Health-e-Child CaseReasoner

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Presentation in Darmstadt for LWA 2009

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Health-e-Child CaseReasoner

  1. 1. HeC CaseReasoner: Neighborhood Graph for Clinical Case Retrieval and Decision Support LWA 2009 Darmstadt, Germany 21 th – 23 th September 2009 Gabor Rendes Alexey Tsymbal Siemens AG, Erlangen, Germany
  2. 2. Overview <ul><li>Health-e-Child CaseReasoner </li></ul><ul><li>Neighborhood Graphs </li></ul><ul><li>Clustering algorithms </li></ul><ul><li>Learning Discriminative Distance Functions </li></ul><ul><li>Framework and Developement </li></ul>
  3. 4. Motivation <ul><ul><li>Case-Based Reasoning (CBR) system -> reasoning by similarity </li></ul></ul><ul><ul><li>Challenges: </li></ul></ul><ul><ul><ul><li>complex clinical data </li></ul></ul></ul><ul><ul><ul><li>lack of transparency and explanation </li></ul></ul></ul><ul><ul><li>Very generic </li></ul></ul><ul><ul><ul><li>basically works for any type of data (all you need is a distance function)‏ </li></ul></ul></ul><ul><ul><ul><li>has been applied to gene expression, 3D anatomical meshes, clinical data </li></ul></ul></ul><ul><ul><li>User defined or learned distances </li></ul></ul>
  4. 5. Motivation <ul><ul><li>Task: </li></ul></ul><ul><ul><ul><li>Display inter-patient proximity for the given context with the purpose of decision making and possibly knowledge discovery </li></ul></ul></ul><ul><ul><li>Visualization using neighborhood graphs </li></ul></ul><ul><ul><ul><li>Overview + explanation </li></ul></ul></ul><ul><ul><li>Potential clinical use cases </li></ul></ul><ul><ul><ul><li>Diagnosis: have similar patients been diagnosed as diseased? </li></ul></ul></ul><ul><ul><ul><li>Treatment selection: did similar patients profit from treatment (surgery, chemotherapy, etc.) </li></ul></ul></ul>
  5. 6. Neighborhood Graphs <ul><ul><li>Node-link entity-relationship representation </li></ul></ul><ul><ul><li>Three basic types </li></ul></ul><ul><ul><ul><li>Relative Neighborhood Graphs (RNG): A and B are connected if: (no patient C is closer to both A and B than AB) [Toussaint, 1980] </li></ul></ul></ul><ul><ul><ul><li>In a nearest neighbor graphs, each case is connected with one or a set of its nearest neighbors </li></ul></ul></ul><ul><ul><ul><li>A threshold graph is simply defined as a graph where two vertices are connected with an edge if the distance between the two corresponding cases is less than a certain threshold </li></ul></ul></ul><ul><ul><li>The graphs nicely represent patient clusterings, are adaptive to the distance function </li></ul></ul><ul><ul><li>Node-link entity-relationship representation </li></ul></ul><ul><ul><li>Three basic types </li></ul></ul><ul><ul><ul><li>Relative Neighborhood Graphs (RNG): A and B are connected if: (no patient C is closer to both A and B than AB) [Toussaint, 1980] </li></ul></ul></ul><ul><ul><ul><li>In a nearest neighbor graphs, each case is connected with one or a set of its nearest neighbors </li></ul></ul></ul><ul><ul><ul><li>A threshold graph is simply defined as a graph where two vertices are connected with an edge if the distance between the two corresponding cases is less than a certain threshold </li></ul></ul></ul><ul><ul><li>The graphs nicely represent patient clusterings, are adaptive to the distance function </li></ul></ul>
  6. 7. Neighborhood Graphs functionality <ul><ul><li>Node colouring to represent numeric and nominal attributes </li></ul></ul><ul><ul><li>Node filtering according to values of nominal and numeric attributes in patient record </li></ul></ul><ul><ul><li>Edge colouring and filtering according to the underlying distance </li></ul></ul><ul><ul><li>Patient clustering (Newman's on the graph and “semantic” in the original space)‏ </li></ul></ul><ul><ul><li>Mesh visualization and transformation (within node, e.g. meshes corresponding to the pulmonary trunk)‏ </li></ul></ul><ul><ul><li>Reconfigurable tooltips displaying patient records and images </li></ul></ul><ul><ul><li>Nearest Neighborhood classification and regression performance visualization for each node, for a selected class attribute and a certain similarity context </li></ul></ul>
