Embed Size (px)
Citation preview
1
- 1 -
Development of analysis system for constitution diagnosis
Chul Kim([email protected]) Senior Researcher
KIOM(Korea Institute of Oriental Medicine), KOREA
- 2 -
Learning Objectives
• Understand the whole process about clinical and biological data handling technique.
• Get the recent research information in building the diagnosis system.
• Make the best use of technique to correspond with your environment.
2
- 3 -
Table of contents
• The Goal of study
• Significance of Project
• Study Contents
- clinical information management system
- Analysis Algorithm for constitution diagnosis with clinical data
- Analysis Algorithm for constitution diagnosis with SNP data
• Conclusion
- 4 -
Goal of study
Optimizing Algorithm
Development of Information Analysis Algorithm for constitution diagnosis
Developing search and managing program
Development of analysis toolkit for constitution diagnosis
Clinical data biological data
Constructing infra for clinical and biological data analysis
3
- 5 -
Significance of Project
Collection of clinical data, blood sample
Measuring about diagnosis of oriental medicine doctor
Development of diagnosis standard
Developm
ent of Analysis
Algorithm
for constitution
Clinical DB
Developm
ent of drug System
for constitution
Multiple clinical study network
SNP Linkage Analysis
Analysis of clinical index Derivation of valid index
Development of
Integrated web system
Development of constitution diagnosis Tool
Integrated analysis Analysis tool
DNA
Clinical info.
Biological info.
DB construction (clinical DB, SNP DB)
DB collaboration
- 6 -
Study Contents – clinical information management system
E-CRF input program
clinical DB creation
9-medical center, 1-oriental medicine hospital, 4-Oriental medicine clinic
Management system
4
- 7 -
Web-based E-CRF(CASE REPORT FORM ) program • Management of 14 input clinical organs • Double-checking function about input data(demographic, figure, personality,
deficiency, disease, sickness, gynecology, drug response, diagnosis results, etc)
• Multi-media data(picture, audio or movie file) upload & download
Clinical DB • Size
– # of data: 1065, # of item: 181 • Replace subjective data by realistic data • # of collaboration organ : 9 medical center of 6 colleges of oriental medicine
, 1 oriental medicine hospital, 4 oriental medicine clinic
Study Contents – clinical information management system
- 8 -
Development of web-based management system of clinical information
• Design the identification code • System environment : Solaris OS + Apache + Tomcat + Altibase DB
MS • Inquiry function of sample ID and collected center • Service for full data download with excel format by checking
authority • Basic statistics to clinical data • Multiple condition search function
Study Contents – clinical information management system
5
- 9 -
clinical valid index - apply to valid items with ANOVA test - valid items of 181 clinical items are derived from decision tree analy
sis.
valid items significance level
14 items(angle of ribs, quantity of perspiration, etc) <0.001
9 items(voice purity and impurity, rate of hypertension, etc) 0.001~0.01
13 items(appetite, rate of asthma, etc) 0.01~0.05
181 clinical items
valid items
Decision tree Significances of valid items
ANOVA test
Study Contents – Analysis Algorithm for constitution diagnosis using clinical data
- 10 -
Study Contents – Analysis Algorithm for constitution diagnosis using clinical data
6
- 11 -
- Data cleaning low call rate / HWE / MAF - Missing Imputation - Format conversion
Genetic conversion -Additive / Dominant / Recessive
Relation analysis(X2)
SNP marker choice
Classification algorithm (HMM, SVM, Naïve Bayesian)
Validation
Choice of classification model
3) marker validation program
1) Data pre-process program
2) Association analysis program
SNP data input
Constitution prediction
4) Constitution prediction program
Associator v2.0 package
classification model
• 4 individual program module • error data handling: increase usefulness to SNP data • apply to multiple classification model • verification relation to Sample
Study Contents – Analysis Algorithm for constitution diagnosis using SNP data
- 12 -
Verification for separation ability about SNP marker
• prediction rate: 75%(100 marker), 90% (300 marker)
• SNP marker analysis (310 sample, 500K SNP data) • various classification algorithm (marker number : 2-300, classification model : 3, 10-fold CV)
object
result
method
Application for constitution classification algo.
Study Contents – Analysis Algorithm for constitution diagnosis using SNP data
7
- 13 -
Web associator
Study Contents – Analysis Algorithm for constitution diagnosis using SNP data
- 14 -
character of data Rate of accuracy(%)
Male Female
1st year data(# : 390 ) 87.5 88.1
2nd year data(# : 161 ) 76.0 54.1
co-analysis with KRIBB (# : 228 ) 62.2 80.0
average 77.6 78.6
Sasang constitution diagnosis result - sasang(taeyang, soyang, taeeum and soeum type) constitution dia
gnosis result using our algorithm
Conclusion
The objectification of clinical data is very important to raise the accuracy of constitution diagnosis.
8
- 15 -
Run # of Test
True Positive
Accuracy (%)
1 228 161 70.6
2 228 159 69.7
3 228 163 71.5
4 228 156 68.4
5 228 160 70.2
6 228 164 71.9
7 228 157 68.9
8 228 161 70.6
9 228 162 71.1
10 228 160 70.2
Average 228 160.3 70.3
228 samples -SVM model, 17 SNP marker (p<10-5) -constitution prediction rate : 70.3 % (10-fold CV)
Conclusion
Our system have the opportunity to improve in proportion to number of sample data and program improvement. We continued this system development and we will make the integrated analysis system for constitution diagnosis using clinical and biological information.
- 16 -
Information of authors
Order 2nd 3rd 4th 5th
Name Sang-Kyun Kim Hee-Jeong Jin Mi-Young Song Young- Joo Kim
title Senior Researcher
Senior Researcher
Principal Researcher
Principal Researcher
Organization KIOM KIOM KIOM KRIBB
country South Korea South Korea South Korea South Korea
Email skkim @kiom.re.kr [email protected] [email protected]
e.kr [email protected]
9
- 17 -
Q & A
Thank you !
