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  • 1. Data mining and its application and usage in medicine By Radhika
  • 2. Data Mining and Medicine
    • History
      • Past 20 years with relational databases
        • More dimensions to database queries
      • earliest and most successful area of data mining
      • Mid 1800s in London hit by infectious disease
        • Two theories
          • Miasma theory  Bad air propagated disease
          • Germ theory  Water-borne
        • Advantages
          • Discover trends even when we don’t understand reasons
          • Discover irrelevant patterns that confuse than enlighten
          • Protection against unaided human inference of patterns provide quantifiable measures and aid human judgment
      • Data Mining
        • Patterns persistent and meaningful
        • Knowledge Discovery of Data
  • 3. The future of data mining
    • 10 biggest killers in the US
    • Data mining = Process of discovery of interesting, meaningful and actionable patterns hidden in large amounts of data
  • 4. Major Issues in Medical Data Mining
    • Heterogeneity of medical data
      • Volume and complexity
      • Physician’s interpretation
      • Poor mathematical categorization
      • Canonical Form
      • Solution: Standard vocabularies, interfaces between different sources of data integrations, design of electronic patient records
    • Ethical, Legal and Social Issues
      • Data Ownership
      • Lawsuits
      • Privacy and Security of Human Data
      • Expected benefits
      • Administrative Issues
  • 5. Why Data Preprocessing?
    • Patient records consist of clinical, lab parameters, results of particular investigations, specific to tasks
      • Incomplete : lacking attribute values, lacking certain attributes of interest, or containing only aggregate data
      • Noisy : containing errors or outliers
      • Inconsistent : containing discrepancies in codes or names
      • Temporal chronic diseases parameters
    • No quality data, no quality mining results!
      • Data warehouse needs consistent integration of quality data
      • Medical Domain, to handle incomplete, inconsistent or noisy data, need people with domain knowledge
  • 6. What is Data Mining? The KDD Process Data Cleaning Data Integration Databases Data Warehouse Knowledge Task-relevant Data Selection Data Mining Pattern Evaluation
  • 7. From Tables and Spreadsheets to Data Cubes
    • A data warehouse is based on a multidimensional data model that views data in the form of a data cube
    • A data cube, such as sales, allows data to be modeled and viewed in multiple dimensions
      • Dimension tables , such as item (item_name, brand, type), or time(day, week, month, quarter, year)
      • Fact table contains measures (such as dollars_sold) and keys to each of related dimension tables
    • W. H. Inmon:“A data warehouse is a subject-oriented , integrated , time-variant , and nonvolatile collection of data in support of management’s decision-making process.”
  • 8. Data Warehouse vs. Heterogeneous DBMS
    • Data warehouse: update-driven, high performance
      • Information from heterogeneous sources is integrated in advance and stored in warehouses for direct query and analysis
      • Do not contain most current information
      • Query processing does not interfere with processing at local sources
      • Store and integrate historical information
      • Support complex multidimensional queries
  • 9. Data Warehouse vs. Operational DBMS
    • OLTP (on-line transaction processing)
      • Major task of traditional relational DBMS
      • Day-to-day operations: purchasing, inventory, banking, manufacturing, payroll, registration, accounting, etc.
    • OLAP (on-line analytical processing)
      • Major task of data warehouse system
      • Data analysis and decision making
    • Distinct features (OLTP vs. OLAP):
      • User and system orientation: customer vs. market
      • Data contents: current, detailed vs. historical, consolidated
      • Database design: ER + application vs. star + subject
      • View: current, local vs. evolutionary, integrated
      • Access patterns: update vs. read-only but complex queries
  • 10.  
  • 11. Why Separate Data Warehouse?
    • High performance for both systems
      • DBMS tuned for OLTP: access methods, indexing, concurrency control, recovery
      • Warehouse tuned for OLAP: complex OLAP queries, multidimensional view, consolidation
    • Different functions and different data:
      • Missing data: Decision support requires historical data which operational DBs do not typically maintain
      • Data consolidation: DS requires consolidation (aggregation, summarization) of data from heterogeneous sources
      • Data quality: different sources typically use inconsistent data representations, codes and formats which have to be reconciled
  • 12.  
