Medical FactNet Barry Smith University at Buffalo and IFOMIS, Leipzig Christiane Fellbaum Princeton University and Berlin Academy Online-Inquiry to MEDLINEplus Query 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 Online-Inquiry to MEDLINEplus Query 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 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 A New Methodology for the Construction and Validation of Information Resources for Consumer Health 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 nonexperts provision of a mapping to UMLS, MeSH, and other expert terminologies use as interlingua for MWNs in other languages 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)? Building blocks of WordNet = ‘synsets’ = ‘concepts’ in medical terminology terms 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). Strengths of WordNet 2.0 Open source Very broad coverage Is-a / part-of architecture Tool for automatic sense disambiguation 13 senses for feel is a verb experience – She felt resentful find – I feel that he doesn't like me feel – She felt small and insignificant; feel – We felt the effects of inflation feel – The sheets feel soft grope –He felt for his wallet finger – Feel this soft cloth! explore – He felt his way around the dark room) feel – It feels nice to be home again feel – He felt the girl in the movie theater) 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. MWN many word units are monosemic (clinician, stethoscope) most common words are polysemic lexicon 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 How to validate Medical WordNet? How to fix the scope of ‘non-expert’? 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 Medical BeliefNet (MBN) = totality of sentences about medical phenomena to which non-experts assent comes for free, given our methodology for creating MFN Sources for MFN 1. WordNet glosses and arcs 2. Online health information services targeted to consumers NetDoctor, MEDLINEplus (factsheets on common diseases) Constructing MBN and MFN sources (WordNet, MEDLINEplus …) filtering for intelligibility by non-experts pool of natural language sentences filtering for non-expert assent Medical BeliefNet filtering for validation by experts ? Medical FactNet 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 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 devices that need to be interpreted with respect to other sentences. 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 nonexpert English-speakers. Constructing MBN Sentences in MBN must receive high marks for assent on being assessed by nonexperts. 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. 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 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 Evaluation of MFN measure the benefits it brings when incorporated into an existing on-line consumer health portal based on termsearch technology. test whether exploiting the resources of MFN can lead to improved results in the retrieval of expert information Differences between expert and nonexpert 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 Differences between expert and nonexpert 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 Mismatches in Doctor-Patient Communication Practical 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 But there are problems 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 varicellazoster immune globulin (VZIG) can be given up to 48 hours after exposure and still prevent disease. ... (from Slaughter) 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. MFN designed to map the generic medical information which non-experts are able to understand 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. 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 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. 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. 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. Both FrameNet and Proposition Bank have 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 Example: CYC knowledge base collection of hundreds of thousands of statements mostly about the external world: The earth is round Mountains are one kind of landform Albany is the capital of New York parcelled into micro-theories 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. Existing Princeton WordNet 2.0 labels 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)} 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)} 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)} 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. 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 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. Source (NIAID) Output There 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. Output sentences use simple syntax and draw on naturallanguage terms used in original sources Sentences containing anaphora, instructions, warnings, … are replaced by complete statements constructed via simple syntactic modifications – or ignored. 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. Validation methods sources A: filtering for intelligibility by non-experts pool B: filtering for non-expert assent Medical BeliefNet C: filtering for validation by experts Medical FactNet Validation methods sources filtering for intelligibility by non-experts pool filtering for non-expert assent filtering for validation by experts This will provide an empirical delineation of the scope of ‘natural language’ (non-expert language) Natural language = language (typical) nonexperts (think they) can understand Does ‘depillation’ belong to natural language? ‘suppuration’? ‘auto-immune’? ‘tomograph’? ‘hypertension’? ‘radiologist’? 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 Validation methods sources filtering for intelligibility by non-experts pool filtering for non-expert assent filtering for validation by experts 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). Validation methods sources filtering for intelligibility by non-experts pool filtering for non-expert assent filtering for validation by experts 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. 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. Training of expert raters for MFN will 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. 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 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. Future work experiments 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? 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. 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. Just as English WordNet serves as an interlingual index between wordnets in different languages, so MWN and MFN can function as an interontology index between different expert factnets prepared for different parts of technical biomedical knowledge NLM goal of expert medical factnet ARistOTLE Aggregative Realist Ontology of Total Language The End
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