Bill Moore's Computer Translation Tutorial Page

WHAT IS A SHORT SENTENCE TRANSLATOR?
0. BASIC CONCEPTS. All medical English is divided into STOP WORDS (punctuation, numerals, articles, prepositions, pronouns, common adjectives and verbs) and KEY WORDS (everything else). In the IAD, these words are contained global-variables on the MUMPS-based translator. Stop words (particularly of, the, and) are the most common words in any free-text English document, according to George K. Zipf, a Harvard professor of humanities, who wrote a classic treatise on this subject in the late 1940s (mostly using works of James Joyce), then promptly died, so that the whole world could feel safe in ignoring his work. Zipf's Law states that, if you rank the words in a large document in descending order of word-frequency (i.e., most-frequent-word has rank-one, second-most-frequent-word has rank-two, etc.), then word-rank is inversely proportional to word-frequency. According to Zipf's Law, if the rank-one (i.e., most-frequent) word has, say 15,000 occurrences, then the rank-two word will have 7,500 occurrences, the rank-three word will have 3,750 occurrences, etc. There are studies on large documents in English, German, and Chinese, which demonstrate this relationship.
Stop words are words that nobody would want to search for in an index. An important corollary of Zipf's Law is that the most frequent hundred words in a large document are almost exclusively stop words, and account for well over half the verbiage in the document. They clutter up the indexing globals, and they distract the user from making a well-targeted search. Many stop words are common to all English (articles, prepositions, etc.), but some are idiosyncratic to a particular field, and must be identified and classified by an expert. I am still debating this, but I think that 'disease', 'patient', and 'syndrome' are medical stop-words, because they are so common as to be nearly meaningless. These words would probably NOT be stop words in any other context. By executive fiat, I have declared all one-letter, two-letter, and numeric strings as stop-words, but there are notable exceptions.
1. PREPROCESSING. The first step in indexing a free-text document is to drop the whole document to lower-case, and perform a preprocessing step, so that certain important concepts won't be lost.
In the good old days, medical English consisted of numerous, long Latin words, whose only drawback was that they were frequently misspelled. Poor spelling in a free-text document is death to the indexer. These days, we have decent spelling checkers on electronic documents, but medical English is filling up with short words that appear as stop words to the IAD computer program (cd4, p53, etc.). You cannot fail to index these wordlets, or else many important concepts will become unavailable to the user. The IAD approach, not necessarily a very good one, is to perseverate the alphabetic character, so as to form a three-letter term, which is then saved by the index. Therefore, p-53 is indexed under ppp, ki-1 is indexed under kii, etc. Concepts containing stop words, like 't cell' or 'in vivo' are likewise transformed by global ^IADBPREP() into tcell and invivo.
2. STOP WORDS. After the free-text document has been preprocessed, it is purged of all stop words, including punctuation, numerals, articles, prepositions, pronouns, common adjectives and verbs.
3. EXACT WORDS. The exact form of every key word (i.e., non-stop-word) in the document is indexed by my program. However, this is only the beginning of the indexing process.
4. WORD PREFIXES. There are many medical prefixes in which the user may recall only the suffix, but would like to locate cases with the entire word. For example, a user querying for 'carcinoma' might also wish to locate cases of 'adenocarcinoma', where 'adeno' is a common medical prefix. Therefore, my program redundantly indexes 'adenocarcinoma' both as its exact form, ADENOCARCINOMA, and as its reduced form, CARCINOMA.
5. SINGULAR, COMMON NOUN. It would be annoying for the user to query for LUNG and miss all cases in which contain LUNGS. For that matter, the user asking for LUNG should also not miss cases containing PULMONARY or PULMONIC or even (from the wretched pedant who wrote up that case) PULMON. Therefore, I maintain a global which maps each keyword into the most common, singular, noun form for that word. For example:
lungs^^lung
pulmon^^lung
pulmonaries^^lung
pulmonary^^lung
pulmonic^^lung

Similarly:
livers^^liver
hepar^^liver
hepatic^^liver
hepato^^liver
hepatocellular^^liver^cell

