A WEB/WAP-BASED SYSTEM FOR REMOTE MONITORING PATIENTS WITH DATA MINING SUPPORT. Petros Daras, Dimitrios K. Bechtsis and Michael G.

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1 A WEB/WAP-BASED SYSTEM FOR REMOTE MONITORING PATIENTS WITH DATA MINING SUPPORT Petros Daras, Dimitrios K. Bechtsis and Michael G. Strintzis* Informatics and Telematics Institute, 1st Km. Thermi-Panorama Road, Thermi-Thessaloniki, Greece, Tel.: , Fax: , ABSTRACT The objective of this paper is to present an experience in the challenge of using Internet and mobile Internet technologies for the development of a Web/WAP (Wireless Application Protocol)-based medical application with Data Mining support. This medical application is focused on the development, diffusion and use of the technology in response to specific domain needs of medical experts in the area of Cardiology, especially for the patients after by-pass operation. Keywords: Remote monitoring patients, WAP medical applications, Web medical applications, Data mining, Clustering. 1. INTRODUCTION The cost of medical care services is a worldwide interest issue. The medical services today, impose the employ of doctors, nurses and the construction and maintenance of hospitals. The principle to reduce the cost of the medical services, relies on the categorization of all the stages of the offered medical services as well as on the propulsion of a great number of incidences to new doctors, nurses and patient consultants [1, 2]. Characteristic applications are the NHS-direct in England, the telephone system for access to medical services in U.S.A., telemedicine programs supported in National and International level as well as a number of web sites that offer relevant information all over the world [2, 3]. It has been proved that the formation of the medical services has provoked significant improvement to the level of quality, efficiency and effectiveness. In spite of the development, the incorporation of new medical systems to the existence procedures of medical services has enormous potentiality for research and development [4]. This work was supported by the Technologies for Remote Monitoring Patients and by the WAP Medical Services, projects of the Greek Secretariat of Research and Technology. The problems that derive from the present medical systems can be summarized below: ffl The offered medical services are limited to national level. Doctors from different countries are using different methods to treat patients, according to the education, religion, social policy etc. ffl Web sites are limited to supply static information, have lack of interaction with the patients as well as with the doctors and the medical staff. ffl The advantages of the research and development of the technology in image and video transmission over the Internet and the interactive applications, have not yet fully researched, exploited and incorporate with the medical research as well as with the supply of medical services. The goals of medical care in these cases are to control the disease processes and to help patients maintain their independence and maximum level of function within their own homes and communities. The scope of medical care includes not only diagnosis and medical treatment but also patient education in self-care and prolonged medical monitoring and supervision. Patients must learn to perform a wide variety of medical tasks that have only been performed by trained medical personnel in the past. Such tasks can range from a diabetic patient who checks his/her blood glucose level 2-4 times a day and adjusts the insulin dose appropriately, to the patient with a pacemaker who learns to use the equipment to send an electrocardiography rhythm strip over the telephone lines to the local physician s office or to a distant monitoring center. In this paper, we present a Web/WAP-based medical system that provides medical telemonitoring and telecare facilities for patients after a by-pass operation. During the design of the system special emphasis has been given in the following:

2 Figure 1: System Architecture. ffl Openness: the system relies basically on commercially available software components based on the opensystem model. Modularity and scalability have therefore been guaranteed so that with the rapid technological advances any subsystem can be easily replaced as soon as a new version with higher performance appears on the market. ffl Extensibility: the system can easily expand to cover all the hospital spectra. Every physician would have his own Web/WAP portal for managing his patients. ffl Usability: the simple interface provides a friendly environment both for the patient and for the physician. In the following section the system architecture and the system description are presented. In Section 3 the Entity- Relationship (E-R) data model is given. The Use Case Diagrams of the UML (Unified Model Language) model of the system are provided in Section 4. The Data Mining procedure is described in Section 5. In Section 7 the Web/WAP interfaces are presented. The implementation specifics issues are given in Section 6. Finally, conclusions are drawn in Section SYSTEM ARCHITECTURE The system consists of three major entities: the entity patient, the entity doctor and the entity administrator (Figure 1). Each entity interacts with both the Web and the WAP gateway; offering great flexibility to the system. The model of the information flow in the system enables the continuous feedback about the patient s status. The entity patient is responsible for the collection of the medical data from the patients. Images, text documents, lab results and effectively designed questionnaires provide to the user a patient oriented interface. A video camera can focus on the patient monitoring his condition. Furthermore, the patient has the Figure 2: Database Architecture. ability to communicate with the doctors using videoconference. The entity doctor is responsible for the evaluation of the collected data and the examination of patients. The entity administrator is responsible for the maintenance of the system. The database of the system, the safe transfer of medical information and the continuous monitoring of the workflow are some of the administrator s responsibilities. The privileges of these three entities differ significantly and that is the reason for the generation of three distinguishing views. There is the doctor s subsystem, the patient s subsystem and the administrator s subsystem. 3. DATABASE ARCHITECTURE The Entity-Relationship (E-R) data model views the real world as a set of basic objects (entities) and relationships among these objects. It is intended primarily for the Database design process by allowing the specification of an enterprize scheme. This represents the overall logical structure of the database (Figure 2).