  7. 8. GUI for NGraphs: plain nodes Toolbar Statusbar Graph panel
  8. 9. GUI for NGraphs: mesh visualization Besides clinical data and patient similarities, graphs are nicely suitable for displaying meshes corresponding to patients. Same operations can be used as for usual graphs; also meshes can be scaled and rotated
  9. 10. Clustering <ul><ul><li>Two algorithms applied: </li></ul></ul><ul><ul><ul><li>Girvan-Newman's algorithm for graphs </li></ul></ul></ul><ul><ul><ul><li>“ Semantic clustering” for general data </li></ul></ul></ul><ul><ul><li>Both algorithms produce a hierarchical, top-down clustering tree </li></ul></ul><ul><ul><li>Intuitive, interactive navigation among the clusters </li></ul></ul><ul><ul><li>Newman's clustering is clustering on the graph </li></ul></ul><ul><ul><ul><li>Starting from the fill weighted graph, it iteratively removes edges according to the edge betweenness criterion (it removes “key links”), so that the graph is split into disconnected clusters </li></ul></ul></ul><ul><ul><ul><li>Popular in the social networks community </li></ul></ul></ul>
  10. 11. Semantic Clustering <ul><ul><li>Goal: provide a top-down hierarchical clustering with semantic splits (rules like “age>10”) </li></ul></ul><ul><ul><li>Intra-cluster variances (square of the distance from centroid) re considered before and after the split </li></ul></ul><ul><ul><li>Almost “every” possible split is considered for every existing cluster </li></ul></ul><ul><ul><li>At every step the cluster with the currently highest “gain” (difference) will be selected for a real split </li></ul></ul>
  11. 12. GUI for Clustering
  12. 13. Distance Functions <ul><ul><li>Underlying distance functions for similarity </li></ul></ul><ul><ul><li>Two option is considered </li></ul></ul><ul><ul><ul><li>Canonical distance: Euclidean metric, weighted </li></ul></ul></ul><ul><ul><ul><li>Learn a strong distance function for a given classification context, two techniques: </li></ul></ul></ul><ul><ul><ul><ul><li>Learning from equivalence constraints in the product or difference space </li></ul></ul></ul></ul><ul><ul><ul><ul><li>The intrinsic Random Forest (RF) distance </li></ul></ul></ul></ul>
  13. 14. Learning Discriminative Functions <ul><ul><li>Learning discriminative distance functions </li></ul></ul><ul><ul><ul><li>Helps to combine the power of strong learners as AdaBoost, RF, SVM, with the transparency of case retrieval and nearest neighbor classification </li></ul></ul></ul><ul><ul><ul><li>Is especially useful for data sets with many irrelevant, weakly relevant and correlated features (e.g. images or clinical data)‏ </li></ul></ul></ul>
  14. 15. Learning Discriminative Functions: two techniques <ul><ul><li>Learning from so called weak representation, equivalence constraints </li></ul></ul><ul><ul><ul><li>Pairs of cases are considered, and the label displays whether they belong to the same class </li></ul></ul></ul><ul><ul><ul><li>Original space is transformed into a product or difference space for equivalence constraints </li></ul></ul></ul><ul><ul><ul><li>Any technique can be used to learn in the new space </li></ul></ul></ul><ul><ul><ul><li>Originates from imaging, no known works in other domains so far </li></ul></ul></ul><ul><ul><li>Using intrinsic Random Forest distance </li></ul></ul><ul><ul><ul><li>Two cases are more similar to each other if they fall into more of same leaves in a forest </li></ul></ul></ul><ul><ul><ul><li>Breiman predicted the power of this distance in his works, however it is still used not so often, mostly for clustering genetic data </li></ul></ul></ul>
  15. 16. Distance learning from equivalence constraints <ul><ul><li>Usually are represented using triplets (x 1 ,x 2 ,y), where x 1 and x 2 are points in the original space and y ∈ {+1, -1} </li></ul></ul><ul><ul><li>Originates and finds most applications so far from imaging </li></ul></ul><ul><ul><li>Was shown to work well for multidimensional data with many irrelevant and redundant features (as in imaging) </li></ul></ul><ul><ul><li>The following function is learnt directly which was shown to be optimal under iid [Mahamud and Hebert, 2003] : </li></ul></ul>
