This document discusses using case-based reasoning for diet menu planning. It provides an overview of related work using CBR for domains like psychiatry and Alzheimer's care. It then describes DietMaster, a knowledge-intensive CBR system for diet menu planning that uses domain knowledge from experts. DietMaster's architecture and functional components are explained, including its representation of knowledge through rules and cases. Finally, the structures for input cases, cases in process, and output/learned cases are defined.
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Abstract:
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Presentation at the "Reasoning from experiences on the Web" workshop (WebCBR 2010) at the International Conference on Case Based Reasoning 2010.
Abstract:
While Case-based reasoning (CBR) has successfully been deployed on the Web, its data models are typically inconsistent with existing information infrastructure and standards. In this paper, we examine how
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suited for the open ecosystem of the emerging Web of Data, and provide
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propose that for the first time the Web of Data provides data and a real
context for open CBR systems.
Case-based reasoning (CBR) classifiers use a database of problem solutions to solve
new problems. Unlike nearest-neighbor classifiers, which store training tuples as points
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symbolic descriptions.
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Case Based Reasoning
1. Diet Menu planning Using
Case Based Reasoning
R. Aker
rkar
Rajendra Akerkar
1
Presented at UKCBR 2009, Cambridge, UK
2. AGENDA
Introduction
Related work
Diet Menu Plan
R. Aker
DietMaster
rkar
Example
Conclusion
2
3. INTRODUCTION
An application of case based reasoning (CBR)
in th di t
i the diet menu planning
l i
R. Akerkar
r
3
4. RELATED WORK
MNAOMIA
operates in the domain of psychiatric eating disorders
disorders.
assists clinicians with the cognitive tasks of diagnosis,
R. Aker
treatment planning, patient follow-up and clinical research.
The Auguste project
h
rkar
is an effort to provide decision support for planning the
ongoing care of Alzheimer’s Disease (AD) patients.
The first reported system prototype supports the decision
to prescribe neuroleptic drugs for behavioural problems.
The prototype is a hybrid system where a CBR part decides
p yp y y p
if a neuroleptic drug is to be given, and a Rule-Based
Reasoning (RBR) part decides which neuroleptic to use.
4
5. RELATED WORK
Cbord
used in hospitals in Australia and New Zeeland
p
a query is formed on the basis of desired energy
content of the menu as well as a number of critical
R. Aker
medical conditions which require special diets, such as
rkar
diabetes.
does not allow the dietitian to alter a retrieved menu
within the system.
MIKAS
a menu construction system that supports individual
requirements and food preferences of a client
q p
multimodal system combining CBR and RBR.
5
6. DIETMASTER
knowledge-intensive approach
based
b d on an intensive use of domain knowledge and relevant
i t i fd i k l d d l t
parts of the surrounding world in its problem-solving and
R. Aker
learning methods.
rkar
Menu planning process is derived using data
collected from experts
p
(dietitians, physicians, medical practitioners, and home
science departments of various colleges) and
literature of the Indian Medical Council
6
7. DIET MENU PLANNING PROCESS
Determine the total calories required for an individual
according to his or her physical state and activities he or she
is d i
i doing.
Determine the nutrient requirements with regard to
R. Aker
carbohydrates, proteins, and fat to suit the person’s health
state and overcome the energy requirement
requirement.
rkar
Determine the proportions of eleven food groups to fulfill
total carbohydrate, protein, and fat requirements as well as
restrictions that should be followed to improve the health
p
state.
Divide these proportions into multiple intakes within a day
(say, 12 hours) period.
Plan a sample menu by assigning proper food items to each
intake, which will match the food group requirement.