  • 13.  
  • 14. Typical OLAP Operations
    • Roll up (drill-up): summarize data
      • by climbing up hierarchy or by dimension reduction
    • Drill down (roll down): reverse of roll-up
      • from higher level summary to lower level summary or detailed data, or introducing new dimensions
    • Slice and dice:
      • project and select
    • Pivot (rotate):
      • reorient the cube, visualization, 3D to series of 2D planes.
    • Other operations
      • drill across: involving (across) more than one fact table
      • drill through: through the bottom level of the cube to its back-end relational tables (using SQL)
  • 15.  
  • 16.  
  • 17. Multi-Tiered Architecture Data Warehouse OLAP Engine Analysis Query Reports Data mining Monitor & Integrator Metadata Data Sources Front-End Tools Serve Data Marts Data Storage OLAP Server Extract Transform Load Refresh Operational DBs other sources
  • 18. Steps of a KDD Process
    • Learning the application domain:
      • relevant prior knowledge and goals of application
    • Creating a target data set: data selection
    • Data cleaning and preprocessing: (may take 60% of effort!)
    • Data reduction and transformation:
      • Find useful features, dimensionality/variable reduction, invariant representation.
    • Choosing functions of data mining
      • summarization, classification, regression, association, clustering.
    • Choosing the mining algorithm(s)
    • Data mining: search for patterns of interest
    • Pattern evaluation and knowledge presentation
      • visualization, transformation, removing redundant patterns, etc.
    • Use of discovered knowledge
  • 19. Common Techniques in Data Mining
    • Predictive Data Mining
      • Most important
      • Classification: Relate one set of variables in data to response variables
      • Regression: estimate some continuous value
    • Descriptive Data Mining
      • Clustering: Discovering groups of similar instances
      • Association rule extraction
        • Variables/Observations
      • Summarization of group descriptions
  • 20. Leukemia
    • Different types of cells look very similar
    • Given a number of samples (patients)
      • can we diagnose the disease accurately?
      • Predict the outcome of treatment?
      • Recommend best treatment based of previous treatments?
    • Solution: Data mining on micro-array data
    • 38 training patients, 34 testing patients ~ 7000 patient attributes
    • 2 classes: Acute Lymphoblastic Leukemia(ALL) vs Acute Myeloid Leukemia (AML)
  • 21. Clustering/Instance Based Learning
    • Uses specific instances to perform classification than general IF THEN rules
    • Nearest Neighbor classifier
    • Most studied algorithms for medical purposes
    • Clustering– Partitioning a data set into several groups (clusters) such that
      • Homogeneity: Objects belonging to the same cluster are similar to each other
      • Separation: Objects belonging to different clusters are dissimilar to each other. 
    • Three elements
      • The set of objects
      • The set of attributes
      • Distance measure
  • 22. Measure the Dissimilarity of Objects
    • Find best matching instance
    • Distance function
      • Measure the dissimilarity between a pair of data objects
    • Things to consider
      • Usually very different for interval-scaled , boolean , nominal , ordinal and ratio-scaled variables
      • Weights should be associated with different variables based on applications and data semantic
    • Quality of a clustering result depends on both the distance measure adopted and its implementation
  • 23. Minkowski Distance
    • Minkowski distance: a generalization
    • If q = 2, d is Euclidean distance
    • If q = 1, d is Manhattan distance
    x i x j q=2 q=1 6 6 12 8.48 X i (1,7) X j (7,1)
  • 24. Binary Variables
    • A contingency table for binary data
    • Simple matching coefficient
    Object i Object j
  • 25. Dissimilarity between Binary Variables
    • Example
    Object 1 Object 2 1 0 0 0 1 1 1 Object 2 0 0 1 1 1 0 1 Object 1 A7 A6 A5 A4 A3 A2 A1 7 3 4 sum 3 1 2 0 4 2 2 1 sum 0 1
  • 26. K-nearest neighbors algorithm
    • Initialization
      • Arbitrarily choose k objects as the initial cluster centers (centroids)
    • Iteration until no change
      • For each object O i
        • Calculate the distances between O i and the k centroids