Similarly:
cystic^^cyst
cysts^^cyst
6. HYPHENATED TERMS. Most concepts in medical English exist as multiple word terms, which may be connected by a blank space (mitral valve), a hyphen (mitral-valve), or a null-string (mitralvalve). The word database has undergone this evolution over the past two decades. It is interesting that English, which is historically an amalgam of French and Germanic languages, has a typically French method of expressing a compound-word concept (hyphenation) and a typically German method (agglomeration).
Space-connected words cannot be recognized by the computer program unless they appear in an appropriate list, which can be obtained by the Barrier Word Method. The IAD program indexes multiple word terms redundantly both as the single component words, or as the corresponding multiple-words, recognized by global ^IADBMULT().

WHAT IS THE BARRIER WORD METHOD?
In the following sample text, the stop-words are lower case, and the KEY-WORDS are UPPER CASE:
ACTINIC DEGENERATION . BASOPHILIC DEGENERATION is present in the UPPER DERMIS . the COLLAGEN BUNDLES there have been replaced by AMORPHOUS MATERIAL staining faintly BASOPHILIC , when stained by HEMATOXYLIN and EOSIN . ELECTRON MICROSCOPIC EXAMINATION of areas of ACTINIC DEGENERATION shows ELASTOTIC MATERIAL as the main component . even though this ELASTOTIC MATERIAL resembles ELASTIC TISSUE in its CHEMICAL COMPOSITION , it differs significantly in appearance from AGED ELASTIC FIBERS , as seen in UNEXPOSED AGED SKIN .
That is:
stop-words: KEY-WORDS:
. ACTINIC
is DEGENERATION
present BASOPHILIC
in DEGENERATION
the ........ UPPER ........

This division of the universe into stop-words and key-words supports a valuable trick: the detection of multiple-word terms, such as ACTINIC DEGENERATION, BASOPHILIC DEGENERATION, UPPER DERMIS, COLLAGEN BUNDLES, etc. You will notice that in the above text, the medical multiple-word terms are bounded on either side by a stop-word barrier (including punctuation, numerals, etc.). It is an easy matter to write a program which collects a file of multiple-word terms using this method. You can then revise the list of stop words and key words, and apply the method to the document, to refine the list of stop words.
8. SNOMED TRANSLATION. Currently, SNOMED-translation of words and concepts in the IAD is performed by simple word-substitution or multiple-word-term substitution on the free-text document (after the above processing), using an enriched SNOMED synonym list.
WHY IS SNOMED USED?
SNOMED International, copyright 1994, by the College of American Pathologists (CAP), is much richer than it was in its 1980s edition, and is particularly rich in terms used by pathologists. Furthermore, the current version is rich in Veterinary Medicine terms. The IAD has a limited license to use 'SNOMED-compatible terms', such as 'Lung, NOS' (NOS=not otherwise specified), but NOT T-28000.

WHY IS UMLS NOT USED FOR THE IAD?
Why not MeSH or UMLS? It is our understanding, that the products of the U.S. National Library of Medicine, may not be freely distributed outside the USA. Since the Internet is an international resource, this restriction effectively rules out the UMLS for the IAD.

WHY IS ICD NOT USED FOR THE IAD?
How about the International Classification of Disease (ICD)? This is a product of the World Health Organization, with ABSOLUTELY NO RESTRICTIONS on worldwide distribution. This is also the coding system used by the U.S. Health Care Financing Administration (HCFA), who pay many medical bills in the USA. Only one problem: it's not as rich in raw terms as SNOMED.

WHY IS READ CLASSIFICATION NOT USED FOR THE IAD?
How about the Read classification, used in the United Kingdom? This is the intellectual property of Her Majesty, Elizabeth II. Even single user license are quite expensive.

