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- hi?i?need?help?with?my?assignment?Answer?the?questions?Applications?9.5?(Spreadsheet?Model?Helps????Medical?Residents),?11.5(Clinical?decision?support?system?for????tendon?injuries)?its?from?the?textbook ?business intelligence and analytics systems for decision support 10th edition by sharda ramesh you need the text book to answer the above asked applications if you can get the same textbook its fine or i can send only the screen shots of the above asked applications only i asked you this?question because you did my last assignment really nice?thank you so much?
9.5 Decision moDeling with spread sheets
Models can be developed and implemented in a variety of programming languages
and systems. These range from third-, fourth-, and fifth-generation programming
languages to computer-aided software engineering (CASE) systems and other
automatically generate usable software. We focus primarily on spreadsheets (with
add-ins), modeling languages, and transparent data analysis tools. With their
and flexibility, spreadsheet packages were quickly recognized as easy-to-use
implementation software for the development of a wide range of applications in
engineering, mathematics, and science. Spreadsheets include extensive statistical,
forecasting, and other modeling and database management capabilities, functions,
and routines. As spread sheet packages evolved, add-ins were developed for
solving specific model classes. Among the add-in packages, many were developed
DSS development. These DSS-related add-ins include Solver (Frontline Systems
solver.com) and What?sBest! (a version of Lindo, from Lindo Systems, Inc.,
for performing linear and nonlinear optimization; Braincel (Jurik Research
Inc., jurikres.com) and NeuralTools (Palisade Corp., palisade.com) for artificial
networks; Evolver (Palisade Corp.) for genetic algorithms; and @RISK (Palisade
for performing simulation studies. Comparable add-ins are available for free or at a
low cost. (Conduct a Web search to find them; new ones are added to the
on a regular basis.)
The spreadsheet is clearly the most popular end-user modeling tool because it
incorporates many powerful financial, statistical, mathematical, and other
Spreadsheets can perform model solution tasks such as linear programming and
regression analysis. The spreadsheet has evolved into an important tool for
and modeling (see Farasyn et al., 2008; Hurley and Balez, 2008; and Ovchinnikov
Milner, 2008). Application Case 9.4 describes an interesting application of a
spreadsheetbased optimization model in a small business.
Application Case 9.4
Showcase Scheduling at Fred Astaire East Side Dance
StudioThe Fred Astaire East Side Dance Studio in New
York City presents two ballroom showcases a year. The
studio wanted a cheap, user-friendly, and quick computer
program to create schedules for its showcases that
involved heats lasting around 75 seconds and solos
lasting around 3 minutes. The program was created using
an integer programming optimization model in Visual
Basic and Excel. The employees just have to enter the
students? names, the types of dances the students want
to participate in, the teachers the students want to dance
with, how many times the students want to do each type
of dance, what times the students are unavailable, and
what times the teachers are unavailable. This is entered
into an Excel spreadsheet. The program then uses
guidelines provided by the business to design the
schedule. The guidelines include a dance type not being
performed twice in a row if possible, a student
participating in each quarter of the showcase in order to
keep him/her active throughout, all participants in each
heat performing the same type of dance (with a
maximum of seven couples per heat), eliminating as
many one-couple heats as possible, each student and
teacher only being scheduled once per heat, and
allowing students and teachers to dance multiple times
per dance type if desired. A two-step heuristic method
wasused to help minimize the number of one-couple
heats. In the end, the program cut down the time the
employees spent creating the schedule and allowed for
changes to be calculated and made quickly as compared
to when made manually. For the summer 2007 showcase,
the system scheduled 583 heat entries, 19 dance types,
18 solo entries, 28 students, and 8 teachers. This
combination of Microsoft Excel and Visual Basic enabled
the studio to use a model-based decision support system
for a problem that could be time-consuming to solve.
