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FROM TINNITUS DATA TO CLASSIFIERS CONSTRUCTION: Building Decision Support System for Diagnosis and Treatment of Tinnitu

FROM TINNITUS DATA TO CLASSIFIERS CONSTRUCTION: Building Decision Support System for Diagnosis and Treatment of Tinnitus. Zbigniew W. Ras 1 & Paul Jastreboff 2 & Pamela Thompson 1 1) University of North Carolina at Charlotte College of Computing and Informatics

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FROM TINNITUS DATA TO CLASSIFIERS CONSTRUCTION: Building Decision Support System for Diagnosis and Treatment of Tinnitu

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  1. FROM TINNITUS DATA TO CLASSIFIERS CONSTRUCTION: BuildingDecision Support Systemfor Diagnosis and Treatmentof Tinnitus Zbigniew W. Ras1 & Paul Jastreboff2 & Pamela Thompson1 1) University of North Carolina at Charlotte College of Computing and Informatics 2) Tinnitus and Hyperacusis Center Emory University School of Medicine

  2. In collaboration with Jan Rauch Department of Computer Science University of Economics, Prague, Czech Republic Research partially supported by the Project ME913 of the Ministry of Education, Youth, and Sports of the Czech Republic

  3. Methodology • Domain Knowledge • Data Collection • Data Preparation • New Feature Construction • Tolerance Relation Based Clustering & New Temporal Features • Classifiers Construction – [for Total Score or Difference in Total Score] • Action Rules Discovery [hints how to treat tinnitus] • Future Research From Music to Emotions and Tinnitus Treatment

  4. Neil Young, Barbra Streisand, Pete Townshend, William Shatner, David Letterman, Paul Schaffer, Steve Martin, Ronald Reagan, Neve Campbell, Jeff Beck, Burt Reynolds, Sting, Eric Clapton, Thomas Edison, Peter Jennings, Dwight D. Eisenhower, Cher, Phil Collins, Vincent Van Gogh, Ludwig Van Beethoven, Charles Darwin, . . . Introduction

  5. Introduction

  6. TRT includes • DIAGNOSIS • Preliminary medical examination • Completion of initial interview questionnaire • Audiological testing • TREATMENT • Counseling • Sound Habituation Therapy • Exposure to a different stimulus to reduce emotional reaction • Visit questionnaire (THI) • Secondary questionnaire (TFI) in new dataset • Instrument tracking (instruments can be table top or in ear, different manufacturers) • Continued audiological tests Methodology: Domain Knowledge

  7. Original Dataset • 555 patients • Relational • 11 tables • New Dataset • 758 patients • Relational • Secondary questionnaire - • Tinnitus Functional Index (TFI)

  8. Initial Interview form provides basis for initial patient classification. Category - 0 to 4 (stored in Questionnaires tables) 0 – low tinnitus only: counseling 1 – high tinnitus: sound generators set at mixing point 2 – high tinnitus w/hearing loss (subjective): hearing aid 3 – Hyperacusis: sound generators set above threshold of hearing 4 – persistent hyperacusis: sound generators set at the threshold; very slow increase of sound level Methodology: Database Features 9

  9. Tinnitus Functional Index New cognitive and emotional questions Scale of 0 to 10 and some % Includes questions related to Anxious/worried Bothered/upset Depressed This new set of features is mapped to “arousal-valence emotion plane” used for construction of emotion-based classifiers in music information retrieval domain (personalization aspects are considered as well). Methodology: Database Features 10 10

  10. Arousal-valence emotion plane - used in Automatic Indexing of Music by emotions 11

  11. New Features Based on the TFI and emotions New Feature Construction: TFI and Emotions 12

  12. Tinnitus Handicap Inventory • Questionnaire, forms Neumann-Q Table • Function, Emotion, Catastrophic Scores • Total Score (sum) • THI • 0 to 16: slight severity • 18 to 36: mild • 38 to 56: moderate • 58 to 76: severe • 78 to 100: catastrophic Methodology: Database Features

  13. New 8 decision attributes based on different discretizations of the difference in Total Score (between first and last visit) New Feature Construction: Decision Feature 14

  14. Data Transformation – ORIGINAL DATABASE • Flattened File (by Patient) From original database, one tuple per patient with addition of features • Discovered from Text Data • Statistical (standard deviations, averages, ..) • Temporal (sound level centroid, sound level spread, recovery rate) • Decision Feature – discretized Difference in Total Score from THI • Data Transformation – NEW DATABASE • Clustered patient-driven datasets (by similar visit patterns) with addition of features • Coefficients, angles

