1 / 10

APPLICATION : DIAGNOSTIC CODING

APPLICATION : DIAGNOSTIC CODING. 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.

bruis
Download Presentation

APPLICATION : DIAGNOSTIC CODING

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. APPLICATION : DIAGNOSTIC CODING • 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, 9th revision (ICD-9) /38 SIEMENS

  2. Hospital Document DB Diagnostic Code DB Code database Patients – Criteria Patient – Notes diagnosis Patient Patient Note 428 A 1 250 B AMI C 1 2 414 D 250 E 429 F 3 SCIP G 2 ... ... ... ... ... ... ... ... ... ... MANUAL CODING (ICD-9) PROCESS heart failure diabetes Insurance Look up ICD-9 codes Statistics reimbursement /38 SIEMENS

  3. Hospital Document DB Diagnostic Code DB Code database Patients – Criteria Patient – Notes diagnosis Patient Patient Note 428 A 1 250 B AMI C 1 2 414 D 250 E 429 F 3 SCIP G 2 ... ... ... ... ... ... ... ... ... ... PATIENT RECORDS heart failure diabetes Insurance Look up ICD-9 codes Statistics reimbursement /38 SIEMENS

  4. Hospital Document DB Diagnostic Code DB Code database Patients – Criteria Patient – Notes diagnosis Patient Patient Note 428 A 1 250 B AMI C 1 2 414 D 250 E 429 F 3 SCIP G 2 ... ... ... ... ... ... ... ... ... ... PATIENT RECORDS heart failure diabetes Insurance Look up ICD-9 codes Statistics reimbursement /38 SIEMENS

  5. Hospital Document DB Diagnostic Code DB Code database Patients – Criteria Patient – Notes diagnosis Patient Patient Note 428 A 1 250 B AMI C 1 2 414 D 250 E 429 F 3 SCIP G 2 ... ... ... ... ... ... ... ... ... ... COMPUTER ASSISTED CODING heart failure Computer coding system diabetes Insurance Statistics reimbursement /38 SIEMENS

  6. HYBRID APPROACH (KNOWLEDGE-BASED) Existing approaches are rule-based systems that solve the coding task using a set of hand crafted expert rules Our Solution: Human Knowledge Machine Intelligence 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 Computerized Coding Papers in IJCNLP 2008, ICMLA 2007, ECML 2008 /38 SIEMENS

  7. Automatic Medical Coding of Patient Records • J. Xu, S. Yu, Jinbo Bi, L. Lita, S. Niculescu, Automatic Medical Coding of Patient Records via Weighted Ridge Regression, Proceedings of the 6thInternational Conference on Machine Learning and Applications, (ICMLA) 2007. • 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, 2008. Joint Optimization of Classifiers for Clinically Interrelated Diseases • Jinbo Bi et al. An Improved Multi-task Learning Approach with Applications in Medical Diagnosis, Proceedings of the 18th European Conference onMachine Learning (ECML), 2008. • Jinbo Bi et al. A Mathematical Programming Formulation for Sparse Collaborative Computer Aided Diagnosis, Proceedings of the 22nd International Conference on ArtificialIntelligence, (AAAI) 2007. • 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) 2006. /38 SIEMENS

  8. Area Under ROC Curve Surgical care infection Pneumonia Diabetes Acute myoinfarc HF measure AMI measure Ischemic HD Heart failure 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) /38 SIEMENS

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

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

More Related