medical factnet
DESCRIPTION
Medical FactNet. Barry Smith University at Buffalo and IFOMIS, Leipzig Christiane Fellbaum Princeton University and Berlin Academy. Online-Inquiry to MEDLINEplus. Online-Inquiry to MEDLINEplus. - PowerPoint PPT PresentationTRANSCRIPT
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Medical FactNetBarry Smith
University at Buffalo and IFOMIS, Leipzig
Christiane FellbaumPrinceton University and Berlin Academy
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Online-Inquiry to MEDLINEplusQuery text response (with links to documents sorted by the
following keywords)
tremor Tremor, Multiple Sclerosis, Parkinson’s Disease, Degenerative Nerve Diseases, Movement Disorders
intentional tremor Tremor, Multiple Sclerosis, Parkinson’s Disease, Spinal Muscular Atrophy, Degenerative Nerve Diseases
tremble Anxiety, Parkinson’s Disease, Panic Disorder, Caffeine, Tremor
trembling Anxiety, Parkinson’s Disease, Panic Disorder, Phobias, Tremor
right hand trembles Phobias, Anxiety, Infant and Toddler Development, Parkinson’s Disease, Diabetes
right hand trembles when grasping
Infant and Toddler Development, Sports Fitness, Sports Injuries, Diabetes, Rehabilitation
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Online-Inquiry to MEDLINEplusQuery text response (with links to documents sorted by the
following keywords)
tremor Tremor, Multiple Sclerosis, Parkinson’s Disease, Degenerative Nerve Diseases, Movement Disorders
intentional tremor Tremor, Multiple Sclerosis, Parkinson’s Disease, Spinal Muscular Atrophy, Degenerative Nerve Diseases
tremble Anxiety, Parkinson’s Disease, Panic Disorder, Caffeine, Tremor
trembling Anxiety, Parkinson’s Disease, Panic Disorder, Phobias, Tremor
right hand trembles Phobias, Anxiety, Infant and Toddler Development, Parkinson’s Disease, Diabetes
right hand trembles when grasping
Infant and Toddler Development, Sports Fitness, Sports Injuries, Diabetes, Rehabilitation
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A consumer health medical information system must be able to map between expert and non-expert medical vocabulary
GOAL: A unified medical language system for non-expert medical vocabulary
UMLS for dummies
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A New Methodology for the Construction and Validation of Information Resources for Consumer Health
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MWN: SPECIFIC AIMS
to extend and validate WordNet 2.0’s medical coverage in light of recent advances in medical terminology research
focusing initially on the English-language single word expressions used and understood by non-experts
provision of a mapping to UMLS, MeSH, and other expert terminologies
use as interlingua for MWNs in other languages
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WordNet (Miller, Fellbaum)
Large lexical database; ubiquitous tool of NLP
coverage comparable to collegiate dictionary, over 130,000 word forms
40 wordnets in different languages
WordNet: rich medical coverage, but pooly validated and poor formal architecture
How create a validated Medical WordNet (MWN)?
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Building blocks of WordNet = ‘synsets’
= ‘concepts’ in medical terminologyterms in same synset = they are interchangeable
in some sentential contexts without altering truth-value: {car, automobile}, {shut, close}
synsets linked via small number of binary relations: is-a part-of verb entailments: (walk-limp, forget-know).
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Strengths of WordNet 2.0
Open source
Very broad coverage
Is-a / part-of architecture
Tool for automatic sense disambiguation
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13 senses for feel is a verb
experience – She felt resentfulfind – I feel that he doesn't like mefeel – She felt small and insignificant; feel – We felt the effects of inflationfeel – The sheets feel softgrope –He felt for his walletfinger – Feel this soft cloth! explore – He felt his way around the dark room)feel – It feels nice to be home againfeel – He felt the girl in the movie theater)
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Medical senses of ‘feel’palpate – examine a body part by palpation:
The nurse palpated the patient's stomach; The runner felt her pulse.
sense – perceive by a physical sensation, e.g. coming from the skin or muscles: He felt his flesh crawl; She felt the heat when she got out of the car; He feels pain when he puts pressure on his knee.
feel – seem with respect to a given sensation: My cold is gone – I feel fine today; She felt tired after the long hike.
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MWN
many word units are monosemic (clinician, stethoscope)most common words are polysemiclexicon of the order of 4000 word units with some 3,000 distinct word senses.
tested by incorporation in NLP applications used for purposes of information retrieval, machine translation, question-answer systems, text summarization
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How to validate Medical WordNet?How to fix the scope of ‘non-expert’?