        • (Re)assign O i to the cluster whose centroid is the closest to O i
      • Update the cluster centroids based on current assignment
  • 27. k -Means Clustering Method cluster mean current clusters new clusters objects relocated
  • 28. Dataset
    • Data set from UCI repository
    • http://kdd.ics.uci.edu/
    • 768 female Pima Indians evaluated for diabetes
    • After data cleaning 392 data entries
  • 29. Hierarchical Clustering
    • Groups observations based on dissimilarity
    • Compacts database into “labels” that represent the observations
    • Measure of similarity/Dissimilarity
      • Euclidean Distance
      • Manhattan Distance
    • Types of Clustering
      • Single Link
      • Average Link
      • Complete Link
  • 30. Hierarchical Clustering: Comparison Average-link Centroid distance Single-link Complete-link 1 2 3 4 5 6 1 2 5 3 4 1 2 3 4 5 6 1 2 5 3 4 1 2 3 4 5 6 1 2 5 3 4 1 2 3 4 5 6 1 2 3 4 5
  • 31. Compare Dendrograms 2 5 3 6 4 1 Average-link Centroid distance Single-link Complete-link 1 2 5 3 6 4 1 2 5 3 6 4 1 2 5 3 6 4
  • 32. Which Distance Measure is Better?
    • Each method has both advantages and disadvantages; application-dependent
    • Single-link
      • Can find irregular-shaped clusters
      • Sensitive to outliers
    • Complete-link, Average-link, and Centroid distance
      • Robust to outliers
      • Tend to break large clusters
      • Prefer spherical clusters
  • 33. Dendrogram from dataset
    • Minimum spanning tree through the observations
    • Single observation that is last to join the cluster is patient whose blood pressure is at bottom quartile, skin thickness is at bottom quartile and BMI is in bottom half
    • Insulin was however largest and she is 59-year old diabetic
  • 34. Dendrogram from dataset
    • Maximum dissimilarity between observations in one cluster when compared to another
  • 35. Dendrogram from dataset
    • Average dissimilarity between observations in one cluster when compared to another
  • 36. Supervised versus Unsupervised Learning
    • Supervised learning (classification)
      • Supervision: Training data (observations, measurements, etc.) are accompanied by labels indicating the class of the observations
      • New data is classified based on training set
    • Unsupervised learning (clustering)
      • Class labels of training data are unknown
      • Given a set of measurements, observations, etc., need to establish existence of classes or clusters in data
  • 37.
    • Derive models that can use patient specific information, aid clinical decision making
    • Apriori decision on predictors and variables to predict
    • No method to find predictors that are not present in the data
    • Numeric Response
      • Least Squares Regression
    • Categorical Response
      • Classification trees
      • Neural Networks
      • Support Vector Machine
    • Decision models
      • Prognosis, Diagnosis and treatment planning
      • Embed in clinical information systems
    Classification and Prediction
  • 38. Least Squares Regression
    • Find a linear function of predictor variables that minimize the sum of square difference with response
    • Supervised learning technique
    • Predict insulin in our dataset :glucose and BMI
  • 39. Decision Trees
    • Decision tree
      • Each internal node tests an attribute
      • Each branch corresponds to attribute value
      • Each leaf node assigns a classification
    • ID3 algorithm
      • Based on training objects with known class labels to classify testing objects
      • Rank attributes with information gain measure
      • Minimal height
        • least number of tests to classify an object
      • Used in commercial tools eg: Clementine
      • ASSISTANT
        • Deal with medical datasets
        • Incomplete data
        • Discretize continuous variables
        • Prune unreliable parts of tree
        • Classify data
  • 40. Decision Trees
  • 41. Algorithm for Decision Tree Induction
    • Basic algorithm (a greedy algorithm)
      • Attributes are categorical (if continuous-valued, they are discretized in advance)
      • Tree is constructed in a top-down recursive divide-and-conquer manner
      • At start, all training examples are at the root
      • Test attributes are selected on basis of a heuristic or statistical measure (e.g., information gain)
