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Case Based Reasoning
 

Case Based Reasoning

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    Case Based Reasoning Case Based Reasoning Presentation Transcript

    • Diet Menu planning Using Case Based Reasoning R. Aker rkar Rajendra Akerkar 1 Presented at UKCBR 2009, Cambridge, UK
    • AGENDA Introduction Related work Diet Menu Plan R. Aker DietMaster rkar Example Conclusion 2
    • INTRODUCTION An application of case based reasoning (CBR) in th di t i the diet menu planning l i R. Akerkar r 3
    • 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
    • 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
    • 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
    • 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
    • DIETMASTER ARCHITECTURE ReasoningNewProblem 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
    • FUNCTIONAL COMPONENTS OFDIETMASTER 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
    • 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.
    • CASE STRUCTUREInput Case Case in process Learned CaseInput findings Input findings Relevant findingsGoal 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.
    • 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
    • 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
    • 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
    • CASE STRUCTUREAge (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 StatusGeneral Test details BP – Blood Pressure in mm1st Trimester Pregnancy g y Systolic, S stolic Diastolic2nd Trimester Pregnancy BGL Test Method3rd Trimester Pregnancy Plasma / Whole Blood / HbA1c / Urine /0 – 6 Mon Lactation Glycometer7 – 12 Mon Lactation BGL - Blood Glucose Level in mg/dl Fasting BGL , Postprandial BGL Body Frame Cholesterol mg/dl gSmall / Medium / Large Insulin 15 Eating Habit
    • CASE STRUCTURETypeDosage for a day in ccExercise(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 )mCurrent meal P ttC t l Pattern 16 (Intake, Food item, Unit Quantity)m
    • 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)
    • 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.
    • PROBLEM SOLVING R. Akerkar r 19
    • INTEGRATED REASONING R. Akerkar r 20
    • DETAILED CBR R. Akerkar r 21
    • THE LEARNINGALGORITHM R. Akerkar r 22
    • LEARNING IN DIETMASTER R. Akerkar r 23
    • 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.
    • 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
    • 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