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Answer: Medical FactNet (MFN)
a large corpus of natural-language sentences providing medically validated contexts for MWN terms.
pilot corpus: 40,000 sentences
full MFN (for common diseases): ~250,000 sentences
accredited as intelligible by non-experts
and as true by experts
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Medical BeliefNet (MBN)
= totality of sentences about medical phenomena to which non-experts assent
comes for free, given our methodology for creating MFN
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Sources for MFN
1. WordNet glosses and arcs
2. Online health information services targeted to consumers
NetDoctor, MEDLINEplus
(factsheets on common diseases)
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Constructing MBN and MFN
sources (WordNet, MEDLINEplus …)
filtering for intelligibility by non-experts
pool of natural language sentences
filtering for non-expert assent filtering for validation by experts
Medical BeliefNet Medical FactNet?
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MFN: SPECIFIC AIMS
To create a pilot open-source corpus of sentences about medical phenomena in the English language
restricted to natural language
grammatically complete
logically and syntactically simple sentences
rated as understandable by non-expert human subjects in controlled questionnaire-based experiments
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MFN: SPECIFIC AIMS
= sentences must be self-contained
make no reference to any prior context
not contain any proper names, indexical expressions or other linguistic devicesthat need to be interpreted with respect to other sentences.
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Constructing MFN
Sentences in MFN must receive high marks for correctness on being assessed by medical experts.
MFN designed to constitute a representative fraction of the true beliefs about medical phenomena which are intelligible to non-expert English-speakers.
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Constructing MBN
Sentences in MBN must receive high marks for assent on being assessed by non-experts.
MBN designed to constitute a representative fraction of the beliefs about medical phenomena (both true and false beliefs) distributed through the population of English speakers.
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Compiling MFN and MBN in tandem
will allow systematic assessment of the disparity between lay beliefs and vocabulary as concerns medical phenomena and the exactly corresponding expert medical knowledge.
will allow us to establish automatically for any given sub-population which areas its beliefs about medical phenomena differ most significantly from validated medical knowledge
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USES OF MFN
for quality assurance of MWN
to support the population of MWN by yielding new families of words and word senses
medical education
consumer health information
(in conjunction with MBN) allow new sorts of experiments in the linguistics, psychology and anthropology of consumer health
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Evaluation of MFN
measure the benefits it brings when incorporated into an existing on-line consumer health portal based on term-search technology.
test whether exploiting the resources of MFN can lead to improved results in the retrieval of expert information
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Differences between expert and non-expert medical language
mismatch between expert and non-expert language
taxonomies reflecting popular lexicalizations have small coverage relative to technical vocabularies
and shallow hierarchies:
no popular terms linking infectious disease and mumps
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Differences between expert and non-expert medical language
popular medical terms (flu) often fuzzier than technical terms
extension of non-expert term used also by experts sometimes smaller, sometimes larger
hypothesis: with few exceptions the focal meanings coincide in their extensions
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Mismatches in Doctor-Patient CommunicationPractical skills of physician in acquiring and
conveying relevant and reliable information by using non-expert language tailored to individual patient
The physician, too, is a human being, thus ex officio a member of the wider community of non-experts
continues to use non-expert language for everyday purposes
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But there are problems
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Question: My seven-year-old son developed a rash today … a friend of mine had her 10-day-old baby at my home last evening before we were aware of the illness. … I have read that chickenpox is contagious up to two days prior to the actual rash. Is there cause for concern at this point?
Answer: Chickenpox is the common name for varicella infection. ... You are correct in that a person with chickenpox can be contagious for 48 hours before the first vesicle is seen. ... Of concern, though, is the fact that newborns are at higher risk of complications of varicella, including pneumonia. ...There is a very effective means to prevent infection after exposure. A form of antibody to varicella called varicella-zoster immune globulin (VZIG) can be given up to 48 hours after exposure and still prevent disease. ...
(from Slaughter)
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Lexical mismatches
rooted in legal concerns?both primary care physician and online information
system must respond primarily with generic, or case- or context-independent, information
most requests relate to specific and episodic phenomena (occurrences of pain, fever, reactions to drugs, etc.).
Hence focus of MFN on generic sentences = context-independent statements about causality, about types of persons or diseases or about typical or possible courses of a disease.
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MFN
designed to map the generic medical information which non-experts are able to understand
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Corpus- and fact-based approaches to information retrieval
meanings of highly polysemous terms cannot be discriminated without consideration of their contexts.
