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Data mining and its application and usage in medicine

Data mining and its application and usage in medicine. By Radhika. 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

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Data mining and its application and usage in medicine

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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 Knowledge Pattern Evaluation Data Mining Task-relevant Data Selection Data Warehouse Data Cleaning Data Integration Databases

  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. 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

  11. 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)

  12. other sources Extract Transform Load Refresh Operational DBs Multi-Tiered Architecture Monitor & Integrator OLAP Server Metadata Analysis Query Reports Data mining Serve Data Warehouse Data Marts Data Sources OLAP Engine Front-End Tools Data Storage

  13. 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

  14. 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

  15. 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)

  16. 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

  17. 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

  18. Minkowski Distance • Minkowski distance: a generalization • If q = 2, d is Euclidean distance • If q = 1, d is Manhattan distance xi Xi (1,7) 12 8.48 q=2 q=1 6 6 xj Xj(7,1)

  19. Binary Variables • A contingency table for binary data • Simple matching coefficient Object j Object i

  20. Dissimilarity between Binary Variables • Example Object 2 Object 1

  21. K-nearest neighbors algorithm • Initialization • Arbitrarily choose k objects as the initial cluster centers (centroids) • Iteration until no change • For each object Oi • Calculate the distances between Oi and the k centroids • (Re)assign Oi to the cluster whose centroid is the closest to Oi • Update the cluster centroids based on current assignment

  22. k-Means Clustering Method cluster mean current clusters objects relocated new clusters

  23. Dataset • Data set from UCI repository • http://kdd.ics.uci.edu/ • 768 female Pima Indians evaluated for diabetes • After data cleaning 392 data entries

  24. 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

  25. Hierarchical Clustering: Comparison 5 1 5 5 4 1 3 1 4 1 2 2 5 2 5 5 2 1 5 2 5 2 2 2 3 3 6 6 3 6 3 1 6 3 3 1 4 4 4 1 3 4 4 4 Single-link Complete-link Average-link Centroid distance

  26. Compare Dendrograms 1 2 5 3 6 4 1 2 5 3 6 4 1 2 5 3 6 4 Single-link Complete-link Centroid distance Average-link 2 5 3 6 4 1

  27. 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

  28. 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

  29. Dendrogram from dataset • Maximum dissimilarity between observations in one cluster when compared to another

  30. Dendrogram from dataset • Average dissimilarity between observations in one cluster when compared to another

  31. 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

  32. Classification and Prediction • 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

  33. 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

  34. 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

  35. Decision Trees

  36. 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

  37. Training Dataset

  38. Construction of A Decision Tree for “Condition X” [P4,P5,P10] Yes: 3, No:0 [P6,P14] Yes: 0, No:2 YES YES YES NO NO [P1,…P14] Yes: 9, No:5 Age? 30…40 <=30 >40 [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 Vision History no yes excellent fair [P9,P11] Yes: 2, No:0 [P1,P2,P8] Yes: 0, No:3

  39. Entropy and Information Gain • S contains si tuples of class Ci for i = {1, ..., m} • Information measures info required to classify any arbitrary tuple • Entropy of attribute A with values {a1,a2,…,av} • Information gained by branching on attribute A

  40. Entropy and Information Gain • Select attribute with the highest information gain (or greatest entropy reduction) • Such attribute minimizes information needed to classify samples

  41. 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

  42. 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

  43. Nomogram

  44. 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

  45. Bayes Theorem • Let X be a data sample whose class label is unknown • Let Hi be the hypothesis that X belongs to a particular class Ci • P(Hi) is class prior probability that X belongs to a particular class Ci • Can be estimated by ni/n from training data samples • n is the total number of training data samples • ni is the number of training data samples of class Ci Formula of Bayes Theorem

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