Other important spreadsheet features include what-if analysis, goal seeking, data
management, and programmability (i.e., macros). With a spreadsheet, it is easy to
change a cell?s value and immediately see the result. Goal seeking is performed by
indicating a target cell, its desired value, and a changing cell. Extensive database
management can be performed with small data sets, or parts of a database can be
for analysis (which is essentially how OLAP works with multidimensional data
in fact, most OLAP systems have the look and feel of advanced spreadsheet
after the data are loaded). Templates, macros, and other tools enhance the
of building DSS.
Most spreadsheet packages provide fairly seamless integration because they read
and write common file structures and easily interface with databases and other
Microsoft Excel is the most popular spreadsheet package. In Figure 9.3, we show a
loan calculation model in which the boxes on the spreadsheet describe the
the cells, which contain formulas. A change in the interest rate in cell E7 is
reflected in the monthly payment in cell E13. The results can be observed and
immediately. If we require a specific monthly payment, we can use goal seeking to
determine an appropriate interest rate or loan amount.
Static or dynamic models can be built in a spreadsheet. For example, the monthly
loan calculation spreadsheet shown in Figure 9.3 is static. Although the problem
the borrower over time, the model indicates a single month?s performance, which
replicated. A dynamic model, in contrast, represents behavior over time. The loan
calculations in the spreadsheet shown in Figure 9.4 indicate the effect of prepayment on the principal
over time. Risk analysis can be incorporated into spreadsheets
by using built-in random-number generators to develop simulation models (see the
Spreadsheet applications for models are reported regularly. We will learn how to
use a spreadsheet-based optimization model in the next section.
section 9.5 revieW QuestiOns
1. What is a spreadsheet?
2. What is a spreadsheet add-in? How can add-ins help in DSS creation
3. Explain why a spreadsheet is so conducive to the development of
Part IV?Prescriptive Analytics diagram
figure 9.3 Excel Spreadsheet Static Model Example of a Simple Loan
Calculation of Monthly Payments.
figure 9.4 Excel Spreadsheet Dynamic Model Example of a Simple Loan
Calculation of Monthly Payments and the Effects
9.6 mathematical programming optimization
The basic idea of optimization was introduced in Chapter 2. Linear
(LP) is the best-known technique in a family of optimization tools called
programming; in LP, all relationships among the variables are linear. It is
used extensively in DSS (see Application Case 9.5). LP models have
many important applications in practice. These include supply chain
management, product mix decisions, routing, and so on. Special forms
of the models can be used for specific applications. For example,
Application Case 9.5 describes a spreadsheet model that was used to
create a schedule for medical interns.
Application Case 9.5
ResidentsFletcher Allen Health Care (FAHC) is a teaching
hospital that works with the University of Vermont?s
College of Medicine. In this particular case, FAHC
employs 15 residents with hopes of adding 5 more in the
diagnostic radiology program. Each year the chief
radiology resident is required to make a yearlong
schedule for all of the residents in radiology. This is a
time-consuming process to do manually because there
are many limitations on when each resident is and is not
allowed to work. During the weekday working hours, the
residents work with certified radiologists, but nights,
weekends, and holidays are all staffed by residents only.
The residents are also required to take the ?emergency
rotations,? which involve taking care of the radiology
needs of the emergency room, which is often the busiest
on weekends. The radiology program is a 4-year program,
and there are different rules for the work schedules of
the residents for each year they are there. For example,
first- and fourthyear residents cannot be on call on
holidays, second-year residents cannot be on call or
assigned ER shifts during 13-week blocks when they are
assigned to work in Boston, and third-year residents
must work one ER rotation during only one of the major
winter holidays (Thanksgiving or Christmas/New Year?s).
Also, first-year residents cannot be on call until after
January 1, and fourth-year residents cannot be on call
after December 31, and so on. The goal that the various
chief residents have each year is to give each person the
maximum number of days between on-call days as is
possible. Manually, only 3 days between on-call days was
the most a chief resident had been able to accomplish.In
order to create a more efficient method of creating a
schedule, the chief resident worked with an MS class of
MBA students to develop a spreadsheet model to create
the schedule. To solve this multiple-objective decisionmaking problem, the class used a constraint method
made up of two stages. The first stage was to use the
spreadsheet created in Excel as a calculator and to not
use it for optimizing. This allowed the creators ?to
measure the key metrics of the residents? assignments,
such as the number of days worked in each category.?