People do this without apparent difficulties
New NLP methodologies to harness computers to manipulate large text corpora
Train automatic systems on large numbers of semantically annotated sentences, exploit standard pattern-recognition and statistical techniques for purposes of disambiguation.
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Use of WordNet in medical informatics
e.g. as tool for simplifying information extraction from the corpus of MEDLINE abstracts:
by replacing verbs with corresponding synsets and so reducing the number of relations that need to be taken account of in the analysis of texts
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Example: FrameNet
500 Frames, each with a plurality of Frame Elements
Medical Frames:
Addiction, Birth, Biological Urge, Body Mark, Cure, Death, Health Response, Medical Conditions, Medical Instruments, Medical Professional, Medical Specialties and Observable Body Parts.
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Frame: Cure
Frame Elements: alleviate. v, alleviation. n, curable. a, curative. a, curative. n, cure. n, cure. v, ease. v, heal. v, healer. n, incurable. a, palliate. v, palliation. n, palliative. a, palliative. n, rehabilitate. v, rehabilitation. n, rehabilitative. a, remedy. n, resuscitate. v, therapeutic. a, therapist. n, therapy. n, treat. v, treatment. n.
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Example: Penn Proposition Bank
designed as a corpus of coherent texts. The intention is to train an automatic system to ‘learn’ the contexts for words and their context-specific meanings.
corpus characterized by a specific logical (function-argument-based) architecture.
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Both FrameNet and Proposition Bankhave poor medical coverage
Both focus on word usage in general, rather than on domain-specific contexts.
Neither concerned with the questions of factuality or validation of statements
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Example: CYC knowledge base
collection of hundreds of thousands of statements mostly about the external world:
The earth is roundMountains are one kind of landformAlbany is the capital of New York
parcelled into micro-theories
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In contrast to CYC,
(i) MFN focuses on one single (albeit very large) domain
(ii) MFN stores English sentences (CYC is language non-specific);
(iii) MFN discriminates folk beliefs and expert knowledge (designed to be consistent with the body of established science;
(iv) MFN will be publicly available.
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Existing Princeton WordNet 2.0labels 504 word-forms ‘medicine’:infection#1 {(the pathological state resulting
from the invasion of the body by pathogenic microorganisms)}
infection#3 {(the invasion of the body by pathogenic microorganisms and their multiplication which can lead to tissue damage and disease)}
infection#4 {infection, contagion, transmission – (an incident in which an infectious disease is transmitted)}
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Maturation
maturation#2 {growth, growing, maturation, development, ontogeny, ontogenesis – ((biology) the process of an individual organism growing organically; a purely biological unfolding of events involved in an organism changing gradually from a simple to a more complex level; he proposed an indicator of osseous development in children)}
maturation#3 {festering, suppuration, maturation – (the formation of morbific matter in an abscess or a vesicle and the discharge of pus)}
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But it mixes up expert and non-expert vocabulary, both current and medieval:
suppuration#2 {pus, purulence, suppuration, ichor, sanies, festering – (a fluid product of inflammation)}
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And it contains medically relevant errors:snore-sleep linked via verb entailment: “if someone
snores, then he necessarily also sleeps.”
In medicine: quite possible to snore while awake, since snoring implies the respiratory induced vibration of glottal tissues as associated not only (and most usually) with sleep but also with relaxation or obesity.
Methodology for constructing MFN will provide us with a systematic means to detect such errors.
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snore sleep
Constructing MBN will give us the resources to do justice to the reason why such cases were included in the first place:
People can only snore when they are asleep and similar sentences belong precisely to the folk beliefs about medicine which MBN will document
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Extracting sentences from online consumer health information sources
In one experiment sentences were derived by researchers in medical informatics from factsheets on Airborne allergens in NIAID’s Health Information Publications and on Hay fever and perennial allergic rhinitis in the UK NetDoctor’s Diseases Encyclopedia.
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Source (NIAID) OutputThere is no good
way to tell the difference between allergy symptoms of runny nose, coughing, and sneezing and cold symptoms. Allergy symptoms, however, may last longer than cold symptoms.
from NIAID HealthInfo
Allergies have symptoms.
Colds have symptoms.
A runny nose is a symptom of an allergy.
Coughing is a symptom of an allergy.
Sneezing is a symptom of an allergy.
Cold symptoms are similar to allergy symptoms.
A cold is not an allergy.
Allergy symptoms may last longer than cold symptoms.
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Output sentences
use simple syntax and draw on natural-language terms used in original sources
Sentences containing anaphora, instructions, warnings, … are replaced by complete statements constructed via simple syntactic modifications – or ignored.
