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A New Hybrid Agent-Based Modeling & Simulation Decision Support System for Breast Cancer Data Analysis Amnah Siddiqa, Muaz Niazi*, Farah Mustafa, Habib Bokhari, Amir Hussain, Noreen Akram, Shabnum Shaheen, Fouzia Ahmed and Sarah Iqbal s

*Corresponding author Abstract—In this paper, we present a novel technique of building hybrid decision support systems which integrates traditional decision support systems with agent based models for use in breast cancer analysis for better prediction and recommendation. Our system is based on using queries from data (converted to a standardized electronic template) to provide for simulation variables in an agent-based model. The goal is to develop an ICT tool to assist non-specialist biologist researcher users in performing analysis of large amounts of data by applying simple simulation techniques. To demonstrate the effectiveness of this novel decision support system, an extensive breast cancer data collection exercise was carried out with the support of Hospitals in a previously unexplored region. The collected data was subsequently integrated in an electronic medical record filing system for patients. We also demonstrate the application of agent based modeling and simulation techniques for building simulation models of tumor growth and treatment. Our proposed decision support system also provides a comprehensive query tool which facilitates the use of retrieved data in statistical tools2 for subsequent interpretation and analysis.

through a significant learning curve. Agent-based modeling offers such a paradigm, which is very convenient to use. It has found its place in a number of domains, ranging from social simulation of researchers [1] to modeling and simulation of complex networks[2] and even web access discovery in Ambient Assisted Environments [3]. We demonstrate the effectiveness of using this technique, in conjunction with a query tool linked to normalized and standardized breast cancer data, to build a new hybrid decision support system. In this paper, we present the following innovations: 1.

2. 3.

I. INTRODUCTION

A

NALYSIS and making decisions based on complicated data can be a daunting task for non-specialist users. Occasionally, it requires the use of sophisticated techniques,, ranging from statistical models to complex inference mechanisms. The classic users of such systems are highly trained in relevant technical skills required for the domain. However, they also, typically lack the ability to develop complex computer programs to simulate and visualize what they are seeing from the data. Therefore, they find it more comfortable to use tools and techniques, which are easier for them to model based on their inherent perceptive ability acquired from being human and living a social life. Thus, if there were such a technique, it could be learnt without going Manuscript received March 16, 2009. Amnah Siddiqa, Dr. Farah Mustafa, Dr. Habib Bokhari, Noreen Akram, Fouzia Ahmed and Sarah Iqbal are with the Department of Biosciences, COMSATS Institute of IT, Islamabad, Pakistan ([email protected], [email protected], [email protected],[email protected], [email protected], [email protected]). Muaz Niazi and Dr. Amir Hussain are with the Department of Computing Science and Mathematics at the University of Stirling, Scotland, UK. (Tel: +92 (321) 5310906, email: [email protected] and [email protected]) 2 For example, Epi Info ™ and Stata

4.

We have used standardization techniques from a variety of current paper data formats to electronic data templates. An ICT user-friendly query tool for retrieval of normalized breast cancer data. An agent-based simulation decision support system developed to assist non-specialist biologists in the analysis of data. This tool also provides inputs to agent and multi-agent based simulation models. Thus, it helps the users make inferences usually not possible to make, with only the micro view exposed by the use of simple visual techniques to assess the what-ifs of the model. To demonstrate the suitability of this approach, we conducted a comprehensive data gathering exercise, which we include as a case study. The breast cancer data collection was performed from scratch.

The remaining paper is structured as follows: Section II provides background and discusses some related work. It is followed by Section III, which formally presents the problem statement. Section IV describes the methodology used in the design and development of the system. Section V gives the details of the case stud., Subsequently, Section VI discusses relevant implementation aspects. Finally, some concluding remarks are given in Section VII.