3 4. UML MODELLING AND USE CASE DIAGRAMS In order to obtain a better understanding of the system the Use Case Diagrams of the system are provided. ACTOR: An Actor defines a coherent set of roles that users of an entity can play when interacting with the entity. An actor may be considered to play a separate role with regard to each use case with which it communicates. In the metamodel, Actor is a subclass of Classifier. An Actor has a Name and may communicate with a set of Use Cases, and, at realization level, with Classifiers taking part in the realization of these Use Cases. An Actor may also have a set of Interfaces each describing how other elements may communicate with the Actor. An Actor may have generalization relationships to other Actors. This means that the child Actor will be able to play the same roles as the parent Actor, i.e. communicate with the same set of Use Cases, as the parent Actor. Figure 3: Web-Use Case Diagram. USE CASE DIAGRAMS: Early requirement analysis also involves the Use Case diagram which identifies the highlevel system functionality (processes) required (equivalent to function points). The Use Cases are created after cooperation with users, in order to identify a large number of scenarios (specific instances of interactions by actors). From the scenarios, those with common elements can be identified and use cases can be extracted. Use cases are like classes and scenarios are like instances. At this stage of analysis we are interested in essential Use-Cases - highlevel Use Cases that are not tied to any particular implementation or technology. The Figures 3, 5 depict all the Use Cases of the proposed system. The description of these Use Cases is shown in Figures 4, DATA MINING The World Wide Web is now undeniably the richest and most dense source of information the world has ever seen, yet its structure makes it difficult to use that information in a systematic way. The Web/WAP portal is able to gather information from patients regardless of their location. Data mining is useful for discovering and outlining hidden patterns in the database. As the data in the database expand as result of the wide use of the portal, it becomes difficult to find information manually. Data mining provides algorithms, which allow automatic pattern discovery and interactive analysis. Data mining has two basic goals: prediction and description. Prediction includes the use of parameters, in our case the use of database records, in order to predict unknown or future values of a variable. Description focuses on finding pattern models, which categorize data via clustering. In clustering data are divided in groups. Each record is assigned to a specific group according to the training technique and the training data set. In comparison with the classification technique there are no predefined groups; records are classified taking into consideration similarity criteria only. The use of the MS SQL Server 2000 for the data mining procedure is outlined above. The administrator can set up a data-mining model in Analysis Services- Data Mining that trains data. In this section the data-mining model uses the Microsoft Clustering algorithm to divide the patients into ten clusters. The Microsoft clustering algorithm is a scalable implementation of Expectation-Maximization (EM) algorithm [5]. Unlike distance-based algorithms such as K-Means, EM constructs proper statistical models of the underlying data source and naturally generalizes to cluster databases containing both discrete-valued and continuous-valued data. The scalable method is based on a decomposition of the basic statistics the algorithm needs: identifying regions of the data that are compressible and regions that must be maintained in memory. The approach operates within the confines of a limited

