Automatic Document Indexing in Large Medical Collections

1. Automatic Document Indexing in Large Medical Collections Angelos Hliaoutakis, Kalliopi Zervanou, Euripides G.M. Petrakis Technical University of Crete, Chania, Greece Evangelos E. Milios Dalhousie University, Halifax, Canada AMTEx

2. Overview • The need for automatic assignment of index terms in large medical collections • MMTx (by the US NLM) • The AMTEx approach to medical document indexing • AMTEx resources: MeSH & C/NC value • Experiments & evaluation • Discussion and future research AMTEx

3. Motivation and Objectives • MeSH is a taxonomy of medical terms • Subset of UMLS Metathesaurus • MEDLINE is indexed by MeSH terms (assigned by experts) • Other medical texts need to be associated with MEDLINE, e.g. consumer medical literature • Need for automatic assignment of MeSH terms to any medical text AMTEx

4. MMTx (MetaMap Transfer) Maps arbitrary text to UMLS Metathesaurus concepts: • Parsing to extract noun phrases (syntactic analysis - linguistic filter) • Variant Generation (uses SPECIALIST Lexicon) • Candidate Retrieval (mapping process to Metathesaurus Concepts) • Candidate Evaluation (criteria: centrality, variation, coverage, cohesiveness) AMTEx

5. MMTx Example • Parsing • Shallow syntactic analysis of the input text • Linguistic filtering: isolates noun phrases • Variant Generation e.g. “obstructive sleep apnea” has variants: obstructive sleep apnea, sleep apnea, sleep, apnea, osa,… • Candidate Retrieval Candidate Metathesaurus concepts for the variant “osa” : osa [osa antigen], osa [osa gene product] osa [osa protein] osa [obstructive sleep apnea] • Candidate Evaluation Obstructive Sleep apnea 1000 Sleep Apnea 901 Apnea 827 … … Sleeping 793 Sleepy 755 AMTEx

6. MMTx limitations • MMTx focus on UMLS rather than MeSH • ButMEDLINE indexing is based on MeSH • Exhaustive variant generation: the initial phrase is iteratively expanded into all possible UMLS variants • term overgeneration • term concept diffusion • unrelated terms added to the final candidate list AMTEx

7. The AMTEx method • New method for automatic indexing of medical documents • Main idea: • Initial term extraction based on a hybrid linguistic/statistical approach, the C/NC value • Extracts general single and multi-word terms • Extracted terms are validated against MeSH AMTEx

8. ΑΜΤΕxOutline INPUT: Document Collection C/NC value Multi-word Term Extraction & Term Ranking OUTPUT: MeSH Term Lists MeSH Term Validation Single-word Term Extraction Non-MeSH multi-word are broken down & validated against MeSH MeSH Thesaurus Resource Variant Generation Term Expansion (MeSH) AMTEx

9. MeSH: Medical Subject Headings The NLM medical & biological terms thesaurus: • Organized in IS-A hierarchies • more than 15 taxonomies & more than 22,000 terms • a term may appear in multiple taxonomies • No PART-OF relationships • Terms organized into synonym sets called entry terms, including stemmed term forms AMTEx

10. Fragment of the MeSH IS-A Hierarchy Root Nervous system diseases Cranial nerve diseases Neurologic manifestations pain Facial neuralgia headache neuralgia AMTEx

11. The C/NC value method • Hybrid (linguistic / statistical) term extraction method • Domain independent • Specifically designed for the identification of multi-word and nested terms: • compound & multi-word terms very common in biomedical domain • multi-word terms often used in indexing AMTEx

12. C-value • C-value: a phrase may be a term, if it often appears alone or within other candidate terms otherwise α: candidate term f(α): frequencyTα: set of candidate terms containing α P(Tα): number of such terms AMTEx

13. NC-value • NC-value: a phrase is more likely a term, if it often appears in specific word context w: context word t(w): number of terms w appears with n: number of all terms fα(w): frequency of w as context word of α AMTEx

14. AMTEx step 1: C/NC valueMulti-word Term Extraction & Ranking • Part-of-Speech Tagging • Linguistic filtering: • N+ N • (A|N)+ N • ( (A|N)+ | ( (A|N)* (N P)? ) (A|N)* ) N • Candidate term ranking based on C/NC-value • Keep terms with NC-value > T1 AMTEx

