An Automatic Retrieval System for Expert and Consumer Users Rena Peraki, Euripides G.M. Petrakis Angelos Hliaoutakis Intelligent Systems Laboratory Technical University of Crete (TUC) Chania, Crete, Greece

Problem Definition Medical information systems are designed for experts ! –Use complex terms in their searches –Domain specific answers Must also serve naive consumers –Do simple searches using natural language terms –Easy to read and comprehend information Investigate methods for the categorization of information by user profile BIBE 2012, Larnaca, Cyprus2

3 Current Practices MedScape, Medlineplus, MedHunt rely on the manual translation and categorization of information for consumers –Slow, does not scale-up for large collections In MEDLINE of U.S. NLM, documents are indexed by experts and for experts only –No categorization by user user profile –10-12 MeSH terms per document (pathology, disease, treatment, drugs etc) –Over 15 million documents - Slow !! –Need to automate this process

BIBE 2012, Larnaca, Cyprus4 Objectives Investigate methods for automatic document indexing in MEDLINE These index terms are subsequently used for filtering documents by user profile Main Idea: categorization of terms to simple terms comprehendible by consumers or more involved terms suitable for experts

Resources Automatic indexing in MEDLINE: –MMTx [U.S. NLM]: MMTx focus on UMLS rather than MeSH –AMTEx [DKE, 2009]: MeSH terms, faster and more accurate than MMTx Dictionaries for biomedical and health related concepts –UMLS Metathesaurus, MeSH Dictionaries for general English words –WordNet, Specialist BIBE 2012, Larnaca, Cyprus5

MMTx (MetaMap Transfer) Developed by U.S. NLM Maps text to UMLS Metathesaurus concepts –but MEDLINE indexing is based on MeSH –MeSH is a subset of Metathesaurus Suffers from term overgeneration Unrelated terms added to the final candidate list The list must be cleaned-up to keep only MeSH terms Topic drift BIBE 2012, Larnaca, Cyprus6

The AMTEx method [DKE 2009] Main idea: Initial term extraction based on a hybrid linguistic/statistical approach, the C/NC value Extracts general single and multi-word terms (noun phrases) Mainly multi-word terms: “heart disease”, “coronary artery disease” Extracted terms are validated against MeSH Faster, improved precision by merely a fifth of term output of MMTx BIBE 2012, Larnaca, Cyprus7

Example BIBE 2012, Larnaca, Cyprus 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” 8

Term & Document Categorization BIBE 2012, Larnaca, Cyprus9

New Vocabularies Vocabulary of General Terms (VGT): general (WordNet) terms Vocabulary of Consumer Terms (VCT): 7,165 consumer (MeSH) terms. Vocabulary of Expert Terms (VET): 16,719 consumer (MeSH) terms BIBE 2012, Larnaca, Cyprus10

Document Categorization Documents are represented by vectors of terms extracted by AMTEx, MMTx or assigned by human experts The more VET (VCT) terms a document contains the higher its probability to be suitable for experts (consumers) –E.g., a document with VET% = 0.62 has 62% probability to be one suitable for experts BIBE 2012, Larnaca, Cyprus11

Evaluation Precision and Recall measures: a good method has high values of both Datasets: OHSUMED: 348,566 MEDLINE abstracts that come with 64 queries and their relevant answers Ground truth: the set of MeSH index terms assigned to documents by experts BIBE 2012, Larnaca, Cyprus12

AMTEx vs MMTx BIBE 2012, Larnaca, Cyprus13 AMTEx: faster, improved precision by merely a fifth of term output of MMTx Data SetMethod Number of Terms PrecisionRecall Time (hours) OHSUMED AMTE X MMT X PMC AMTE X MMT X

Categorization by User Profile How good is the method in retrieving answers for consumers and experts ? We run retrievals for consumers & experts –15 out of the 64 queries contain no expert terms and are suitable for consumers –The remaining queries are suitable for experts –Documents are represented by document vectors of MeSH, MMTx, or AMTEx terms –The retrieval method is Vector Space Model –The document similarity score of VSM is multiplied by its respective VET or VCT score BIBE 2012, Larnaca, Cyprus14

Consumers Retrieval Task BIBE 2012, Larnaca, Cyprus15

Experts Retrieval Task BIBE 2012, Larnaca, Cyprus16

Results Analysis The results indicate –A tendency of human experts to assign simple terms to documents and –Selective ability of AMTEx in extracting complex terms suitable for experts BIBE 2012, Larnaca, Cyprus17

Conclusions & Future Work We investigate methods: –Automatic document indexing –Categorization by user profile AMTEx is well suited for both problems Future work: more elaborate document categorization methods (machine learning, fuzzy) More term and document categories –According to UMLS SN (pathology, treatment) –User categories (e.g., specialty) BIBE 2012, Larnaca, Cyprus18

BIBE 2012, Larnaca, Cyprus19 Questions and answers

ΑΜΤΕx Outline BIBE 2012, Larnaca, Cyprus 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 20

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 BIBE 2012, Larnaca, Cyprus21

Fragment of the MeSH IS-A Hierarchy BIBE 2012, Larnaca, Cyprus neuralgia Root Nervous system diseases Neurologic manifestations pain headache Cranial nerve diseases Facial neuralgia 22