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Output Sentences
1644 sentences produced (= 20 person hours of effort)500 sentences were subjected to a preliminary evaluation by pairs of medical students (on a score of 1-5 …)
58% were rated by with a score of 2 x 5
but: measures for inter-rater agreement too low for these results to be statistically significant.
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Validation methods
sources
A: filtering for intelligibility by non-experts
pool
B: filtering for non-expert assent C: filtering for validation by
experts
Medical BeliefNet Medical FactNet
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Validation methods
sources
filtering for intelligibility by non-experts
pool
filtering for non-expert assent filtering for validation by experts
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This will provide an empirical delineation of the scope of ‘natural language’ (non-expert language)
Natural language = language (typical) non-experts (think they) can understand
Does ‘depillation’ belong to natural language? ‘suppuration’? ‘auto-immune’? ‘tomograph’? ‘hypertension’? ‘radiologist’?
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Method
400 x 250 statements will be rated for understandability by two participants, making for a total of 200,000 ratings in response to the question: on a scale from 1-5, would you describe this sentence as hard to understand or easy to understand?
Raters will be encouraged not to reflect on successive statements
Only those statements which receive a score of at least 4 from each of 2 subjects will pass on to the pool
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Validation methods
sources
filtering for intelligibility by non-experts
pool
filtering for non-expert assent filtering for validation by
experts
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Method
Collections of 200 statements from the pool will be rated for assent by each of 250 participants.
on a scale from 1-5, would you describe this sentence with the words do not agree at all … agree completely?
Raters will be encouraged to reflect upon their answers if necessary
Statements receiving a score of at least 4 from each of two raters will be stored as components of Medical BeliefNet (MBN).
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Validation methods
sources
filtering for intelligibility by non-experts
pool
filtering for non-expert assent filtering for validation by
experts
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Method
Raters, selected from medical faculty and advanced medical students, will be subject to a pre-evaluation as follows. A set of 40 sentences in the pool will be validated as true or false by the relevant specialists Only those candidate participants with very high scores in matching these validations will be selected to serve as raters in the validations of sentences for MFN.
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Method
Rating for MFN will involve no time constraints raters will be encouraged to use reference works On a scale from 1-5, how strongly do you believe
this statement? Only sentences receiveing scores of 5 from each
of two raters will be added to the MFN database. Thus in relation to those sentences which receive
a score of less than 5, raters will be encouraged to propose alternative statements, which will be used as new input to the non-expert phase for assessment.
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Training of expert raters for MFNwill include e.g. guidance as to the treatment of statements which relate only to what holds for the most part or in most cases.
people with a cold sometimes sneeze
could mean either: not all people with a cold sneeze, contradicting the fact that sneezing is a mandatory symptom for a cold,
or all people with a cold sneeze, but not all the time, which would be rated as correct.
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Evaluation of MWN and MFN
users of a consumer health information portal will be randomly assigned to one of four groups: 1. access to the unsupplemented portal; 2. access also to MWN, 3. access to MFN, 4. access to both MWN and MFN
then apply Saracevic Kantor method for evaluating user satisfaction with internet query services
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Future work
application of MBN/MFN methodology to evaluate the reliability of the medical knowledge of different non-expert communities
by preserving data pertaining to the sources of entries in MBN it will be possible to keep track of specific kinds of false beliefs as originating in specific kinds of informants. This may prove a valuable source of information in targeting specific groups for specific types of remedial medical education.
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Future workexperiments in the tradition of E. Rosch to investigate how the domain of medical phenomena is conceptualized by non-expert human subjects ()
Basic level words: tomato, cabbage vs.bean vs. vegetable (too general) / cherry tomato
(too specific)
what is the basic level of lexical specification in the domain of medical phenomena?what are the basic kinds in the ontology of medicine of natural-language-using subjects?
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Different roles of MFN and MBN
MFN associated with constructing practical tools designed to assist users in coming to believe what is true
MBN associated with researchregarding what people believe about medical phenomena.
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Towards a comprehensive assay of consumer health knowledge
Ultimate goal: to document in an ontologically coherent fashion the entirety of the medical knowledge that is capable of being understood by average adult consumers of healthcare services in the United States today.
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Just as English WordNet
serves as an interlingual index between wordnets in different languages,
so MWN and MFN can function as an inter-ontology index between different expert factnets prepared for different parts of technical biomedical knowledge
NLM goal of expert medical factnet
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ARistOTLEAggregative Realist Ontology of Total
Language
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The End