Medical Expert System - Improving Healthcare Quailty

1. ARTIFICIAL INTELLIGENCE
DIAGNOSIS
(AID)
Group Members: Supervisor: Dr. Muhammad Sarim
Mahira Akhtar
Aly Akbar Ali Hirji
Hammad Ahmed
Muhammad Hassan Siddiqui
18 December 2017 1

2. OBJECTIVES
• Assist medical professionals in diagnosis
• Predict probable disease and diagnosis
• Provide personalized healthcare to patients
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3. MOTIVATION & BACKGROUND
• Too many patients but very few doctors
• Doctors short on time and overlook details
• Lab tests end up in false diagnosis
• Diagnosis is dependent on Doctor’s mood
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4. MOTIVATION & BACKGROUND
• EMR data is not utilized properly
– Patient’s personal information and medical history
not taken in account
– Patients are often prescribed unnecessary tests
• Demographic characteristics ignored
– Existing expert systems do not take them into account
– These account for significant differences in baselines
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5. METHODOLOGY
• Extract rules from data provided by UMDC
– This process will make use of Data mining methods
such as Neural Fuzzy learners
• Extract rules from medical literature
– Online repositories such as PubMed, Medscape, and
Wikipedia
– Crawl data from them using web crawlers such as
PHPcrawl
• Take baseline differences in account during rule
generation.
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6. METHODOLOGY
● Generated rules will be accessible to doctors
–Through an excel spreadsheet containing results
values of lab tests
–Rules presented in a table with each row
denoting test result parameter values for each
disease
–Doctors could add and edit parameter values and
diseases without need for any programming skills
● The rules will then be converted into XML for
updating the expert system
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7. METHODOLOGY
• Ranked list of possible diseases based on rules
and scoring
• Storage and retrieval of previous diagnosis of
patients to improve accuracy of prediction
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8. SYSTEM DESIGN OVERVIEW
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9. EXTENSIONS
• Use of Symptoms during the prediction
• Medical Analysis based on demographic characteristics such
as gender, residential address etc.
• Integration of expert system with an existing Hospital EMR
• Risk monitoring system to identify patients at risk
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10. DATA UNDERSTANDING
▪ The Blood Test Data provided by UMDC contains about 200,000 records
▪ Multiple test of about 54,000 patients
▪ Out of these, diagnosis of only 3000 is recorded
▪ Patient Tests:
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Test
Code
Test name Normal values range
1 Haemoglobin 11.5 – 18 (mg/dl)
17 Urea 10 – 50 (mg%)
18 Creatinine 0.5 – 1.5 (mg%)
25 Potassium 3.8 – 5.2 (ME q/L)
47 Glucose Fasting 70 – 110 (mg%)
48 Glucose Random 80 – 180 (mg%)

11. DATA UNDERSTANDING
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•Actual Data – Test Results Table

12. DATA UNDERSTANDING
•Actual Data – Vitals Table
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13. DATA UNDERSTANDING
•Actual Data – Diagnosis Table
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14. DATA UNDERSTANDING
•Correlation Matrix
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15. DATA UNDERSTANDING
• Problems with the data
― Multiple diagnosis of patients at the same date and time
― Test codes inconsistent with the test names
e.g. Haemoglobin records are classified under test code 1 and most of the
Glucose (fasting) records are classified under test code 47. However, a few of
the Glucose (fasting) records are misclassified under test code 1
― Some of the test names are not consistent
e.g Haemoglobin test name is recorded as “Haemoglobin”, “Hb”, and
“Haemoglobin %”
― Human Errors in data entry. E.g. Temperature recorded as 980 *F (prob he
was trying to record 98.0)
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16. DATA UNDERSTANDING
•Problems with the data
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17. DATA UNDERSTANDING
•Problems with the data
– Multiple test results values are recorded against the same registration number and the same
date and time.
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18. DATA UNDERSTANDING
–Test Value Inconsistency- above 800 cells found with text such as ‘127 (AFTER GLOCOUSE 01
HR)’ and ‘AFTER 75GRM GLOCOUSE 01HR (92)’
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19. DATA UNDERSTANDING
–Test Code and Test Name inconsistency problem solved by Excel formulas such
as:=IF(OR(P2="Haemoglobin %",P2="Hb"),"Haemoglobin",P2)
–And
=IF(N2="true",(MID(L2,SEARCH("(",L2)+1,SEARCH(")",L2,SEARCH("(",L2)+1)-SEARCH("(",L2)-1)),N2)
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20. DATA UNDERSTANDING
•Pivot Table: Test Codes as Columns, grouped by date and regno
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21. DATA CLEANING
•Handling Missing Values
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22. MODELLING – Naïve Bayes
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23. DATA CLEANING
•Binning: Out of range column added for test values using normal ranges
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24. DATA CLEANING
•Handling missing values: Since a patient whose test reports are cleared will have normal test range
values. So we handled those missing values by inserting the average of normal test range values
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25. KNIME Workflows
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26. KNIME Workflows
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27. MODEL AND PREDICTION
•Multinomial Naïve Bayes
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28. CONCLUSION
• Aim to build a Medical Expert System to assist medical
professionals especially doctors in diagnosis
• Want to make medical literature as a direct support for
diagnosis
• Want to allow patients to be provided personalised treatment
using their medical history
• Wish to serve the medical community as Computer Scientists,
considering the field’s interdisciplinary nature
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29. QUESTIONS?
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