15. AMTEx step 2: MeSH Term Validation • Candidate terms are validated against the MeSH Thesaurus (simple string matching) • Only candidate terms matching MeSH are kept • Multi-word candidates not matching MeSH may still contain (shorter) MeSH terms AMTEx

16. AMTEx step 3: Single-word Term Extraction For multi-word terms not matching MeSH: • Multi-word are split into single-word terms • Single-word terms matched against MeSH • Matched MeSH terms added to term list AMTEx

17. AMTEx step 4: Term Variant Generation Variants are added to the list of terms: • Inflectional variants of the extracted terms identified during term extraction (C/NC-value) • Stemmed term-forms available in MeSH AMTEx

18. AMTEx step 5: Term Expansion AMTEx

19. AMTEx step 5: Term Expansion • Each term in the list is expanded with neighbouring terms in MeSH hierarchy • The expansion may include terms more than one level higher or lower than the original term, depending on similarity threshold T • Semantic similarity metric by Li et al.Y. Li, Z. A. Bandar, and D. McLean. An Approach forMeasuring Semantic Similarity between Words UsingMultiple Information Sources. IEEE Trans. on Knowledgeand Data Engineering, 15(4):871–882, July/Aug. 2003. AMTEx

20. Example Input:Full text article MEDLINE index terms: “Aged”, “Data Collection”, “Humans”,“Knee”, “Middle Aged”, “Osteoarthritis,Knee/complications”, “Osteoarthritis, Knee/diagnosis”,“Pain/classification”, “Pain/etiology”, “ProspectiveStudies”, “Research Support, Non-U.S. Gov’t” MMTx terms: “osteoarthritis knee”, “retention”, “peat”,“rheumatology”, “acetylcholine”, “lysine acetate”,“potassium acetate”, “questionnaires”, “target population”,“population”, “selection bias”, “creativeness”,“reproduction”, “cohort studies”, “europe”, “couples”,“naloxone”, “sample size”, “arthritis”, “datacollection”,“mail” ‘health status”, “respondents”, “ontario”, “universities”,“dna”, “baseline survey”, “medical records”,“informatics”, “general practitioners”, “gender”, “beliefs”,“logistic regression”, “female”, “marital status”,“employment status”, “comprehension”, “surveys”,“age distribution”, “manual”,“occupations”, “manuals”,“persons”, “females”, “minor”, “minority groups”,“incentives”, “business”, “ability”, “comparativestudy”, “odds ratio”, “biomedical research”, “pubmed”,“copyright”, “coding”, “longitudinal studies”, “immunoelectrophoresis”,“skin diseases”, “government”,“norepinephrine”, “social sciences”, “survey methods”,“tyrosine”, “new zealand”, “azauridine”, “gold”, “nonrespondents”,“cycloheximide”, “rheum”, “jordan”,“cadmium”, “radiopharmaceuticals”, “community”,“disease progression”, “history” AMTExterms: “health surveys”, “pain”, “review publicationtype”, “data collection”, “osteoarthritis knee”, “knee”,“science”, “health services needs and demand”, “population”,“research”, “questionnaires”,“informatics”,“health” AMTEx

21. Evaluation Precision and Recall measures • Dataset: • 61 full MEDLINE documents (not abstracts), from PMC database of NCBI Pubmed • MEDLINE documents are paired to respective MeSH index terms, manually assigned by experts • Ground Truth: • the set of MeSH document index terms • Benchmark method: • MMTx against our AMTEx AMTEx

22. Multi-Word Terms only T: term expansion threshold, lower T means further expansion AMTEx

23. Contribution of Single-Word Terms AMTEx

24. Conclusions: AMTEx • Designed for indexing and retrieval of MEDLINE documents • Focuses on multi-word term extraction using valid linguistic & statistical criteria • Based on MeSH -- similarly to human indexing • Selectively expands into term variants, synonyms • Outperforms the current benchmark MMTx method, in both precision & recall AMTEx

25. Future Work • Better ranking of terms, using semantic similarity • Learning of thresholds T1, T • Word sense disambiguation to detect the correct sense for expansion rather than the most common sense • Handling shorter documents AMTEx