7
8. DIETMASTER ARCHITECTURE
Reasoning
New
Problem Planning Task
Control
C l
Combined RBR CBR
Reasoning Control
R. Aker
MBR
rkar
Solved Rules Cases
Problem
Models
Sustained Learning RBL
Control CBL RBR – Rule Based Reasoning,
CBL – Case Based Learning,
Learning CBR – Case Based Reasoning,
RBL – Rule Based Learning
Learning,
8
MBR – Model Based Reasoning
9. FUNCTIONAL COMPONENTS OF
DIETMASTER
The flow of control and information between
the knowledge base and th processes of
th k l d b d the f
problem solving
R. Akerkar
r
9
10. REPRESENTATION OF KNOWLEDGE
There are two types of heuristic rules
the premise-conclusion rule - called conclusive
rule
premise-> conclusion statement
the premise-action rule - called conditioned
R. Aker
procedure.
premise -> action sequence structure.
i > ti t t
rkar
different types of knowledge at the object
and control levels
Domain knowledge at the object level:
conceptual domain model in which specific
experiences (past cases) and general
heuristics (premise-conclusion rules) are
integrated.
i t t d
Explicit knowledge about how to use
domain knowledge for problem solving and
learning is described partly as control level
10
concepts, partly as control rules.
11. CASE STRUCTURE
Input Case Case in process Learned Case
Input findings Input findings Relevant findings
Goal Derived findings Case reused for generating a plan
Solution constraints Explanation of successful plan
Planning states Successful diet plan
Possible planning Different cases
Explanation of planning Book-keeping information
Possible diet plan
Explanation of plan
Similar cases
Case base is a set of features of client C = { I,O,R), which is
stored in the form of frame.
R subset represents outcome, it is a knowledge collected
after solving a case by system for further reuse.
12. INPUT CASE
When diet planning process starts,
it generates some intermediate results
describing the client in more detail.
R. Aker
Those are called as derived descriptors of the
rkar
case, those along with input descriptors
forms case in process.
After completion of the planning process case
will be stored in the case base for future
reuse.
Here only required information for reuse gets
stored in the case base this becomes case to
store.
store 12
13. OUTPUT CASE
This set is a collection of two subsets,
i.e. calculated results and planning information.
Subset calculated results consists of
standard weight,
R. Aker
weight state ( under-weight or over-weight),
total calories required, total calories gain from previous
required
rkar
meal plan, nutrient requirement ( carbohydrates, proteins,
fats requirement) in gm.
Subset planning information consists of food group
proportion
i.e. ratio of 11 food groups to contribute total calories
distribution, intake wise food group distribution, restrictions
on food items indicates food items desirable for health
state and food items to be avoided to improve health statestate.
This also contains menu depicting total number of
intakes per day in detail giving food items with their
quantities for each intake.
13
14. OUTCOME
Consists of attributes made
for case numbers of previous cases along with their usage
to generate current output,
matching string giving neighborhood of current case with
R. Aker
previous reused case, case number allotted to current case
for identification purpose,
p p ,
rkar
state of case indicating whether meal plan suggested in
output is successful,
failed or case is under observation.
This set maintains results of the case which consists
of state of blood glucose level ( improved, aggravated,
or no change), insulin dosage status ( stopped, dosage
reduced, dosage increased or no change) , health
complaints state indicates diseases get cured and
which diseases are aggravated.
This structure is helpful for multilevel adaptation
method to reuse for solving similar future cases.
14
15. CASE STRUCTURE
Age (in years)
Weight (kg) Vegetarian / Non Vegetarian
BFI (W i t t Hi ratio)
(Waist to Hip ti )
Height (cm)
Work type / activity
Gender
R. Aker
Lose of wt / Bed patient / Sedentary /
Male / Female Moderate / Heavy y
rkar
Female Status
General Test details
BP – Blood Pressure in mm
1st Trimester Pregnancy
g y
Systolic,
S stolic Diastolic
2nd Trimester Pregnancy BGL Test Method
3rd Trimester Pregnancy Plasma / Whole Blood / HbA1c / Urine /
0 – 6 Mon Lactation Glycometer
7 – 12 Mon Lactation BGL - Blood Glucose Level in mg/dl
Fasting BGL , Postprandial BGL
Body Frame
Cholesterol mg/dl
g
Small / Medium / Large Insulin
15
Eating Habit
16. CASE STRUCTURE
Type
Dosage for a day in cc
Exercise
(Type, duration in minutes )n
R. Aker
Diabetic Symptoms
Polyuria
rkar
Polydypsia
Polyphagia
Dehydration
Loss of weight / Weakness
L f i h W k
Less Immunity
Less Healing capacity
Degenerative Changes
Ketosis/Acidosis
Other health complaints
(Disease )m
Current meal P tt
C t l Pattern 16
(Intake, Food item, Unit Quantity)m
17. RULES
Balanced weight for given height, age and gender has
been stored in terms of associated rules. There are
two sets of rules. One set maintains standard weight
f l O i i d d i h
for children.
