APPLICATION : DIAGNOSTIC CODING 1 SIEMENS  Coding is the translation of diagnosis terms describing patients diagnosis or treatment into a coded number  Used for medical bills and insurance reimbursement  Used for Disease statistics  International classification of diseases, 9 th revision (ICD-9) /38

MANUAL CODING (ICD-9) PROCESS Patients– Criteria Patient diagnosis 250 AMI SCIP... heart failure diabetes Code database Look up ICD-9 codes Patient– Notes Patient 1 A Note B C D E 2 F G... Hospital Document DB Diagnostic Code DB Statistics reimbursement Insurance 2 SIEMENS /38

PATIENT RECORDS Patients– Criteria Patient diagnosis 250 AMI SCIP... heart failure diabetes Code database Look up ICD-9 codes Patient– Notes Patient 1 A Note B C D E 2 F G... Hospital Document DB Diagnostic Code DB Statistics reimbursement Insurance 3 SIEMENS /38

Patients– Criteria Patient diagnosis 250 AMI SCIP... heart failure diabetes Code database Look up ICD-9 codes Patient– Notes Patient 1 A Note B C D E 2 F G... Hospital Document DB Diagnostic Code DB Statistics reimbursement Insurance 4 SIEMENS PATIENT RECORDS /38

Patients– Criteria Patient diagnosis 250 AMI SCIP... heart failure diabetes Code database Patient– Notes Patient 1 A Note B C D E 2 F G... Hospital Document DB Diagnostic Code DB Statistics reimbursement Insurance Computer coding system 5 SIEMENS COMPUTER ASSISTED CODING /38

Existing approaches are rule-based systems that solve the coding task using a set of hand crafted expert rules Our Solution: HYBRID APPROACH (KNOWLEDGE-BASED) Papers in IJCNLP 2008, ICMLA 2007, ECML 2008 Human Knowledge Machine Intelligence Computerized Coding Medical textbook, medical ontology, clinical practice Natural language processing, statistical text mining In-house DB with 300,000 records from 15,000 patients Diagnostic code DB 6 SIEMENS /38

 J. Xu, S. Yu, Jinbo Bi, L. Lita, S. Niculescu, Automatic Medical Coding of Patient Records via Weighted Ridge Regression, Proceedings of the 6 th International Conference on Machine Learning and Applications, (ICMLA)  L. Lita, S. Yu, S. Niculescu, Jinbo Bi, Large Scale Diagnostic Code Classification for Medical Patient Records, Proceedings of the 3rd International Joint Conference on Natural Language Processing, (IJCNLP) 2008  Jinbo Bi et al, Incorporating Medical Knowledge into Automatic Medical Coding of Patient Records, Patent Invention Disclosure of Siemens Medical Solutions, Technical Report,  Jinbo Bi et al. An Improved Multi-task Learning Approach with Applications in Medical Diagnosis, Proceedings of the 18th European Conference on Machine Learning (ECML),  Jinbo Bi et al. A Mathematical Programming Formulation for Sparse Collaborative Computer Aided Diagnosis, Proceedings of the 22nd International Conference on Artificial Intelligence, (AAAI)  T. Xiong, Jinbo Bi, B. Rao, V. Cherkassky, Probabilistic Joint Feature Selection for Multi-task Learning, Proceedings of SIAM International Conference on Data Mining, (SDM) SIEMENS 7 Automatic Medical Coding of Patient Records Joint Optimization of Classifiers for Clinically Interrelated Diseases /38

STANDALONE ACCURACY OF CAC No prior: pure data-driven SVM classifier (IJNLP 2008); Hybrid: combine medical knowledge with SVM classifier; Hybrid MTL: combine medical knowledge with collaborative prediction method (ECML 2008) Area Under ROC Curve Heart failure Ischemic HD Acute myo infarc Diabetes Pneumonia Surgical care infection AMI measure HF measure 8 SIEMENS /38

 Preliminary results show combining known medical knowledge with statistical learning techniques strengthened the data mining applications in coding process  A lot more … … CONCLUSIONS 9 SIEMENS /38

Clinical Decision Support POTENTIAL RESEARCH 10 SIEMENS Images Patient factors Proteomics Genomics Treatment plans Known Medical Knowledge Personalized Knowledge Models /38