4 main memory buffer and requires at most a single database scan. Data resolution is preserved to the extent possible based upon the size of the main memory buffer and the fit of the current clustering model to the data. In case when there are too many attributes involved in training (e.g., more than 255 attributes by default), a feature selection method, is applied, to filter out less interesting attributes. The interestingness of an attribute is calculated based on the entropy of the attribute found in [6]. Fields, which measure the information used by the data-mining algorithm, are selected in order to analyze and exploit the records of the database. Our scope is to classify the patients into groups according to their current health care condition, in order to assure the doctors awareness. The input fields are presented at the left of the picture (Figure 7). The ten clusters divide the patients into ten different classes. The color of the cluster specifies the density of the results, the darker the color - the higher the density for a specified cluster. At the right, we are able to view the Node details for each cluster. 6. IMPLEMENTATION SPECIFICS The presented system was developed using the Hypertext Markup Language (HTML) [7] and the Hypertext Preprocessor (PHP)[8] for the manipulation of the dynamic pages. The WAP part of the system was developed using the Wireless Markup Language (WML) [9]. In order to achieve maximum safety, the Secure Sockets Layer protocol (SSL) [10] was used, with a 56-bit key. Finally, the database and the data mining procedure were created using the MS SQL Server 2000 and the MS Analysis Manager respectively (is part of the MS SQL Server 2000). 7. WEB/WAP INTERFACES In this paper a Web/WAP-based medical system was presented. After a short introduction, the proposed environment and the underlying architecture were described. According to the above mentioned analysis it is clear that the proposed system is a user friendly, cost-effective and powerful tool. It implements the concept of care at the point of need in co-operative environments to provide the continuity of patients care through simple Web and WAP interfaces and, also, ensures the privacy and the confidentiality of the patients. Finally, it uses the Analysis Manager of the MS SQL Server 2000 in order to divide the patients into clusters. Its open and modular architecture makes the proposed system easily adaptable to many new patient categories. 9. REFERENCES [1] E. A. Balas and I. Iakovidis, Distance Technologies for Patient Monitoring, BMJ Publishing Group, vol. 319, November [2] F. Magrabi, N. H. Lovel, and B. G. Celler, A Web-based Approach for Electrocardiogram Monitoring in the Home, Int. Journal of Medical Informatics, no. 54, pp , [3] E. A. Balas, Electronic Communication with Patients: Evaluation of Distance Medicine Technologies, JAMA, no. 278, pp , [4] K. Nakamura, T. Takano, and C. Akao, The effectiveness of videophones in home healthcare for the elderly, Med Care, no. 37, pp , [5] P. Bradley, U. Fayyad, and C. Reina, Scalling EM (Expectation Maximazation) Clustering to Large Databases, Microsoft Tech. Report MSR-TR-98-35, [6] H. Liu and H. Motoda, Feature Extraction, Construction and Selection: A Data Mining Perspective, Kluwer Academic Publishers, [7] HTML, Hypertext Markup Language, [8] PHP, Hypertext Preprocessor, [9] WML, Wireless Markup Language, [10] SSL, Secure Sockets Layer Protocol, A patient can access the application using either a Personal Computer or a cellular phone. The patient is able to send his/her medical data via WWW by completing the associated forms (Figure 9), or via his/her cellular phone (Figure 8) and furthermore, to view the doctor s replies and messages. The doctor s subsystem is responsible for managing the medical data. The doctor is able to browse the data and check the patient s condition. The system can support the doctor s effort by posting up alerts whenever a patient s health is in a critical position. A critical condition can be identified either by data mining techniques or by simple constraints. The alerts generated by the system are shown in Figure CONCLUSIONS

5 Figure 5: WAP-Use Case Diagram. Figure 4: Description of Web-Use Cases. Use Case Name Description Read Medical News No registration is necessary. Actors can simply view medical articles. User Log In The user provides the system with user name and password in order to log in. The Log In Use Case includes User Registration and User Acceptance use cases. Session Creation Every time a user logs in the system a session is created. Send Medical Data A patient is able to send to the medical center medical images, documents, videos, lab results etc. Send Diagnosis A d octor sends his diagnosis after examining the patient s health condition. Fill Questionnaires A patient fills the questionnaires in order to inform the medical staff. Send Alert The doctor is able to send an alert to the patient in order to inform him a bout a critical condition. Evaluate Medical Data This group represents the evaluation of medical data. Doctors and the medical staff must evaluate the posted medical data. Evaluate Questionnaires Essential to the system is the evaluation of the questionnaires. Medical Data Management Under this group subsist some basic functionalities, which enable the viable character of the system. Manage Database The administrator of the system is able to update, search and retrieve the databases information. Generate Alerts The data management system with the aid of rules is able to identify a problem and therefore notify the doctor. Figure 6: Description of WAP-Use Cases.

6 Figure 9: Sending data using Web. Figure 7: Data Mining using MS SQL Server Figure 8: Sending data using WAP. Figure 10: Patients critical condition alerts a doctor.