  15. Text Mining • Text fields • Demographic, Miscellaneous, Medication tables • Categories may show cause of tinnitus for patient • Stress, Noise, Medical: New Feature Construction: Text Features

  16. New Temporal Features • Sound Level Centroid T = Total number of Visits per patient (3) V is some sound level feature (ex. LDL measurement) measured at each visit V(1), V(2), V(3) 1/3*V(1) + 2/3 * V(2) + 3/3 * V(3) V(1) + V(2) + V(3) New Feature Construction: Temporal Features

  17. New Temporal Features • Sound Level Spread SQRT V(1) * (1/3-C)2 + V(2) * (2/3-C)2 + V(3) * (3/3 – C)2 V(1) + V(2) + V(3) New Feature Construction: Temporal Features

  18. New Temporal Features • Recovery Rate V = Total Score from THI Vo = first score (should be less than Vk) Vk is the best or min score in the vector Tk is the date of best score New Feature Construction: Temporal Features

  19. In Search for Optimal Classifiers describing Total Score or changes in Total Score [new decision attributes] • WEKA • J48 (C4.5 Decision Tree Learner) • Random Forest • Multilayer Perceptron Data Mining: Unclustered Data

  20. Experiments and Results • 1) Original data with Standard Deviations and Averages from Audiological features • 2) Original data with Standard Deviations, Averages, Sound level centroid and sound level spread (Sound) only • 3) Original data with Standard Deviations, Averages, and Text • 4) Original Data Standard Deviations, Averages, Text and Sound • 5) Original Data with Text • 6) Original Data with Sound • 7) Original Data with Sound, Text, and Recovery Rate • 8) Original Data with Sound, and Recovery Rate /the winner/ • 9) ………………………………………. Data Mining: Unclustered Data

  21. Top Classification Results for all 8 decision variables Original Data with Sound Level Centroid, Sound Level Spread, Recovery Rate Data Mining: Unclustered Data

  22. Continuing the Search for Optimal Classifiers • Transformation to Visit Structure • Creating Tolerance-Relation based Datasets • Adding New Features Two groups of databases: three and four visit centered sets were constructed. Data Mining: Clustered Data

  23. Coefficients and Angles Feature Construction for Dp where p is a patient with 4 visits: Clustering Techniques for Temporal Feature Extraction

  24. Quadratic Equation Based New Features Clustering Techniques

  25. Clustering Techniques

  26. Eight new decision attributes based on different discretizations of Differences in Total Score New Feature Construction: Decision Feature 30 30

  27. Classifiers Construction [learning differences in total score] for clustered data: J48, Random Forest, and Multilayer Perceptron (Neural Network) have been tested on the cluster-based original datasets with: • 1) standard deviations and averages, • 2) coefficients and text, • 3) coefficients and angles, • 4) coefficients only, • 5) angles only, • 6) angles and text, • 7) angles, coefficients and text /the winner/. Data Mining: Clustered Data

  28. Data Mining: Clustered Data

  29. Results are quite encouraging • Top precision is .884 • This represents an improvement over the classification precision of .751 with J48 classification on the original dataset and features Sound Level Centroid, Sound Level Spread and Recovery Rate being present Summary Data Mining: Clustered Data

  30. Action Rules

  31. Action rule is defined as a term [(ω) ∧ (α→β)] →(ϕ→ψ) conjunction of fixed condition features shared by both groups Information System proposed changes in values of flexible features desired effect of the action Action Rules

  32. New Decision Feature • Boolean features + or – related to a feature such as Total Score improving or getting worse • Calculated from score on next visit • Stored as + or – on visit related tuple New Feature Construction: Decision Features showing change over time

  33. Rules using LISpMiner ACTION RULES: EXPERIMENT AND RESULTS

  34. Analysis: Before confidence: 9/9+0 After confidence: 9/ [9+20] Low confidence but shows promise ACTION RULES: EXPERIMENT AND RESULTS

  35. Summary

  36. Continue Action Rule Study • Develop GUI for patient data entry • Use knowledge gained from rules to develop decision support system for treatment support for tinnitus sufferers • Continue research with music, emotions, and tinnitus treatment Future Research

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