  16. 17. Intrinsic RF distance <ul><ul><li>Is rather a “black horse”; has never been compared with learning from equivalence constraints so far </li></ul></ul><ul><ul><li>Not many applications yet, the most popular is clustering genetic data </li></ul></ul><ul><ul><ul><li>See e.g. [Shi and Horvath, 2006] </li></ul></ul></ul><ul><ul><li>But: usually good performance is reported </li></ul></ul><ul><ul><li>RF similarity between two cases x 1 and x 2: where K is number of trees and z ij is terminal position of case x i in tree j </li></ul></ul>
  17. 18. Learning distance: experimental methodology <ul><ul><li>Two approaches to learning discriminative distance; learning from equivalence constraints and intrinsic RF distance compared </li></ul></ul><ul><ul><li>Equivalence constraints are learnt using AdaBoost and RF in product and difference spaces </li></ul></ul><ul><ul><li>Compared with simple learners; k-NN, AdaBoost and RF </li></ul></ul><ul><ul><li>9 benchmark data sets; 1 SCR mesh data, 3 UCI clinical data sets, 4 gene expression data sets, 1 mass spectrometry data set </li></ul></ul><ul><ul><li>LOO for genetic and mesh data, 70/30% CV for the rest </li></ul></ul>
  18. 19. CaseReasoner: the application <ul><ul><li>The basic philosophy: provide clinicians with a flexible and interactive tool </li></ul></ul><ul><ul><li>Explore and compare the patients' records </li></ul></ul><ul><ul><li>Various visualization techniques </li></ul></ul><ul><ul><ul><li>User define similarity context </li></ul></ul></ul><ul><ul><ul><li>View basic statistics of the retrieved cases </li></ul></ul></ul><ul><ul><ul><li>Visualize them utilizing neighborhood graphs, treemaps and heatmaps </li></ul></ul></ul>
  19. 20. CaseReasoner: the workflow
  20. 21. CaseReasoner: the framework <ul><ul><li>Using a less strict variation of the Presentation-Abstraction-Control (PAC) pattern </li></ul></ul><ul><ul><li>Hierarchical structure of 'agents' </li></ul></ul><ul><ul><li>The core and the modules communicate with each other only through their Controller part </li></ul></ul><ul><ul><li>Flexible, easy module adding/removal, common libraries, simple workflow, clear APIs </li></ul></ul>
  21. 22. Conclusion <ul><ul><li>Ngraphs are an alternative way to represent clinical data and interpatient similarity for knowledge discovery and decision support </li></ul></ul><ul><ul><li>Learning discriminative distances is a way to combine the power of string black box learners with the transparency of case retrieval and nearest neighbor classification </li></ul></ul><ul><ul><li>Decision support becomes transparent </li></ul></ul><ul><ul><li>Flexible architecture makes life easier </li></ul></ul>
  22. 23. Ongoing / Future work <ul><ul><li>Comparison with other distance learning techinques </li></ul></ul><ul><ul><ul><li>RCA (Relevant Component Analysis), etc. </li></ul></ul></ul><ul><ul><li>One-class learning from positive or negative constraints only </li></ul></ul><ul><ul><li>Incrementalization of learning from equivalence constraints </li></ul></ul><ul><ul><ul><li>Existing incrementalizations of boosting are far from being lossless and converge very slowly </li></ul></ul></ul><ul><ul><ul><li>Use incrementalization of RF instead? There exist lossless incrementalizations both for bagging and decision trees </li></ul></ul></ul><ul><ul><li>Support of comparative constraints </li></ul></ul><ul><ul><ul><li>Triplet (a,b,c): a is closer to b than to c </li></ul></ul></ul><ul><ul><li>Application to other domains </li></ul></ul><ul><ul><ul><li>Prediction of suitability to PPVI (mesh-based), VHD treatment </li></ul></ul></ul><ul><ul><ul><li>Brain tumour classification on microarray dara </li></ul></ul></ul>
  23. 24. Thank you! Any questions?

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