II. BACKGROUND AND RELATED WORK A. What is a Decision Support System? Decision Support Systems (or DSS) are a class of computerized information systems that support decisionmaking activities. They are typically computer-based

interactive systems and occasionally, subsystems, intended to help decision makers use various tools such as communications technologies, data, documents, knowledge and/or models to assist in making decisions. There are various kinds of decision support systems which include but are not limited to the following: Communication-driven DSS [4], Data-driven DSS [5], Documentation-driven DSS, Knowledge-driven DSS [6], Model-driven DSS [4] and Simulation-based DSS[7]. Because of shortage of space, we cannot provide a comprehensive review. However, the interested reader is referred to review these sources. B. Decision Support Systems in Biological Research DSS has long been considered an ideal tool for dealing with tough and complex decision making in all fields, especially in fields related to biological research. Decision Support Systems, that are specifically designed for generating casespecific advice to assist clinicians belong to a special class of DSS known as Clinical Decision Support Systems (CDSS). CDSS have almost 40 years of history--from the first generation CDSSs such as MYCIN [8] and QMR [9], to the second generation Protégé [10], and the very recent CDSS such as those for lower back pain diagnosis[11]. Two ongoing research examples are described in [12].

III. PROBLEM STATEMENT A. Problems in existing DSS for breast cancer analysis in the domain of clinical research. Researchers need to look at certain trends in data for better diagnosis of disease, prognosis of treatment, and better guidelines for prevention of disease. For example, in the present study, prevalent trends about important risk factors of breast cancer, i.e., age, weight, height, BMI, menopausal status, etc., in a particular population are needed to be viewed in one picture. This is needed to enable the user to make better judgments of what he is observing. Thus, a system is needed for biological researchers, and hopefully extensible subsequently to assist even clinicians in making tailored informed recommendations for their patients of a particular population. The shortcomings faced by researchers for their analysis etc., at present include lack of user friendly interfaces, and learning complicated statistical tools for not only analyzing the data but also interpreting the data. Therefore, there is a need to use tools which are more intuitive and user friendly, such as agent-based modeling and simulation techniques.

B. Our solution to the problems Agent-based modeling (ABM) is a powerful method of testing the collective effects of individual action selection.

More generally, ABM allows the examination of macrolevel effects from micro-level behavior. Many people view formal analytic models as preferable to ABM, but there are several reasons to prefer ABM. First, even formally correct models can be wrong if their premises or assumptions are incorrect [13]—thus ABM with its more experimentally-oriented approach can actually help verify a valid model. Second, ABM is sometimes more accessible or intuitive. Such models can consequently play an important role in advancing scientific understanding, including developing a formal analytic understanding of a system by helping explore the space of possible solutions [14]. And finally, there are large classes of dynamic systems that are not amenable to closed analytic solutions [15]. Particularly interesting to biologists are those systems involving openended co-evolution of multiple interdependent species. In this paper, we focus on designing a hybrid approach employing a data driven DSS integrated with agent based models for breast cancer treatment and disease spread in order to provide sophisticated and reliable recommendations efficiently and with a user friendly interface. C. How agent based modeling and simulation helps nonspecialist users? The contribution from agent-based computing to the field of computer simulation mediated by agent-based simulation systems (ABSS) is a new paradigm for the simulation of complex systems involving much interaction between the system entities [16]. As ABSS and other micro simulation techniques explicitly attempt to model specific behaviors of particular individuals. They may be contrasted to macro simulation techniques that are typically based on mathematical models where the characteristics of a population are averaged together. Thus, the model attempts to simulate changes in these averaged characteristics for the whole population. In macro simulations, the set of individuals are viewed as a structure that can be characterized by a number of variables, whereas in micro simulations the structure is viewed as emergent from the interactions between the individuals. Parunak et al. [17] have compared these approaches and pointed out their relative strengths and weaknesses. They concluded that "...agent-based modeling is most appropriate for domains characterized by a high degree of localization and distribution and dominated by discrete decision. Equationbased modeling is most naturally applied to systems that can be modeled centrally, and in which the dynamics are dominated by physical laws rather than information processing." One of the most famous tools used in agent based simulation is NetLogo that is available freely for download [18]. It is based on the Logo language, which is extremely easy to use and is even used to teach programming to kids.

