information, geographical information and the multimedia file database. The system functions are shown as follow.
Figure. 14. The architecture of Healthy-life Map Guiding Subsystem (HMGS)
5.2.1 Clients
The user can acquire (i) the tourist introduction of every Healthy-Life scenic spots, (ii) the relevant comment of every Healthy-Life scenic spots, and (iii) the virtually geographical guide. This paper retrieve the relevant comments from blogs via Blog Content Retrieve Agent (BCRA), and provide the scenic spots comment summaries via Multiple Document Summarization, plus the route guiding service, to help user make their tourist decision.
