Medical FactNet

1. Medical FactNet Barry Smith University at Buffalo and IFOMIS, Leipzig Christiane Fellbaum Princeton University and Berlin Academy

2. Online-Inquiry to MEDLINEplus

3. Online-Inquiry to MEDLINEplus

4. 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

5. A New Methodology for the Construction and Validation of Information Resources for Consumer Health

6. 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

7. 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)?

8. 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).

9. Strengths of WordNet 2.0 • Open source • Very broad coverage • Is-a / part-of architecture • Tool for automatic sense disambiguation

10. 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)

11. 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.

12. 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

13. How to validate Medical WordNet?How to fix the scope of ‘non-expert’?

14. 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

15. Medical BeliefNet (MBN) • = totality of sentences about medical phenomena to which non-experts assent • comes for free, given our methodology for creating MFN

16. Sources for MFN • WordNet glosses and arcs • Online health information services targeted to consumers • NetDoctor, MEDLINEplus • (factsheets on common diseases)

17. Medical BeliefNet Medical FactNet 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 ?

18. 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

19. 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.

20. 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.

21. 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.

22. 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

23. 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

24. 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

25. 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

26. 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

27. 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

28. But there are problems

29. 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)

30. 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.

31. MFN • designed to map the generic medical information which non-experts are able to understand

32. 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.

33. 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

34. 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 Specialtiesand Observable Body Parts.

35. 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.

36. 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.

37. 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

38. 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

39. In contrast to CYC, • MFN focuses on one single (albeit very large) domain • MFN stores English sentences (CYC is language non-specific); • MFN discriminates folk beliefs and expert knowledge (designed to be consistent with the body of established science; • MFN will be publicly available.

40. 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)}

41. 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)}

42. 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)}

43. 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.

44. 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

45. 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.

47. 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.

48. 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.

49. Medical BeliefNet Medical FactNet Validation methods • sources • A: filtering for intelligibility by non-experts • pool • B: filtering for non-expert assent C: filtering for validation by • experts

50. Validation methods • sources • filtering for intelligibility by non-experts • pool • filtering for non-expert assent filtering for validation by experts