D. Need for standardization of electronic data It is unreasonable to demand non-specialist users to convert data to standardized templates. In newly investigated areas, it was discovered that no single (or common) standard is followed by medical practitioners for data collection. In addition, non-specialist biologists are also not very conversant in application of data normalization techniques. Hence, there is a compelling need to standardize the existing data input formats to conform to both the collected data as well as to the end-users (biologists/clinicians) of the decision support system. One idea behind this study was also to design standard electronic templates for data input as shown in screen shot below (Fig. 1) and then trickle down this data to simple queries so that the end-users can easily access these end numbers to insert in custom-built agent based models in NetLogo [18]. This would be followed by making decisions on the what-ifs of the dynamics of the complex models of cancer progression inside the individuals and outside of the populations.

prediction in populations significantly larger than the sample data but with similar prevalent epidemiological trends. By integrating agent-based models with knowledge-based DSS, this hybrid system can assist clinicians and researchers in sophisticated and reliable tailored recommendations of variables to be used in the Agent Based DSS. Towards this end, our first goal was to research and develop an approach to design some standards for managing and interpreting patients’ data from all over the world. For this purpose few 1st generation tools have been built, that facilitate particular domains with different versions in different regions, such as smart forms for medical records etc.[19]. Furthermore, we can add some statistical modules to our DSS in order to assist clinicians in better analysis and interpretation of the data and for providing better guidelines to patients without using/learning complex software. These tools can be improved further for providing more precise and reliable results. Our second task was to integrate this knowledge-based DSS with agent-based simulation models to assist clinicians in diagnosis and prognosis, and to aid researchers for not only mining useful information from these bio-intelligent applications for better interpretations and results, but also overlapping of this mined information with other knowledge based applications. This is especially important since dissemination of new knowledge discovered from genomics and proteomics data mining applications needs to be integrated with clinical forms of knowledge to develop more innovative and tailored applications. IV. METHODOLOGY A. How can agent based modeling and simulation be integrated with decision support systems?

Figure 1: Electronic Data Specifically, in this paper an effective hybrid system has been developed by the integration of agent-based simulating models and knowledge driven DSS. This hybrid decision support system performs the following major tasks: 1.

Demonstrates the standardized conversion process of data from medical practitioners to DSS to endusers (biologists).

2.

Provides tools for easy analysis of data by trickling data down to results of queries.

3.

Allows use of an easy to use agent-based tool, NetLogo, from the domain of agent-based modeling and simulation for use with conventional DSS tools to evaluate strategies for drug treatment and

The current decision support system aims to benefit nonspecialist users by providing easy access to clinical data as well as ease of application of agent-based modeling and simulation techniques. Initially, standard electronic data can be stored into the database. Subsequently, the DSS can be used in order to extract user significant data by applying queries that enhance the analytical ability of the system. As the tool fetches these filtered figures from DSS, agent based models are developed for scrutinizing and predicting data trends. Figure 2 below reviews the ordered steps of this novel methodology for better understanding. The first step is digitizing data using standard formats since the data may not be in electronic format, especially in developing countries like Pakistan where paper data storage trends are still prevalent. In the next step, the data is filtered to reveal desired results using DSS. The DSS provides a user friendly interface without requiring the learning of complicated techniques to make use of it. In the last step of the

methodology, these DSS filtered numbers are then fed into agent-based models, simulating this data for use by nonspecialist users.

treatment and prevention of the disease. For example, in our case, the doctor can guide the patient according to the risk factors such as body-mass index, addictions, diet and lifestyles, work profiles, etc., that may be prevalent in our population.

Module 1: Data Manager: Converts data from paper format to electronic format.

Module 2:

Module 3: Hybrid system with integrated Module 2 and agent-based models: Simulates filtered data from DSS using these models, to assist nonspecialist end users in understanding data trends.

To summarize, extensive data collection was performed; patients’ data piled in paper formats were digitized first (as illustrated in Fig. 3). After digitizing this data in a standard form, DSS was designed to filter significant results and assist non-specialist users for better analysis of data. In the next step of the system, this DSS was integrated with different intuitive agent based simulating models for assisting non specialist end users (biologists) without requiring them to undergo complicated learning curves.

