1. Incorporating Artificial Intelligence into Mammography Prediction Louis Oliphant Computer Sciences Department University of Wisconsin-Madison

2. What is Artificial Intelligence? The study and design of intelligent agents Poole, Mackworth & Goebel 1998 http://www.bostondynamics.com/content/sec.php?section=BigDog GeneScan, C. Burge and S. Karlin 1997 http://picasa.google.com/http://www.toshiba.co.jp/about/press/2005_05/pr2001.htmhttp://babelfish.yahoo.com/ the spirit indeed is willing, but the flesh is weak. De geest is wel gewillig, maar het vlees is zwak.

3. Y 0 0 1 0 0 0 Prediction: X 986545346 298643211 213456478 900958424 679046279 021112466 Y 1 X 675494221 Supervised Machine Learning XY 1324118330 2324621720 4567772171 4562178260 3211104331 5431647320 7867534170 5677423111 0989746320 Training Data Classifier ModelTrained Classifier Model Test Data Y 0 0 1 0 0 1 Accuracy 0.87 Nearest NeighborNeural NetworkSupport Vector Machine XY 1324118330 2324621720 4567772171 4562178260 3211104331 5431647320 7867534170 5677423111 0989746320 Fixed Length Feature Vector

4. First Order Logic Data Xy 1324118330 2324621720 4567772171 4562178260 3211104331 5431647320 7867534170 5677423111 0989746320

5. IdPatientDateMass Shape …Mass Size Loc 1P15/03Oval3mmRU4 2P15/04Round8mmRU4 3P25/04Oval4mmLL3 4P36/00Round2mmRL2 ……………… Mammography Dataset Birads 3 5 1 4 … Malignant/ Benign M M B B … Collected from April 1999 to February 2004 18,270 Patients 47,669 Mammograms 510 Malignant 61,709 Benign

6. Inductive Logic Programming Growth Medium: soil, wood Cap Color: white, red Grouping: single, cluster Annulus: present, not present ediblepoisonous edible(X) :- cap_color(X,red), annulus(X,present). edible(X) :- medium(X,wood), grouping(X,single).

7. Our Model Rules+ TAN Prolog+ Java Do: Find rule and add it if improves model performance Until time limit

8. Testing The Model Purpose: assess future performance Data set Train setTest set Score: 0.67 Small test set High variance Cross Validation Train set Test set Score: 0.68 Score: 0.65 Score: 0.79 Score: 0.81 Average Score: 0.72 Standard Deviation: 0.07

9. Birads >= 5 Birads >= 4 Birads… TAN TAN + Rules Our Results

10. Fielding The System  Approval process for pilot study  Web page interface XHTML, Javascript Enter Descriptors, Patient profile, Radiologist’s Score  Backend Java + Prolog Return probability of malignancy and Why model makes the prediction

11. Conclusions  Computers good at combining multiple interacting features  Adding rules improves performance  Rules lend insight into predictive models To Remember Supervised Machine Learning Data set Model Evaluation Metric

12. Sample Rules Learned is_malignant(A) :- covers:(+7,-0) BIRADS(A,5), MassPAO(A,present), Age(A,age6570), previous_finding(A,B), MassesShape(B,none), Calc_Punctate(B,notPresent), previous_finding(A,C), BIRADS(C,3). is_malignant(A) :- covers:(+42,-11) BIRADS(A,5), MassPAO(A,present), MassesDensity(A,high), HO_BreastCA(A,hxDCorLC), in_same_mammogram(A,B), Calc_Pleomorphic(B,notPresent), Calc_Punctate(B,notPresent).

13. Interesting Findings Mass Density BenignMalignant Low/Fat99.95%0.05% Equal96.7%3.3% High68.2%31.7%

14. Adding More Knowledge  ILP allows easy addition of more tables of information  Added following information to our system: Radiologist’s handmade rules Gail model information Feature-engineered rules

15. Adding More Knowledge TAN + Rules More Knowledge