      • Examples are partitioned recursively based on selected attributes
  • 42. Training Dataset no excellent no medium 31…40 P14 yes fair yes high >40 P13 yes excellent no medium >40 P12 yes excellent yes medium <=30 P11 yes fair yes medium 31…40 P10 yes fair yes low <=30 P9 no fair no medium <=30 P8 yes excellent yes low >40 P7 no excellent yes low 31…40 P6 yes fair yes low 31…40 P5 yes fair no medium 31…40 P4 yes fair no high >40 P3 no excellent no high <=30 P2 no fair no high <=30 P1 Risk of Condition X Vision Hereditary BMI Age
  • 43. Construction of A Decision Tree for “Condition X” Age? >40 30…40 <=30 [P1,…P14] Yes: 9, No:5 [P1,P2,P8,P9,P11] Yes: 2, No:3 [P3,P7,P12,P13] Yes: 4, No:0 [P4,P5,P6,P10,P14] Yes: 3, No:2 History no yes [P1,P2,P8] Yes: 0, No:3 [P9,P11] Yes: 2, No:0 Vision fair excellent YES NO YES NO YES [P6,P14] Yes: 0, No:2 [P4,P5,P10] Yes: 3, No:0
  • 44. Entropy and Information Gain
    • S contains s i tuples of class C i for i = {1, ..., m }
    • Information measures info required to classify any arbitrary tuple
    • Entropy of attribute A with values {a 1 ,a 2 ,…,a v }
    • Information gained by branching on attribute A
  • 45. Entropy and Information Gain
    • Select attribute with the highest information gain (or greatest entropy reduction)
      • Such attribute minimizes information needed to classify samples
  • 46. Rule Induction
    • IF conditions THEN Conclusion
    • Eg: CN2
      • Concept description:
        • Characterization : provides a concise and succinct summarization of given collection of data
        • Comparison : provides descriptions comparing two or more collections of data
    • Training set, testing set
    • Imprecise
    • Predictive Accuracy
      • P/P+N
  • 47. Example used in a Clinic
    • Hip arthoplasty trauma surgeon predict patient’s long-term clinical status after surgery
    • Outcome evaluated during follow-ups for 2 years
    • 2 modeling techniques
      • Naïve Bayesian classifier
      • Decision trees
    • Bayesian classifier
      • P(outcome=good) = 0.55 (11/20 good)
      • Probability gets updated as more attributes are considered
      • P(timing=good|outcome=good) = 9/11 (0.846)
      • P(outcome = bad) = 9/20 P(timing=good|outcome=bad) = 5/9
  • 48. Nomogram
  • 49. Bayesian Classification
    • Bayesian classifier vs. decision tree
      • Decision tree: predict the class label
      • Bayesian classifier: statistical classifier; predict class membership probabilities
    • Based on Bayes theorem ; estimate posterior probability
    • Naïve Bayesian classifier:
      • Simple classifier that assumes attribute independence
      • High speed when applied to large databases
      • Comparable in performance to decision trees
  • 50. Bayes Theorem
    • Let X be a data sample whose class label is unknown
    • Let H i be the hypothesis that X belongs to a particular class C i
    • P( H i ) is class prior probability that X belongs to a particular class C i
      • Can be estimated by n i / n from training data samples
      • n is the total number of training data samples
      • n i is the number of training data samples of class C i
    Formula of Bayes Theorem
  • 51. More classification Techniques
    • Neural Networks
      • Similar to pattern recognition properties of biological systems
      • Most frequently used
        • Multi-layer perceptrons
          • Input with bias, connected by weights to hidden, output
        • Backpropagation neural networks
    • Support Vector Machines
      • Separate database to mutually exclusive regions
        • Transform to another problem space
        • Kernel functions (dot product)
        • Output of new points predicted by position
    • Comparison with classification trees
      • Not possible to know which features or combination of features most influence a prediction
  • 52. Multilayer Perceptrons
    • Non-linear transfer functions to weighted sums of inputs
    • Werbos algorithm
      • Random weights
      • Training set, Testing set
  • 53. Support Vector Machines
    • 3 steps
      • Support Vector creation
      • Maximal distance between points found
      • Perpendicular decision boundary
    • Allows some points to be misclassified
    • Pima Indian data with X1(glucose) X2(BMI)
  • 54. What is Association Rule Mining?
