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Extracting BI-RADS Features from Portuguese Clinical Texts

Extracting BI-RADS Features from Portuguese Clinical Texts. H. Nassif , F. Cunha, I.C. Moreira, R. Cruz-Correia, E. Sousa, D. Page, E. Burnside, and I. Dutra. University of Wisconsin – Madison, and University of Porto, Portugal. The American Cancer Society, Cancer Facts & Figures 2009.

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Extracting BI-RADS Features from Portuguese Clinical Texts

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  1. Extracting BI-RADS Features from Portuguese Clinical Texts H. Nassif, F. Cunha, I.C. Moreira, R. Cruz-Correia, E. Sousa, D. Page, E. Burnside, and I. Dutra University of Wisconsin – Madison, and University of Porto, Portugal

  2. The American Cancer Society, Cancer Facts & Figures 2009.

  3. Mammogram Radiologist Structured Database Impression (free text) Predictive Model Benign Malignant

  4. BI-RADS Lexicon Concepts

  5. Example • In the right breast, an approximately 1.0 cm mass is identified in the right upper slightly inner breast. This mass is noncalcified and partially obscured and lobulated in appearance. Concepts

  6. Nassif 09

  7. Syntax Analyzer • Tokenize sentences • Discard punctuation • Keep stop words • Stem words

  8. Nassif 09

  9. Information from Lexicon • Translate lexicon into Portuguese • Lexicon specifies synonyms: Eg: Equal density, Isodense • Lexicon allows for ambiguous wording:

  10. Nassif 09

  11. Experts • Provide domain specific information • Synonyms: Oval, Ovoid • Acronyms, abbreviations • Domain idiosyncrasies • Interact with and modify semantic rules

  12. Nassif 09

  13. Concept Finder • Regular expression rules • Extract concepts from text • Rule formation: • Initial rules based on lexicon • Rules refined by experts

  14. Rule Generation Example 1 • Aim: Regional Distribution Concept • Lexicon specifies the word “regional” • Initial rule: presence of the word “regional” • Run on training set, experts see results • Many false positives: • “regional medical center”, “regional hospital” • Rule refined by experts: • “regional .* !(medical|hospital)”

  15. Rule Generation Example 2 • Aim: Skin Thickening Concept • Lexicon specifies “skin thickening” • Try “skin” and “thickening” in same sentence • “skin retraction and thickening” • “thickening of the overlying skin” • “A BB placed on the skin overlying a palpable focal area of thickening in the upper outer right breast” • Experts suggest “skin” and “thickening” in close proximity

  16. Scope • Scope: distance between two words • Start with a large scope: • assess number of true and false positives • Move to smaller scopes: • assess number of false negatives • Check precision and recall estimates • Experts decide on the best distance

  17. Nassif 09

  18. Negation Detector • Negation triggers (Mutalik 01, Gindl 08): • “não” (not) when not preceded by “onde” (where) • “sem” (without) • “nem” (nor). • Precedes or appears within the subsentence • Establish negation scope • “without evidence of suspicious cluster of microcalcifications”

  19. Dataset • Training set: 1,129 reports, unlabeled • Testing set: 153 pairs, labeled by radiologist • Basic screening report • Detailed diagnostic report • Perform three refinement passes • Double blind, based on lexicon • Refine rules • Refine manual labeling and rules

  20. Results

  21. Conclusion • Out of 48 disputed cases, parser correctly classified 25 (52.1%) • First Portuguese BI-RADS extractor • Discovers features missed or misclassified • Similar performance to manual annotation • Method portable to other languages

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