Sixth German Workshop on Case-Based Reasoning: Foundations, Systems, and Applications, Rostock, Germany.This paper describes a system aimed at prescribing nutritional
programs using Case-Based Reasoning (CBR). A nutritional program is a
balanced dietetic plan aimed at better health conditions of the individuals. The
nutritional task refers to prescribing a nutritional program as a therapy for a given
nutritional disorder, in accordance to the patient’s characteristics that act as
constraints, functions and goals. The main reason to use a case-based reasoning
system in designing nutritional programs lies in the nature of the nutritional expert
task, which is carried out by reusing past experiences. Nutritional professionals
usually express their knowledge with generalizations, even when pointing specific
instances as examples. This has motivated us to model the starting case base with
a prototypical memory. Our approach to perform the nutritional task in a casebased
reasoning system can be viewed as a two-fold process. First, the
characteristics, symptoms, goals and restrictions are used to classify the new
patient in a group associated to a diagnostic category. Second, the hypothetical
case corresponding to the category that classifies the new case provides the design
that represents the solution to be adapted to solve the new case. The system we
are describing contemplates the nutritional task from the collection of patient's
characteristics performing the diagnosis task implicitly, and prescribing the
nutritional program (meal plan) to treat the respective nutritional disorder

Camargo, Katia G.

Thé, Maria Alice L.

Weber, Rosina O.

Martins, Alejandro

Ricardo, Barcia M.