The second stage was an optimization model, which was
layered on the calculator spreadsheet. Assignment
constraints and the objective were added. The Solver
engine in Excel was then invoked to find a feasible
solution. The developers used Premium Solver by
Frontline and the Xpress MP Solver engine by Dash
Optimization to solve the yearlong model. Finally, using
Excel functions, the developers converted the solution
for a yearlong schedule from zeros and ones to an easyto-read format for the residents. In the end, the program
could solve the problem of a schedule with 3 to 4 days in
between on calls instantly and with 5 days in between on
calls (which was never accomplished manually). Source:
Based on A. Ovchinnikov and J. Milner, ?Spreadsheet
Model Helps to Assign Medical Residents at the
Vermont?s College of Medicine,? Interfaces, Vol. 38, No.
August 2008, pp. 311?323.
Sharda, Ramesh; Delen, Dursun; Turban, Efraim;
Aronson, Janine; Liang, Ting Peng. Business Intelligence
and Analytics: Systems for Decision Support (Page 407).
Pearson Education. Kindle Edition.
11.5 ApplicAtions oF expert systems
ES have been applied to many business and technological areas to
support decision making. Application Case 11.3 shows a recent realworld application of ES. Table 11.2 shows some representative ES and
their application domains.
classical Applications of es
Early ES applications, such as DENDRAL for molecular structure
identification and MYCIN
for medical diagnosis, were primarily in the science domain.
XCON for configuration of the VAX computer system at Digital
Equipment Corp. (a major producer of minicomputers around
1990 that was later taken over by Compaq) was a successful
example in business.dendrAl The DENDRAL project was initiated
by Edward Feigenbaum in 1965. It used a set of knowledge- or
rule-based reasoning commands to deduce the likely molecular
structure of organic chemical compounds from known chemical
analyses and mass spectrometry data.
determine a proper credit line. Rules in the knowledge base can also
help assess risk and risk-management policies. These kinds of systems
are used in over one-third of the top 100 commercial banks in the
United States and Canada.
pension Fund Advisors Nestl? Foods Corporation has developed an ES
that provides information on an employee?s pension fund status. The
system maintains an up-to-date knowledge base to give participants
advice concerning the impact of regulation changes and conformance
with new standards. A system offered on the Internet at the Pingtung
Teacher?s College in Taiwan has functions that allow participants to plan
their retirement through a what-if analysis that calculates their pension
benefits under different scenarios.
AutomAted help desks BMC Remedy (remedy.com) offers HelpDeskIQ,
a rule-based help desk solution for small businesses. This browserbased tool enables small businesses to deal with customer requests
more efficiently. Incoming e-mails automatically pass into HelpDeskIQ?s
business rule engine. The messages are sent to the proper technician,
based on defined priority and status. The solution assists help desk
technicians in resolving problems and tracking issues more effectively.
Areas for es Applications
As indicated in the preceding examples, ES have been applied
commercially in a number
of areas, including the following:
Finance. Finance ES include insurance evaluation, credit analysis,
fraud prevention, financial report analysis, financial planning, and
Data processing. Data processing ES include system planning,
equipment selection, equipment maintenance, vendor evaluation, and
Marketing. Marketing ES include customer relationship
analysis, product planning, and market planning.
Human resources. Examples of human resources ES are human
resources planning, performance evaluation, staff scheduling, pension
management, and legal
Manufacturing. Manufacturing ES include production planning,
quality management, product design, plant site selection, and
equipment maintenance and
Homeland security. Homeland security ES include terrorist threat
and terrorist finance detection.
Business process automation. ES have been developed for help
desk automation, call center management, and regulation enforcement.
Healthcare management. ES have been developed for
bioinformatics and other
healthcare management issues.
