Automatic Document Categorisation by User Profile in MEDLINE Euripides G.M. Petrakis Angelos Hliaoutakis Intelligent Systems Laboratory

Automatic Document

Categorisation by User Profile in MEDLINE

Euripides G.M. PetrakisAngelos Hliaoutakis

Intelligent Systems Laboratory www.intelligence.tuc.grTechnical University of Crete (TUC)Chania, Crete, Greece

http://www.intelligence.tuc.gr/

ISHIMR 2011, Zurich, Switzerland

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Problem Definition

• Medical information systems are designed for experts !– Domain specific answers to experts

• Must also serve naive consumers – Easy to read and comprehend information

• Investigate methods for the categorization of information by user profile– Experts: use complex terms for their searches– Consumers: do simple searches using natural

language terms


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Current Practices

• In MEDLINE of U.S. NLM, documents are indexed by experts – 10-12 MeSH terms per document (pathology,

disease, treatment, drugs etc)– Over 15 million documents - Slow !!– Automate this process – No categorization

• MedScape, Medlineplus, MedHunt rely on the manual categorization of information – Slow, does not scale-up for large collections


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Objectives

• Investigate methods for automatic document indexing in MEDLINE

• These 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


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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

AMTEx

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

Topic drift

HIKM’2006

AMTEx

HIKM’2006

The AMTEx method [DKE 2009]

• Main ideas:

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

AMTEx

Example

HIKM’2006

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”


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Term & Document Categorization


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New Vocabularies

• Vocabulary of General Terms (VGT): 105.675 general (WordNet) terms

• Vocabulary of Consumer Terms (VCT): 7,165 consumer (MeSH) terms.

• Vocabulary of Expert Terms (VET): 16,719 consumer (MeSH) terms

(MeSH) - (WordNet)=VGT

(MeSH) (WordNet)=VCT

(WordNet) - (MeSH)=VET


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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


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Evaluation

• Precision and Recall measures: a good method has high values of both

• Datasets: OHSUMED: 348,566 MEDLINE abstracts that come along with 64 queries and their relevant answers

• Ground truth: the set of MeSH index terms assigned to documents by experts


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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


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Consumers Retrieval Task


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Experts Retrieval Task


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Results

• Consumers retrieval task: – Retrievals with the manually assigned MeSH

terms performs better– MMTx, AMTEx perform equally well

• Experts retrieval task:– Retrievals with AMTEx perform better

• 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


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Conclusions & Future Work

• We investigate methods:– Automatic document indexing – Categorization by user profile

• AMTEx is very well suited for both problems

• Future work: more elaborate documents methods (machine learning, fuzzy)

• More categories – According to UMLS SN (pathology, treatment)– User categories (e.g., specialty)


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Questions and answers

AMTEx

ΑΜΤΕx OutlineClick icon to add SmartArt graphic

HIKM’2006

INPUT:Document Collection

INPUT:Document Collection C/NC value

Multi-word Term Extraction& Term Ranking

C/NC valueMulti-word Term Extraction

& Term Ranking

MeSHTerm Validation

MeSHTerm Validation

Single-word Term ExtractionNon-MeSH multi-word are broken down & validated against MeSH

Single-word Term ExtractionNon-MeSH multi-word are broken down & validated against MeSH

Variant GenerationVariant Generation Term Expansion(MeSH)

Term Expansion(MeSH)

MeSHThesaurusResource

MeSHThesaurusResource

OUTPUT:MeSH

Term Lists

OUTPUT:MeSH

Term Lists


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AMTEx vs MMTx

• AMTEx: faster, improved precision by merely a fifth of term output of MMTx

Data Set MethodNumber of Terms

Precision RecallTime

(hours)

OHSUMEDAMTEX MMTX

840

0.1250.089

0.1010.336

7.38314.516

PMCAMTEX

MMTX

2572

0.0340.033

0.0620.162

1.3872.727

AMTEx

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’2006

Fragment of the MeSH IS-A Hierarchy

AMTExHIKM’2006

neuralgia

Root

Nervous systemdiseases

Neurologicmanifestations

pain

headache

Cranial nervediseases

Facialneuralgia