R. Aker
(Age, Height, Gender)-> Standard Weight.
This rule checks the physical state of a person and
rkar
decides whether he is under weight, over weight or
obese (recognized with obesity status).
(Standard Weight, Actual weight, Body Frame, Waist to
Hip ratio)-> Obesity Status
ratio) >
Restrictions on food groups are also defined as
minimum and maximum levels of food groups, which
suit the health state of the client, and also help to
improve it These are conclusive rules and stored in a
it.
Table.
(Obesity symptoms, Other health complaints) ->
(Restrictions on food group) n
(Food
(F d groups are Cereals, Pulses, Milk, Eggs, Meat, Green
C l P l Milk E M t G 17
Veg, Other Veg., Roots & tubers, Fruits, Sugar, Oil)
18. PROCEDURES
General knowledge is stored in terms of procedures
solves problem partially whenever past cases are not
available.
il bl
These procedures are also used in adaptation process.
R. Aker
Major five operations in diet menu planning along
with supportive processes are g
ith ti generalized and k t
li d d kept
rkar
available.
Calculate Total Calories
CPF Proportion finds nutrient requirement of the
individual
Food Group Proportion procedure to distribute nutrients
among 11 food groups.
Food
F d group di t ib ti procedure to distribute food group
distribution d t di t ib t f d
exchanges among different intakes.
Menu construction
These can also act as fundamental knowledge and
18
some times with conditioned rules.
24. INDEXING OF CASES
DietMaster case contains the following information
Relevant findings Successful planning Explanation of successful
planning
differential features Successful diet Explanation of successful
p
treatment treatment
differential cases failed plan
prototype of case failed diet treatment
a case also contains ’book-keeping’ information
book keeping
time reference, the number of times it has been used to
solve new problems, etc.
25. TO RETRIEVE MOST APPLICABLE CASE
Indexed in multiple ways. Those are broadly categorized
in three types:
Relevant input features : This is the subset of the input
descriptors that has been explained to be relevant for solving
R. Aker
the problem. If a numeric descriptor was transformed into a
symbolic value, this becomes the value of the index feature.
b li l hi b h l f h i d f
rkar
Derived findings : These are findings, which may disqualify a
case, since they point to cases that probably are better matches
if such a finding is involved. Differential findings are inserted in a
involved
case reminded of when this case does not lead to a successful
solution. Hence, they are indices between rather similar cases,
although different enough to associate different solutions.
Plan : S
Pl Successful as well as failed solutions provide indices to
f l ll f il d l ti id i di t
the cases. The primary use of indices which are successful plan
is for the retrieval of cases in order to find proper intake.
Indices that are failed solutions are used to avoid retrieval of
similar failures while solving new problems. 25
26. REFERENCES
Akerkar, R. and Sajja, P. Knowledge Based Systems, Jones and
Bartlett, 2009.
Jamsandekar, P., Akerkar, R. Dietary Planner, A Case Based
R. Aker
Reasoning System; In proceedings of Recent Trends in IT, A National
Conference, Amaravati, India, 2000.
Co fere ce A aravati I dia 2000
rkar
Kolodner, J. Case based Reasoning: Morgan Kauffmann Publisher;
San Mateo, CA, 1993.
Dietary Guidelines for Indians – A Manual, National Institute of
Manual
Nutrition, ICMR, Hydrabad.
26