Now that you are familiar with a variety of different ES applications, it is
look at the internal structure of an ES and how the goals of the ES are
sectiOn 11.5 revieW QuestiOns
1. What is MYCIN?s problem domain?
2. Name two applications of ES in finance and describe their benefits.
3. Name two applications of ES in marketing and describe their
4. Name two applications of ES in homeland security and describe
11.6 structure oF expert systems
ES can be viewed as having two environments: the development
environment and the
consultation environment (see Figure 11.4). An ES builder uses the
development environment to build the necessary components of the
ES and to populate the knowledge
base with appropriate representation of the expert knowledge. A
nonexpert uses the
consultation environment to obtain advice and to solve problems using
knowledge embedded into the system. These two environments can be
separated at the
end of the system development process.
The three major components that appear in virtually every ES are the
base, the inference engine, and the user interface. In general, though,
an ES that interacts
with the user can contain the following additional components:
Knowledge acquisition subsystem
Explanation subsystem (justifier)
Currently, most ES do not contain the knowledge refinement
component. A brief
description of each of these components follows.
knowledge Acquisition subsystem
Knowledge acquisition is the accumulation, transfer, and
transformation of problem-solving expertise from experts or
documented knowledge sources to a computer
Figure 11.4 Structure/Architecture of an Expert
program for constructing or expanding the knowledge base. Potential sources of
knowledge include human experts, textbooks, multimedia documents, databases
(public and private), special research reports, and information available on the
Web.Currently, most organizations have collected a large volume of data, but the
organization and management of organizational knowledge are limited.
Knowledge acquisition deals with issues such as making tacit knowledge explicit
and integrating knowledge from multiple sources.
Acquiring knowledge from experts is a complex task that often creates a
in ES construction. In building large systems, a knowledge engineer, or knowledge
elicitation expert, needs to interact with one or more human experts in building
the knowledge base. Typically, the knowledge engineer helps the expert structure
the problem area by interpreting and integrating human answers to questions,
drawing analogies, posing counterexamples, and bringing conceptual difficulties to
The knowledge base is the foundation of an ES. It contains the relevant
knowledge necessary for understanding, formulating, and solving problems. A
typical knowledge base may include two basic elements: (1) facts that describe
the characteristics of a specific problem situation (or fact base) and the theory of
the problem area and (2) special heuristics or rules (or knowledge nuggets) that
represent the deep expert knowledge to solve specific problems in a particular
domain. Additionally, the inference engine can include general-purpose problemsolving and decision-making rules (or meta-rules?rules about how to process
production rules).It is important to differentiate between the knowledge base of
an ES and the knowledge base of an organization. The knowledge stored in the
knowledge base of an ES is often represented in a special format so that it can be
used by a software program (i.e., an expert system shell) to help users solve a
particular problem. The organizational knowledge base, however, contains various
kinds of knowledge in different formats (most of which is represented in a way
that it can be consumed by people) and may be stored in different places. The
knowledge base of an ES is a special case and only a very small subset of an
organization?s knowledge base.
The ?brain? of an ES is the inference engine, also known as the control structure
or the rule interpreter (in rule-based ES). This component is essentially a
computer program that provides a methodology for reasoning about information
in the knowledge base and on the blackboard to formulate appropriate
conclusions. The inference engine provides directions about how to use the
system?s knowledge by developing the agenda that organizesorganizes and
controls the steps taken to solve problems whenever a consultation takes place.
It is further discussed in Section 11.7.
An ES contains a language processor for friendly, problem-oriented
communication between the user and the computer, known as the user interface.
This communication can best be carried out in a natural language. Due to
technological constraints, most existing systems use the graphical or textual
question-and-answer approach to interact with the user.
The blackboard is an area of working memory set aside as a database for
description of the current problem, as characterized by the input data. It is also
used for recording intermediate results, hypotheses, and decisions. Three types of
decisions can be recorded on the blackboard: a plan (i.e., how to attack the
problem), an agenda (i.e., potential actions
awaiting execution), and a solution (i.e., candidate hypotheses and alternative
action that the system has generated thus far).
