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Amit Satsangi amit@cs.ualberta

Amit Satsangi amit@cs.ualberta.ca. Towards Applying Text Mining and Natural Language Processing for Biomedical Ontology Acquisition Inniss T., Light M., Thomas G., Lee J., Grassi M., Williams A. TMBIO(2006). Focus. Ontology for describing age-related macular degeneration (AMD)

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Amit Satsangi amit@cs.ualberta

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  1. Amit Satsangi amit@cs.ualberta.ca Towards Applying Text Mining and Natural Language Processing for Biomedical Ontology AcquisitionInniss T., Light M., Thomas G., Lee J., Grassi M., Williams A. TMBIO(2006) CMPUT 605

  2. CMPUT 605 Focus • Ontology for describing age-related macular degeneration (AMD) • Comparison of the accuracy of three methods for Ontology – Natural Language Processing (NLP) – Text Mining (SAS Text Miner) – Human Expert • Manual and adhoc knowledge acquisition • IDOCS (Intelligent Distributed Ontology Consensus System)

  3. CMPUT 605 Introduction • No existing common and standardized vocabulary for classification of disease types for certain eye-diseases • Clinicians, dispersed geographically, may use different terms to describe the same condition • Research aimed at extracting the feature and attribute descriptions for the vocabulary of AMD, and build an Ontology from that.

  4. CMPUT 605 Related Work • Lot of research done, since 1990’s, for applying NLP techniques in medicine, bio-medicine etc. • NLP & Text Data Mining have been recognized to play an important role in this endeavor • Research focused on online repositories such as Medline & PubMed • NLP systems developed: MedLee, UMLS, GENIES etc.

  5. CMPUT 605 IDOCS

  6. CMPUT 605 Methodology • Four clinical experts in retinal diseases enlisted to view 100 eye sample images of AMD • Experts in different geographic locations • Described the observations using digital voice recorders – no artificially imposed vocabulary constraints • Another retinal expert for manual parsing of the transcribed text – extracting key words, organization of key-words into categories etc.

  7. CMPUT 605 Results: Human Experts

  8. CMPUT 605 Methodology: NLP • NLP: Used for information extraction and automatic summarization. • Identify short sequences of words having meaning over and above a meaning composed directly from their parts – “extreme programming” • Ngram Statistics Package (NSP) used for collocation discovery in case of bi-grams • Word-pair associations measured by PMI

  9. CMPUT 605 Methodology: NLP • Large PMI for larger degree of association between the words

  10. CMPUT 605 Results: NLP

  11. CMPUT 605 Methodology:Text Mining (SAS Text Miner) • Collection of documents (corpus) used as input to any text mining algorithm • Corpus broken into tokens or terms (tokens in a particular language) • Term weighting Measures: Entropy, Inverse Document Frequency (IDF), Global Frequency (GF) -IDF, None (Global weight of 1) & Normal term wt.

  12. CMPUT 605 Results: Text Miner • Frequency wt. None • Term wt. Normal

  13. CMPUT 605 Common Terms • sss

  14. CMPUT 605 Comparison • Thus text mining is a viable and effective method for determining vocabulary to describe a particular disease • Text Mining found a lot of terms that NLP found • Human Expert is the best Ground Truth

  15. CMPUT 605 Ontology Generation

  16. CMPUT 605 Conclusion and Future Work • Human experts are the best, but they did miss some key descriptors • Text Mining and NLP can enhance the generation of feature generations, by preventing the above case • As a consequence more robust vocabulary can be generated • Extension – evaluate the effectiveness of the automated tools, text mining & NLP • Different weighting schemes to be tried in the future

  17. Thank You For Your Attention! CMPUT 605

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