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Designing Nutritional Programs with Case-
Based Reasoning
Katia G. Camargo
gavra@eps.ufsc.br
Maria Alice L. Thé
maria@eps.ufsc.br
Rosina Weber
rosina@eps.ufsc.br
Alejandro Martins
martins@eps.ufsc.br
Barcia M. Ricardo Ph.D
rbarcia@eps.ufsc.br
Federal University of Santa Catarina - Graduate Program in Production Engineering
(PPGEP/UFSC), Florianópolis, SC - BRAZIL, Phone/Fax: +55 48-331-7010
Abstract. This paper describes a system aimed at prescribing nutritional
programs using Case-Based Reasoning (CBR). A nutritional program is a
balanced dietetic plan aimed at better health conditions of the individuals. The
nutritional task refers to prescribing a nutritional program as a therapy for a given
nutritional disorder, in accordance to the patient’s characteristics that act as
constraints, functions and goals. The main reason to use a case-based reasoning
system in designing nutritional programs lies in the nature of the nutritional expert
task, which is carried out by reusing past experiences. Nutritional professionals
usually express their knowledge with generalizations, even when pointing specific
instances as examples. This has motivated us to model the starting case base with
a prototypical memory. Our approach to perform the nutritional task in a case-
based reasoning system can be viewed as a two-fold process. First, the
characteristics, symptoms, goals and restrictions are used to classify the new
patient in a group associated to a diagnostic category. Second, the hypothetical
case corresponding to the category that classifies the new case provides the design
that represents the solution to be adapted to solve the new case. The system we
are describing contemplates the nutritional task from the collection of patient's
characteristics performing the diagnosis task implicitly, and prescribing the
nutritional program (meal plan) to treat the respective nutritional disorder.
1. Introduction
The nutritional task refers to prescribing a nutritional program as a therapy
for a given nutritional disorder, in accordance to the patient’s characteristics that
act as constraints, functions and goals. In this sense, this task can be viewed as a
design task. This paper describes a system aimed at prescribing nutritional
programs using a Case-Based Reasoning (CBR) system.
Computerized systems to support the nutritional task have been developed
and used since the sixties (Eckstein, 1967). A milestone on such research occurs
with the development of case-based reasoners. The research developed by
Hammond (1986) implementing CHEF is a CBR application to plan meal recipes.
In this research, recipes are viewed as plans. CHEF plans through the reminding of
recipes that were successful in similar situations and modifies the recipes
according to the conditions of the new situation. Performing a design task, JULIA
(Hinrich & Kolodner, 1991) suggests complete meals. The experiences are
described according to constraints and the solutions are designs that satisfy them.
However, neither systems deal with the nutritional task as a target, and this is
because they are not designed to handle variables conducting to the diagnosis task.
Planning recipes and designing meals are secondary tasks in the nutritional
domain, in which a nutritional program is designed to solve a nutritional disorder
in accordance with the patient's needs, symptoms, constraints and goals. Both
CHEF and JULIA attempt to meet the user's needs concerning to their preferences
but not considering nutritional needs. The system we are describing contemplates
the nutritional task from the collection of patient's characteristics, performs the
diagnosis task implicitly, and prescribes the nutritional program (meal plan) to
solve the respective nutritional disorder. Another example of a computerized
system to support the nutritional task is CAMP (Marling, 1997) that design meals
from nutritional recommendations. Camp represents an attempt to support another
secondary task on the nutritional domain since it requires the nutritional
knowledge to be input to the system.
2. Nutritional Programs
The nutritional consultation consists of four steps: interview, evaluation,
design of the nutritional program, and follow up (BAXTER, 1993). The
nutritional program is based upon information from different natures, such as
anthropometric, biochemichal, clinical trials, food intake, and psychosocial; all
addressing the nutritional status of the patient (KRAUSE, 1995). The overall
evaluation of these information support the nutritional diagnosis in which the
nutritional program is grounded. The complementing issues in this task embody a
nutrition education and intervention counseling and a follow up guided by
patient's feedback.
The nutritional program is carried out through a balanced dietetic plan
aimed at better health conditions of the individuals. This dietetic plan considers
the food intake, nutrients, balanced amounts of foods concerning quality and
quantity, food distribution, meals per day, individual requirements, preferences
and food habits. The real challenge when designing the dietetic program is in
including all essential nutrients in the indicated quantities (proteins, 10-10-15%;
fat, 25-30%; and carbohydrates, 55-60%, of calories per day, respectively), in
accordance with RDA (Recommended Dietary Allowances), (Assis, 1997). The
nutritional professional has to calculate the total amount of calories per day, and
the indicated amounts of salt, sugar, cholesterol, fiber and fat (specially saturated)
(KRAUSE, 1995). Besides, this professional has to take into account the patient's
habits or disorders to reach a fully efficient program.
3. Using RBC to design Nutritional Programs
The main reason to use a case-based reasoning system to design nutritional
programs lies in the nature of the nutritional expert task, which is carried out by
reusing past experiences. Using case-based reasoning to the nutritional task aims
at benefiting from the advantage of retrieving a nutritional program design instead
of building it from scratch. Every nutritional consultation is an experience and
thus it can be represented as cases in the system. This provides the availability of
past knowledge to support the performance of the nutritional task.
The nutritionist, from the interview, can characterize the new situation to
retrieve similar ones from the case base. Hence, supporting an evaluation and
providing a starting point to build the design of the nutritional program by
adapting the design (or a combination of) associated to the most similar cases.
Case-based Reasoning has enlarged its fields of application to several
domains and tasks such as design. The experience-based reasoning is recognized
as the cognitive process in design creation. While searching for solutions, the
designers can research on the existing ones and adapt previous solutions (Oxman
& Voss, 1996).
The design task embodies a great amount of decisions and the level of difficulty
depends upon the knowledge available, the definition of variables and the results.
This is why the expert knowledge can be very helpful, since it guides these
choices and analysis.
Our approach to perform the nutritional task in a case-based reasoning
system can be viewed as a two-fold process. First, the characteristics, symptoms,
goals and restrictions of the patient are used to assess the similarity of each case of
the base as an attempt to classify the new patient in a group associated to a
diagnostic category. Second, the hypothetical case (in a prototypical memory)
corresponding to the category that classifies the new case provides the design that
represents the solution to be adapted to solve the new case.
Case Basenew case problem
prototypical
memory
prototypical
