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Treatment Outcome Prediction Model of Visual Field Recovery Using SOM

Treatment Outcome Prediction Model of Visual Field Recovery Using SOM. JOJO 2011.12.22. Outline. Basic knowledge Treatment Outcome Prediction Model Feature selection Self-organizing-maps Conclusion. Outline. Basic knowledge Treatment Outcome Prediction Model Feature selection

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Treatment Outcome Prediction Model of Visual Field Recovery Using SOM

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  1. Treatment Outcome Prediction Model of Visual Field Recovery Using SOM JOJO 2011.12.22

  2. Outline • Basic knowledge • Treatment Outcome Prediction Model • Feature selection • Self-organizing-maps • Conclusion

  3. Outline • Basic knowledge • Treatment Outcome Prediction Model • Feature selection • Self-organizing-maps • Conclusion

  4. Basic knowledge 1 Diagnosis of damage to the visual system Reaction time High Resolution Perimetry (HRP) Detection

  5. Basic knowledge 1 Diagnosis of damage to the visual system Diagnostic spots definition:

  6. Basic knowledge 2 Vision Restoration Training(VRT) After damages to visual system, spontaneous recovery happens. When the recovery finished, VRT is used to treat patients. How can we know the results of VRT before it’s applied?

  7. Basic knowledge 3 Treatment Outcome Prediction Step1: build a TOPM with patients’ baseline diagnosis and diagnostic charts Step2: extract features from a patient’s baseline diagnosis chart Step3: predict the treatment outcome with TOPM

  8. Outline • Basic knowledge • Treatment Outcome Prediction Model • Feature selection • Self-organizing-maps • Conclusion

  9. TOMP (FS) Global features

  10. TOMP (FS) Conformitytohemianopiaandquadrantanopia

  11. TOMP (FS) Local features

  12. TOMP (SOM) 1 Theory: Winner takes all

  13. TOMP (SOM) Local feature

  14. TOMP (SOM) 2 Prediction: the winner takes all decided

  15. TOMP (SOM) 3 Results:

  16. TOMP (SOM) 3 Results: (Model evaluation: 10-fold cross validation) P: the number of hot spots N: the number of cold spots TP: correctly classified positive samples FP: incorrectly classified positive samples

  17. TOMP (SOM) 3 Results: (Model evaluation: 10-fold cross validation) ROC:

  18. TOMP (SOM) 3 Model evaluation: 10-fold cross validation 44%±4.7% 6%±1.9% 84.2%±1.4% 45.3%±4.5% 3.2%±0.8% 86.8%±1.1% 68.5%±4.0% 4.7%±1.0% 90.0%±0.8%

  19. Outline • Basic knowledge • Treatment Outcome Prediction Model • Feature selection • Self-organizing-maps • Conclusion

  20. Conclusion Why choose SOM? • Its non-linearity and self-organization methodology allows a comprehensible adaptation to the data distribution. • Simplify the process of data mining and the feature selection phase by conveniently combining both prediction and data exploration.

  21. Thank you!

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