HIKM’2006AMTEx 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

HIKM’2006AMTEx 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

HIKM’2006AMTEx 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

HIKM’2006AMTEx 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)

HIKM’2006AMTEx 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 apnea1000 Sleep Apnea 901 Apnea827… Sleeping793 Sleepy755

HIKM’2006AMTEx MMTx limitations MMTx focus on UMLS rather than MeSH  But MEDLINE 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

HIKM’2006AMTEx 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

HIKM’2006AMTEx ΑΜΤΕx Outline INPUT: Document Collection INPUT: Document Collection C/NC value Multi-word Term Extraction & Term Ranking C/NC value Multi-word Term Extraction & Term Ranking MeSH Term Validation MeSH Term Validation Single-word Term Extraction Non-MeSH multi-word are broken down & validated against MeSH Single-word Term Extraction Non-MeSH multi-word are broken down & validated against MeSH Variant Generation Term Expansion (MeSH) Term Expansion (MeSH) MeSH Thesaurus Resource MeSH Thesaurus Resource OUTPUT: MeSH Term Lists OUTPUT: MeSH Term Lists

HIKM’2006AMTEx 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

HIKM’2006AMTEx Fragment of the MeSH IS-A Hierarchy Root Nervous system diseases Neurologic manifestations pain headacheneuralgia Cranial nerve diseases Facial neuralgia

HIKM’2006AMTEx 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

HIKM’2006AMTEx 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): frequency T α : set of candidate terms containing α P(T α ): number of such terms

HIKM’2006AMTEx 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 f α (w): frequency of w as context word of α

HIKM’2006AMTEx AMTEx step 1: C/NC value Multi-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 > T 1

HIKM’2006AMTEx 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

HIKM’2006AMTEx 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

HIKM’2006AMTEx 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

HIKM’2006AMTEx AMTEx step 5: Term Expansion

HIKM’2006AMTEx 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 for Measuring Semantic Similarity between Words Using Multiple Information Sources. IEEE Trans. on Knowledge and Data Engineering, 15(4):871–882, July/Aug

HIKM’2006AMTEx 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”, “Prospective Studies”, “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”, “data collection”, “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”, “comparative study”, “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” AMTEx terms: “health surveys”, “pain”, “review publication type”, “data collection”, “osteoarthritis knee”, “knee”, “science”, “health services needs and demand”, “population”, “research”, “questionnaires”, “informatics”, “health”

HIKM’2006AMTEx 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

HIKM’2006AMTEx Multi-Word Terms only MethodPrecisionRecall MMTx0,0130,015 AMTEx (T = 0,5) 0,1860,108 AMTEx (T = 0,6) 0,2180,090 AMTEx (T = 0,7) 0,2360,072 AMTEx (T = 0,8) 0,2360,072 AMTEx (T = 0,9) 0,2360,070 T: term expansion threshold, lower T means further expansion

HIKM’2006AMTEx Contribution of Single-Word Terms MethodPrecisionRecall MMTx0,0130,015 AMTEx0,2360,070 AMTEx & single-word MeSH terms0,1200,228

HIKM’2006AMTEx 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

HIKM’2006AMTEx Future Work Better ranking of terms, using semantic similarity Learning of thresholds T 1, T Word sense disambiguation to detect the correct sense for expansion rather than the most common sense Handling shorter documents