solution
new case problem
+
ptototypical solution
adaptation
+
evaluation
expert
new instance case
Figure 1: System Project
4. The Prototypical Memory
Expert knowledge is represented in a CBR system in the case base, in
assessing similarity between cases, evaluating differences, and performing
adaptation. The case base is the means to deal with domain experiences in a CBR
system in which the expert knowledge is stored. The nutritionist first learns the
theoretical knowledge and, through time, incorporates experiences to this
knowledge turning it into expertise. However, the nutritional professionals usually
express their knowledge with generalizations, even when pointing specific
instances as examples. This has motivated us to consider starting our system from
a prototypical memory and making use of the research provided by PROTOS
(Bareiss & Slator, 1991). The use of a prototypical memory enables the
implementation of a case base that comprises every important category of
nutritional programs.
The prototypical memory is built concerning variations on symptoms,
goals, and other relevant characteristics that cannot be adapted, representing the
categories based on a knowledge acquisition from the experts. One example is a
category related to obesity and an associated solution of a nutritional program
aiming at weight loss. The characteristics indicate a BMI (Body Mass Idex) in the
interval between 25 and 45 (kg/m²), a caloric intake history in the interval (0.5,
10) representing a ratio over the caloric needs, and the presence of obesity in close
relatives. The specific values of caloric intake, for instance, can be adapted thus
the values to be used as indexes are the relative ones. This prototype (category)
suggests a nutritional program that embeds a weight loss goal, representing a
collection of experiences of the expertise employed by the nutritionists. This
nutritional program is a diet prescription expressed through a meal plan
(suggestions on meals and foods) contemplating alternatives and nutritional
recommendations.
5. System Design
The overall project consists of several phases, aiming at different purposes,
namely:  prototyping, acquiring cases, adaptation knowledge elicitation,
programming the final version.
The first phase comprises the development of the first prototype that
demonstrates the feasibility of the use of the CBR paradigm to the nutritional task
based on a prototypical memory.
The second phase refers to the use of this first prototype by some experts
who search for actual cases to feed the system. From a new case, the user inputs
all the characteristics asked by the system which returns a category most similar to
this new case. The expert includes this new case to the case base.
Simoultaneous to the secong phase, a third phase is employed when the
experts watch the functioning of the CBR system obserbing adaptation needs. For
every new case that is added to the case base, the expert elicits the possible
required knowledge that the system would need to have represented to
dynamically adapt the prototypical program to prescribe it to the new case (see
Figure 1). These second and third phases have no time frame set a priori as they
end when experts consider that the case base of actual individual instances
comprises a sufficient amount of cases to be used, with reduced adaptation
knowledge requirements.
The fourth phase is accomplished by programming a PROTOS-style
retrieval system. At this phase, the adaptation knowledge acquired during the
second phase is programmed to be performed automatically also enabling an
automatic integration to the new cases (learning) after an evaluation confirmed by
an expert's feedback. The final version of the system integrates the automatic
adaptation phase. A new case is input and it is assessed to determine the category
it classifies. The similarity assessment is then carried out only with the actual
cases that are linked to the chosen category. The most similar case is selected and
its nutritional program is adapted to fit this new case. This new case is solved and
after a positive evaluation, it is added to the case base and linked to the originating
category, enabling the system to learn with experience.
5.1 The First Prototype
The cases are an interpretation of the domain experiences modeled to
accomplish the system's task. The nutritional experience is related to individuals
who have a nutritional disorder that is classified according to symptoms and who
deserve a special nutritional program in conformity with their needs, goals and
adequacy. As the task has two distinctive moments, cases have to comprise
differently a description of the problem and the solution. The appropriate
interpretation of the experiences is indicated by domain experts who have chosen
characteristics to describe the problem,: among others, sex, age, height, weight,
BMI, daily and average activity level, caloric intake ratio, and the solution.
Expert knowledge also teaches us on which dimensions one can compare
two nutritional experiences in order to assess their similarity. These knowledge is
modeled through the selected attributes that comprise the indexing vocabulary,
namely, sex, age, IBM, food intake and activity level.
6. Conclusions and Future Developments
One advantage provided by an intelligent system shared by CBR systems is
the ability to store knowledge from several experts making the expert task it
represents more reliable and less error prone. Besides, when employing the
paradigm of reminding a similar previous episode, humans are subject to
emotional blocks that may avoid them of retrieving a relevant solution. A
computational system, as long as it fulfills theoretic requirements, guarantees that
the similar experiences are retrieved.
Although our prototype requires expert knowledge to be acquired, one
always benefits from the ease representation provided by CBR systems. Besides,
since an efficient adaptation method can be implemented, with knowledge
acquired from adaptation experiences, learning gains consistency contributing to
the increasing robustness of the system.
An improvement considered necessary to our system is a proper modeling
of concepts such as BMI, age, etc. The use of fuzzy sets is indicated in order to
increase the precision of the membership of a given new case to a category in the
case base.
7. References
Assis, Maria Alice A. (1997) Consulta de nutrição, controle e prevenção do
colesterol elevado, Ed. Insular, Florianópolis, Brasil.
Bareiss, E.R. & Slator, B.M. (1991). From PROTOS to ORCA:  Reflections on a
Unified Approach to Knowledge Representation, Categorization, and
Learning. Northwestern University, Institute for The Learning Sciences,
Technical Report no. 20.
Baxter, Yara C. (1993). Diagnóstico nutricional: criação, implantação e
Perspectivas, II Curso de Recentes Avanços em Unidade de Nutrição
Hospitalar, Hospital das Clínicas FMUSP, Divisão de Nutrição e
Dietética, São Paulo.
Eckstein, Eleanor Foley. (1967). Menu planning by computer: the random
approach. Journal of American Dietetic Association, 51, 529-533.
Hammond, Kristian J. (1986). CHEF: A model of case-based planning. In
Proceedings of AAAI-86. Cambridge, MA: AAAI Press/MIT Press.
Hinrich, T. & Kolodner, J. (1991). The roles of adaptation in case-based design. In
Proceedings of AAAI-91. Cambridge, MA: AAAI Press/MIT Press.
Krause, K.L. & Mahan, A. T. (1995). Alimentos, Nutrição e dietoterapia, 8ª
edição, Ed. Afiliada.
Marling, Cindy. The Case-based menu planner (CAMP). Available online,
http://toros.ces.cwru.edu/~marling/camp.html,  [September, 1997].
Oxman, R. & Voss, A. (1996). CBR in design, AI Communications 9, 117-127.


Designing nutritional programs with case-based reasoning

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