    • Finding frequent patterns, associations, correlations, or causal structures among sets of items or objects in transaction databases, relational databases, and other information repositories
    Example of Association Rules { High LDL, Low HDL }  { Heart Failure }
    • People who have high LDL (“bad” cholesterol), low HDL (“good cholesterol”) are at
    • higher risk of heart failure.
    High BMI , High LDL Low HDL , Heart Failure 5 High LDL Low HDL , Heart Failure 4 Diabetes 3 High LDL Low HDL , Heart Failure, Diabetes 2 High LDL Low HDL, High BMI, Heart Failure 1 Conditions PatientID
  • 55. Association Rule Mining
    • Market Basket Analysis
      • Same groups of items bought placed together
      • Healthcare
        • Understanding among association among patients with demands for similar treatments and services
      • Goal : find items for which joint probability of occurrence is high
      • Basket of binary valued variables
      • Results form association rules, augmented with support and confidence
  • 56. Association Rule Mining
    • Association Rule
      • An implication expression of the form X  Y, where X and Y are itemsets and X  Y= 
    • Rule Evaluation Metrics
      • Support (s): Fraction of transactions that contain both X and Y
      • Confidence (c): Measures how often items in Y appear in transactions that contain X
    Trans containing Y Trans containing both X and Y Trans containing X D
  • 57. The Apriori Algorithm
    • Starts with most frequent 1-itemset
    • Include only those “items” that pass threshold
    • Use 1-itemset to generate 2-itemsets
    • Stop when threshold not satisfied by any itemset
    • L 1 = {frequent items};
    • for (k = 1; L k !=  ; k++) do
      • Candidate Generation: C k+1 = candidates generated from L k ;
      • Candidate Counting: for each transaction t in database do increment the count of all candidates in C k+1 that are contained in t
      • L k+1 = candidates in C k+ 1 with min_sup
    • return  k L k ;
  • 58. Apriori-based Mining b, e 40 a, b, c, e 30 b, c, e 20 a, c, d 10 Items TID Min_sup=0.5 1 d 3 e 3 c 3 b 2 a Sup Itemset Data base D 1-candidates Scan D 3 e 3 c 3 b 2 a Sup Itemset Freq 1-itemsets bc ae ac ce be ab Itemset 2-candidates ce be bc ae ac ab Itemset 2 1 2 2 3 1 Sup Counting Scan D ce be bc ac Itemset 2 2 2 3 Sup Freq 2-itemsets bce Itemset 3-candidates bce Itemset 2 Sup Freq 3-itemsets Scan D
  • 59. Principle Component Analysis
    • Principle Components
      • In cases of large number of variables, highly possible that some subsets of the variables are very correlated with each other. Reduce variables but retain variability in dataset
      • Linear combinations of variables in the database
        • Variance of each PC maximized
          • Display as much spread of the original data
        • PC orthogonal with each other
          • Minimize the overlap in the variables
        • Each component normalized sum of square is unity
          • Easier for mathematical analysis
      • Number of PC < Number of variables
        • Associations found
        • Small number of PC explain large amount of variance
      • Example 768 female Pima Indians evaluated for diabetes
        • Number of times pregnant, two-hour oral glucose tolerance test (OGTT) plasma glucose, Diastolic blood pressure, Triceps skin fold thickness, Two-hour serum insulin, BMI, Diabetes pedigree function, Age, Diabetes onset within last 5 years
  • 60. PCA Example
  • 61. National Cancer Institute
    • CancerNet http://www.nci.nih.gov
    • CancerNet for Patients and the Public
    • CancerNet for Health Professionals
    • CancerNet for Basic Reasearchers
    • CancerLit
  • 62. Conclusion
    • About ¾ billion of people’s medical records are electronically available
    • Data mining in medicine distinct from other fields due to nature of data: heterogeneous, with ethical, legal and social constraints
    • Most commonly used technique is classification and prediction with different techniques applied for different cases
    • Associative rules describe the data in the database
    • Medical data mining can be the most rewarding despite the difficulty
  • 63.
    • Thank you !!!