Automatic Extraction of Synoptic Data. George Cernile Artificial Intelligence in Medicine AIM
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1 Automatic Extraction of Synoptic Data George Cernile Artificial Intelligence in Medicine AIM
2 Agenda Background Technology used Demonstration
3
4 Questions How often are checklist elements included in a report, and does this vary by type of report/cancer? With what degree of confidence can checklist elements be identified and the associated value read? Could a system associate the appropriate set of checklist elements with the cancer that is being reported? How much variability is there in the completeness of checklist elements between and within types of cancer? What type of lexicon and logic system would be required to encode the checklist elements? Will the system be able to determine when data are ambiguous and, if so, how should they be dealt with?
5 Machine readability criteria Score Criteria 1 Checklist element present, value clearly defined 2 Checklist element present, value ambiguous 3 Checklist element not present, value can be inferred 4 Cannot determine value
6 Examples Checklist Element Text Score Explanation Lymph Node Sampling Tumor Size Size of Invasive Component Histologic Type The specimen consists of 3 small lymph nodes ranging in size from 0.5 to 1.2 cm. 2 The specimen is serially sectioned to reveal a grayish white firm tumor with ill defined Margins measuring 2 x 2.2 x 2.8 cm in greatest dimensions. Invasive Tumour Size 2.0 x 1.7 cm Sections showed multiple cores of breast tissue showing fibrosis with scattered areas of small ductal epithelial cells arranged in sheets and cords of cells suspicious for invasive carcinoma Lymph nodes are mentioned but not labeled as a sampling, the words specimen consists is taken to mean that they are part of the sampling. Not explicit the measurement might also refer to the margins Invasive Tumor size clearly identified. No element identifier present, histologic type can be inferred.
7 Machine readability data Checklist Element Name (CE) No. of Reports Containing CE Identifier No. of Reports Containing CE Value Machine Legibility Score Distribution 1 2 3** 4 Histologic Grade Specimen Type Lymph Node Sampling Specimen size Laterality Tumor Site Size of Invasive Component Histologic Type Pathologic Staging Margins pathology reports of breast cancer **Checklist element not present, value can be inferred
8 Internal Coding system SYN xx x xx xx Site Procedure Attribute Value e.g. Site: Breast (01) Procedure: n/a (0) Attribute: Histologic Type (07) Value: Non invasive Carcinoma (01) Code: SYN_
9 Example from Breast Specimen type: Mastectomy, modified radical Orientation SYN SYN Site: Breast (01) Value: Attribute Name(00) Value: Mastectomy(02) Procedure:n/a (0) Attribute: Specimen Type (01)
10 Synoptic extraction tasks Identify the report type Lexicons ICD-O-3, etc return concepts found in text Knowledge base infers report type from concepts Extract relevant data elements Lexicon specific report type returns relevant concepts Numeric extraction algorithm Knowledge base matches values and numbers to elements
11 Site Identification NHL M-9590/3 M-9591/3, M-9670/3 M-9699/3, M-9700/3 M- 9719/3 Hodgkin s Lymphoma M-9650/3 M-9667/3 Ewing Sarcoma M-9260/3 Uveal Melanoma C69.* + M-8772/3 M-8774/3 GI Lymphoma C16.*, C17.*, C18.*, C19.* C20.*, C21.* + M-9590/3 M-9591/3, M-9670/3 M-9699/3, M-9700/3 M-9719/3, M- 9650/3 M-9667/3
12 Inference Engine Imbedded AI tool for knowledge base CLIPS (developed by NASA) Powerful pattern matching and rule based inference engine Can express arbitrarily complex knowledge to provide a system with expert level problem solving
13 Numeric Extraction Algorithm designed to extract numeric values Knowledge base compares ranges and units Examples The tumor measures 1.5 cm. breast tissue measuring approximately 5 x 2 x 1 cm
14 System block diagram
15 Live Demonstration
16 Shows how value was derived
17 Uses for Synoptex API for embedded synoptic extraction capability Add-on to E-path for automatic synoptic report generation Quality control tool for reporting institutions Automated data extraction to populate database for clinical trial matching system Other uses where synoptic summaries are needed (ICU report scanning)
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