ai ppt

Fuzzy logic applied to a Patient Classification System

Presented by:Dheeraj Mor (121416012)Pallavi Patil (121416009)

Najuka Jagtap(121417015)Anuja Agharkar(121417008)

CONTENTS

• INTRODUCTION• LITERATURE SURVEY• FUZZY LOGIC CONCEPT• RESULT • CONCLUSION• FUTURE SCOPE • REFERENCE

INTRODUCTION• Requirement of optimization of hospital management.- Correct number of nurses and healthcare workers

• Why patient classification required?- nurse duty adjustment- Complexity level of each patient- Economy point of view

• Why fuzzy logic?-optimization-works best incase of uncertainity

LITERATURE SURVEY• Optimization prime concern

FUZZY CONCEPT

• Grouping patients according to nursing care required.

• An algorithm MAP(Metodo Assistenziale Professionalizzante – Professionalizing Healthcare Method)

• MAP evaluates patients on his clinical condition and environment.

• Three dimensions are:1)Clinical stability2)responsiveness3)Self sufficiency

• Dimensions along with environment is associated with set of characteristics for classification of patients.

• a list of variables is used to describe the possible patient conditions relative to a specific characteristic.

• This distribution enables minimum and recommended number of nurses

Model based on FUZZY• What is fuzzification?• Example: temperature of a patient can be model into four

categories :1) Normal temperature2) Hypothermia3) Hyperthermia4) Hyperyrexia

FUZZIFICATIONBody temperature = {Low, Medium, High}

RESULTS

Figure 1. Example of the body temperature characteristic as defined in theoriginal MAP version (left) and in the new version based on FL (right).

• Figure 2. Input (clinical stability, responsiveness, self-sufficiency,

• environment) and the output (complexity) MFs of the final FIS. Figure 2. Input (clinical stability, responsiveness, self-sufficiency,

• environment) and the output (complexity) MFs of the final FIS.

Figure 2. Input (clinical stability, responsiveness, self-sufficiency,environment) and the output (complexity) MFs of the final FIS.

Figure 3. Example of rule activation and patient classification in the finalFIS. In this rule the four input variables are connected among them with theAND operator.

CONCLUSION• Patient classification based on fuzzy logic enable nurses to

evaluate single patient in more efficient way.

• Optimization of clinical resource is possible.

THANK YOU!!