1. داده كاوي و كاربرد آن در پزشكي بنام خدا نام دانشجو : بابك رزاقي شماره دانشجويي : 85233510 استاد راهنما : جناب آقاي دكتر توحيد خواه ( سمينار درس كاربرد فناوري اطلاعات در پزشكي )

2.  Necessity is mother of invention  Huge amounts of data  Electronic records of our decisions  Choices in the supermarket  Financial records  Our comings and goings  We swipe our way through the world – every swipe is a record in a database  Data rich – but information poor  Lying hidden in all this data is information! 2

3. 3  Extracting or “mining” knowledge from large amounts of data  Data -driven discovery and modeling of hidden patterns in large volumes of data  Extraction of implicit, previously unknown and unexpected, potentially extremely useful information from data

4. 4 Large database Data mining Data visualization  Ways of seeing patterns in large data sets  Uses the efficiency of human pattern recognition

5. 5  Gold Mining  Knowledge mining from databases  Knowledge extraction  Data/pattern analysis  Knowledge Discovery Databases or KDD

6. 6 ____ __ __ Transformed Data Patterns and Rules Target Data Raw Data Knowledge Data Mining Transformation Interpretation & Evaluation Selection & Cleaning Integration Understanding Knowledge Discovery Process DATA Ware house Knowledge

7. 7 Find true patterns and avoid overfitting (false patterns due to randomness)

8. 8  Classification: predicting an item class  Clustering: finding clusters in data  Associations: e.g. A & B & C occur frequently  Visualization: to facilitate human discovery  Summarization: describing a group  Estimation: predicting a continuous value  Deviation Detection: finding changes  Link Analysis: finding relationships

9. 9  Computationally expensive to investigate all possibilities  Dealing with noise/missing information and errors in data  Choosing appropriate attributes/input representation  Finding the minimal attribute space  Finding adequate evaluation function(s)  Extracting meaningful information  Not over fitting

10. 10 INSIGHTFUL MINER Angoss Knowledge ACCESS ARMiner Eudaptics Viscovery Goal TV MDR Viscovery SOMine SPSS

11. 11  Science: Chemistry, Physics  Bioscience  Sequence-based analysis  Protein structure and function prediction  Protein family classification  Microarray gene expression  Financial Industry - banks, businesses, e-commerce  Stock and investment analysis  Pharmaceutical companies  Health care  Sports and Entertainment

12. Clinical Data Mining processes  Digital format for all pertinent data  Create structure  Obtain coded information  Natural language understanding  Create a widely accessible repository 12

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14. Minimum systolic blood pressure over a 24-hour period following admission to the hospital Class 2: Early death Age of Patient Class 1: Survivors Was there sinus tachycardia? Class 1: Survivors Class 2: Early death <= 91 > 91 <=62.5 >62.5 YES NO 14

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16. 16  An organism’s genome is the “program” for making the organism, encoded in DNA  Human DNA has about 30-35,000 genes  A gene is a segment of DNA that specifies how to make a protein  Cells are different because of differential gene expression  About 40% of human genes are expressed at one time  Microarray devices measure gene expression

17. 17 Gene Value D26528_at 193 D26561_cds1_at -70 D26561_cds2_at 144 D26561_cds3_at 33 D26579_at 318 D26598_at 1764 D26599_at 1537 D26600_at 1204 D28114_at 707 Scanner enlarged section of raw image raw data

18. 18  New and better molecular diagnostics  New molecular targets for therapy  few new drugs, large pipeline, …  Outcome depends on genetic signature  best treatment?  Fundamental Biological Discovery  finding and refining biological pathways  Personalized medicine ?!

19. 19  Avoiding false positives, due to  too few records (samples), usually < 100  too many columns (genes), usually > 1,000  Model needs to be robust in presence of noise  For reliability need large gene sets; for diagnostics or drug targets, need small gene sets  Estimate class probability  Model needs to be explainable to biologists

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29. 29  Discover useful relationships in data  Discover information otherwise overlooked  Provide intelligence to improve various phases  Intellectual property  Competitive advantages:  Getting more out of your data  Finding other relevant information faster  Exploratory, hypothesis-generating analyses  Increase productivity – reduced amount of time and money

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31. 31 Thank You All razaghi.b@gmail.com