Consider this example. When your car fails to start, you can enter the symptoms
of the failure into a computer for storage in the blackboard. As the result of an
intermediate hypothesis developed in the blackboard, the computer may then
suggest that you do some additional checks (e.g., see whether your battery is
connected properly) and ask you to report the results. This information is also
recorded in the blackboard. Such an iterative process of populating the
blackboard with values of hypotheses and facts continues until the reason for the
failure is identified.
explanation subsystem (Justifier)
The ability to trace responsibility for conclusions to their sources is crucial both in
of expertise and in problem solving. The explanation subsystem can trace such
responsibility and explain the ES behavior by interactively answering questions
such as these:
Why was a certain question asked by the ES?
How was a certain conclusion reached?
Why was a certain alternative rejected?
What is the complete plan of decisions to be made in reaching the
example, what remains to be known before a final diagnosis can be determined?
In most ES, the first two questions (why and how) are answered by showing the
rule that required asking a specific question and showing the sequence of rules
that were used (fired) to derive the specific recommendations,
respectively.knowledge-refining systemHuman experts have a knowledge-refining
system; that is, they can analyze their own knowledge and its effectiveness, learn
from it, and improve on it for future consultations. Similarly, such evaluation is
necessary in expert systems so that a program can analyze the reasons for its
success or failure, which could lead to improvements resulting in a more accurate
knowledge base and more effective reasoning.The critical component of a
knowledge refinement system is the self-learning mechanism that allows it to
adjust its knowledge base and its processing of knowledge based on the
evaluation of its recent past performances. Such an intelligent component is not
yet mature enough to appear in many commercial ES tools. Application Case 11.4
illustrates another application of expert systems in healthcare.
Application Case 11.4
Diagnosing Heart Diseases by Signal Processing Auscultation is
the science of listening to the sounds of internal body organs, in
this case the heart. Skilled experts can make diagnoses using this
technique. It is a noninvasive screening method of providing
valuable information about the conditions of the heart and its
valves, but it is highly subjective and depends on the skills and
experience of the listener. Researchers from the Department of
Electrical & Electronic Engineering at Universiti Teknologi
Petronas have developed an Exsys Corvid expert system, SIPMES
(Signal Processing Module Integrated Expert System) to analyze
digitally processed heart sound.The system utilizes digitized heart
sound algorithms to diagnose various conditions of the heart.
Heart sounds are effectively acquired using a digital electronic
stethoscope. The heart sounds were collected from the Institut
Jantung Negara (National Heart Institute) in Kuala Lumpur and
the Fatimah Ipoh Hospital in Malaysia. A total of 40 patients age
16 to 79 years old with various pathologies were used as the
control group, and to test the validity of the system using their
abnormal heart sound samples and other patient medical
data.The heart sounds are transmitted using a wireless link to a
nearby workstation that hosts the Signal Processing Module
(SPM). The SPM has the capability to segment the stored heart
sounds into individual cycles and identifies the important cardiac
events.The SPM data was then integrated with the Exsys Corvid
knowledge automation expert system. The rules in the system
use expert physician reasoning knowledge, combined with
information acquired from medical journals, medical textbooks,
and other noted publications on cardiovascular diseases (CVD).
The system provides the diagnosis and generates a list of diseases
arranged in descending order of their probability of
occurrence.SIPMES was designed to diagnose all types of
cardiovascular heart diseases. The system can help general
physicians diagnose heart diseases at the earliest possible stages
under emergency situations where expert cardiologists and
advanced medical facilities are not readily available.The diagnosis
made by the system has been counterchecked by senior
cardiologists, and the results coincide with these heart experts. A
high coincidence factor of 74 percent has been achieved using
Questions for Discussion
1. List the major components involved in building
SIPMES and briefly comment on them.
2. Do expert systems like SIPMES eliminate the
need for human decision making?
3. How often do you think that the existing expert
systems, once built, should be changed?
What We can Learn from this application caseMany expert
systems are prominently being used in the field of medicine.
Many traditional diagnostic procedures are now being built into
logical rulebased systems, which can readily assist the medical
staff in quickly diagnosing the patient?s condition of disease.
These expert systems can help in saving the v...
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