VI. SYSTEM IMPLEMENTATION DETAILS A. Querying for data

DSS Designing: Filters useful data from Figure 2: Case Study

In this study, we collected data for certain risk/ epidemiological and clinical factors of breast cancer, such as tumor grade, family history, previous medical history,

V. CASE STUDY Data was collected from breast cancer patients from two major onco-referral hospitals, specifically the Bahawalpur Institute of Nuclear Medicines and Oncology (BINO) and the Nuclear medicine and Oncology Radiotherapy Institute (NORI), Islamabad. As illustrated in Fig. 3, the data was originally in paper format which was digitized based on different citations and oncologists’ guidelines. The need to develop a standardized template was felt at this point as we had started from scratch in order to convert this paper-based data, which was not only difficult to read but also had some missing history details, due to lack of a standard input format. Hospital records can be grouped in two parts: one group comprising billings payments and the other group comprising data relating to tests conducted, diagnosis, history, on-going treatment, present conditions, etc. The latter group is important for conducting analysis and mining useful information from data, as well as for assisting clinicians to give tailored recommendations with less errors and better guidelines to patients for future health care. Furthermore, this information can be used for developing better healthcare policies by interpreting disease trends across populations. For example, different queries can assist the doctor in prognosis of treatments where clinicians can take a look at overall trends in data. In this case, a particular histological type of disease treatment type is mentioned for the present data or else for a particular grade/tumor size/tumor type (benign or local) treatment might vary. Additionally, these queries can assist doctors in providing better guidelines for

Step1: Data collection from Scratch; was converted from dirty paper format to electronic format.

Step 3: This filtered data is beneficial not only for analysis but also for utilizing in agent based simulating models in order to assist non specialist end users (biologists).

Step 2: This data was trickled through different queries to obtain useful results; as shown in figure 

Figure: Methodology receptor statuses, menopausal history, testsImplementation that patients Figure 3: Case Study

underwent during treatment, treatment dosages, etc. (as shown in Fig. 3). Next we applied a large number of

database queries using the SQL language in order to trickle down useful data which would not only support biologists for better analysis of breast cancer, but could also be used in agent-based models to simulate trends of the disease without having to be a specialist of sophisticated statistical and simulating tools. Screen shots and details of a selected couple of queries are described below: Query#1: In this query (shown in Fig. 4), when a particular tumor grade (0, 1, 2, 3, 4) is selected, the frequency of patients undergoing specific treatment types such as either adjuvant or neo-adjuvant or both is displayed with respect to that tumor grade.

Figure 4: Query I

In the example of the tumor growth model, our inputs include parameters such as treatments given (chemotherapy, radiotherapy, and hormonal therapy) on the number of tumors, etc., to determine the effectiveness of the particular therapy on tumor growth and numbers. Figure 6 shows the interface for NetLogo program GUI. The starting number of tumors, cycles of chemo, radio, and hormonal therapies, can all be adjusted according to the disease’s severity. In Fig. 8, we see even more growth. However, the effects of one or more therapies are observed either at the same or separate times. VII. CONCLUSION AND FUTURE WORK In this paper, we have developed a hybrid decision support system based on a novel methodology of using agent-based modeling and simulation tools as a black box. The entire system is based on rigorously collected comprehensive breast-cancer data and implemented as a decision support system, which provides both dynamic access to the data as well as makes use of agent-based modeling and simulation techniques to assist bioinformaticians in making decisions about breast cancer trends in individuals as well as large patient populations. In the future, we hope to develop more advanced decision support systems based on data collected from HIV and other diseases. In the future, we also hope to build a theory around ABSS (Agent-Based Simulation System) based hybrid decision support systems by generalizing the methodology.

Query#2: This query (shown in Fig. 5) filters metastasis result for Her2/neu receptor status (+1, +2, +3) to determine the aggressiveness of cancer.

Figure 5: Query II

Figure 6: Treatment Model Figure 7 shows the initial stages of the model.

B. Using Agent-based Modeling & Simulation to assist biologists in predicting future disease outcomes

Figure 7: Initializing Parameters (note the start of tumors)

Figure 8: Tumor Spread

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