WHAT IS ZIPF'S LAW?
The extremely high frequency of a few words in any running text may be characterized by `Zipf's Law'. If one determines the frequency of each word in the document, and sorts the words in the descending order of frequency, then the most frequent word is said to have `rank 1', the second-most frequent word has `rank 2', etc. Zipf's Law states that word-frequency is inversely proportional to word-rank. On linear graph paper, the plot of word-frequency against word-rank is a hyperbola; on log-log graph paper, the plot of word-frequency against word-rank is a straight line with a negative slope. Large samples of English, German, and Chinese text have been shown to satisfy Zipf's Law. The essential feature of Zipf's Law is that a short list of words account for a large proportion of all word-occurrences in any natural language text.
Similarly, there is a short list of grammar-formulas, which account for half of all grammar-formula-occurrences in a given text. We assert that, as with words, grammar formulas sufficient to translate a majority of the document belong to a short list, a sort of `Zipf's Law' for grammar formulas.
The consistent presence of Zipf's Law as a property of both words and grammar formulas in natural language text suggests a strategy for constructing both the LEXICON and GRAMMAR. First, one obtains a frequency distribution for both words and parsing formulas. Then one should fashion the major design considerations around these high-frequency elements in the LEXICON or GRAMMAR. Next, moderate-frequency elements should be fashioned around the models created for high-frequency forms. Finally, low-frequency forms which do not fit the existing design may be flagged for pre- or post- editing.
There is a very general relationship between rank, r, in a descending order word list and the frequency, f, of each word, called Zipf's Law. Let r=1 for the most common word, r=2 for the second-most common word, etc. Then f*r=k for some constant, k. That is, there is a hyperbolic relationship (straight line with negative slope on log-log graph paper) between word-rank and word-frequency in large text documents. This relationship is approximately true for (1) the works of James Joyce; (2) the two million words in the century-old autopsy files of The Johns Hopkins Hospital; (3) nine million German words from the Heidelberg Cancer Center; and (4) twenty-five million Chinese characters from the Xinhua Printing Factory in Beijing.

HOW CAN I BUILD A STOPWORD LIST?
You can produce a first-rate list in a few hours. First, guess. You can obtain a first-draft list of stop words consisting of punctuation, numerals, single letters, double letters, articles, conjunctions, prepositions, auxiliary verbs, and common adjectives and verbs (mild, moderate, come, exhibit, demonstrate, go, etc.). Next, produce a list of multiple-word terms consisting of consecutive non-stop-words bounded on either side by stop words. Finally, eyeball the multiple-word term list, and new stop words will jump out at you. Enrich your stop word list with these new stop words, and repeat the process all over again. There are a few problems with this method: for example, 'in situ' and 'in vitro' will not be discovered. These terms must be added manually to your multiple-word term list.

REFERENCES

Moore GW, Boitnott JK, Miller RE, Eggleston JC, Hutchins GM. Integrated pathology reporting, indexing, and retrieval system using natural language diagnoses. Modern Pathol. 1988; 1:44-50.

Berman JJ, Moore GW, Donnelly WH, Massey JK, Craig B. A SNOMED analysis of three years' accessioned cases of (40,124) of a surgical pathology department: implications for pathology-based demographic studies. Journal of the American Medical Informatics Association (JAMIA), Symposium Supplement 1994 and the Proceedings of the 18th Annual Symposium for Computer Appllications in Medicine (SCAMC), pp. 188-192, 1994

Berman JJ, Moore GW. SNOMED-Encoded surgical pathology databases: a tool for epidemiologic investigation. In press, Modern Pathology.

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Moore GW, Berman JJ. Automatic versus manual SNOMED coding: quantitating the differences. Am J Clin Pathol 101:253-256, 1994.

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Berman JJ, Moore GW, Donnelly WH, Massey, JK, Craig B. SNOMED Analysis of 40,124 Surgical Pathology Cases. Am J Clin Pathol 102:539-540, 1994

Berman JJ, Moore GW. SNOMED-Encoded surgical pathology databases: a tool for epidemiologic investigation. Modern Pathology 9:944-950, 1996.

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INTERNET: The Oxford Text Archive: ftp://ota.ox.ac.uk. A fantastic collection of various wordlists, including an 8 megabyte unabridged dictionary; various English language lists (British, American, Australian, Shakespeare), as well as lists in Afrikaans, Chinese, Croatian, Danish, Dutch, Esperanto, Finnish, French, German, Hindi, Hungarian, Italian, Japanese, Latin, Norwegian, Polish, Russian, Spanish, Swahili, Swedish, Yiddish.

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Moore GW, Riede UN, Polacsek RA, Miller RE, Hutchins GM: Automated translation of German to English medical text. Am J Med 81:103-111, 1986.

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Weiss SM, Kulikowski CA and Safir A: Glaucoma consultation by computer. Comput Biol Med 8:25-40, 1978.

Pauker SG, Gorry GA, Kassirer JP, Schwartz WB: Towards the simulation of clinical cognition. Taking a present illness by computer. Am J Med 60:981-996, 1976.

Szolovitz P, Pauker SG: Categorical and probabilistic reasoning in medical diagnosis. Artif Intel 11:115-144, 1978.

Shortliffe EH, Buchanan BG: A model of inexact reasoning in medicine. Math Biosci 23:351-379, 1975.

Shortliffe EH, Buchanan BG and Feigenbaum EA: Knowledge engineering for medical decision making: A review of computer-based clinical decision aids. Proc IEEE 67:1207-1224, 1979.

Duda RO, Shortliffe EH: Expert systems research. Science 220:261-268, 1983.

Pauker SG and Kassirer JP: The threshold approaches to clinical decision making. N Engl J Med 302:1109-1117, 1980.

Schwartz WB, Wolfe HJ and Pauker SG: Pathology and probabilities. A new approach to interpreting and reporting biopsies. N Engl J Med 305:917-923, 1981.

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Miller RA, Pople HE Jr and Myers JD: Internist-I, an experimental computer-based diagnostic consultant for general internal medicine. N Engl J Med 307:468-476, 1982.

Miller RA: Internist-l/Caduceus: Problems facing expert consultant programs. Methods Inf Med 23:9-14, 1984.

Halpern JY, Rabin MO: A logic to reason about likelihood. IBM Research Report RJ 4313, 1984.

Zeman JJ: Modal Logic. The Lewis-Modal Systems, Oxford at the Clarendon Press, London, 1973.

Anderson JM, Johnstone HW Jr: Natural Deduction. The Logical Basis of Axiom Systems. Wadsworth Publishing Co, Inc, Belmont, CA, l962.

Quine WV: Theory of Deducation, Harvard Cooperative Society, Cambridge, Mass, 1948, pp 65-81.

Quine WV: Methods of Logic. Henry Holt & Co, New York, 1950.

Quine WV: The problem of simplifying truth functions. Am Math Monthly 59:521-531, 1952.

Quine WV: A way to simplify truth functions. Am Math Monthly 62:627-631, 1955.

McCluskey EJ Jr: Minimization of Boolean Functions. Bell Syst Tech J 36:1417-1444, 1956.

Swets JA: Information retrieval systems. Science 141:245-250, 1963.

Lusted LB: Introduction to Medical Decision Making. Charles C Thomas, Springfield, Ill, 1968, p 98-140.

Lusted LB: Decision-making studies in patient management. N Engl J Med 284:416-424, 1971.

Swets JA: Signal Detection in Medical Diagnosis. In, Computer Diagnosis and Diagnostic Methods (Jacquez JA, ed). Charles C Thomas, Springfield, Ill, 1972, p 8-28.

Meadow CT: The Analysis of Information Systems. Second Edition. Melville Publishing Co., Los Angeles, CA, 1973.

Date CJ: An Introduction to Database Systems. Addison-Wesley Publishing Co., Reading, Mass., 1975.

Greenes RA, Pappalardo AN, Marble CW, Barnett GO: Design and implementation of a clinical data management system. Comput Biomed Res 2:469-485, 1969.

Bowie J and Barnett GO: MUMPS - An economical and efficient time-sharing system for information management. Comput Programs Biomed 6:11-22, 1976.

CONFIDENTIALITY AND PRIVACY.

Schneier B. Applied Cryptography. Protocols, Algorithms, and Source Code in C. New York: John Wiley & Sons, Inc. 1996.

Berman JJ, Moore GW. SNOMED-Encoded surgical pathology databases: a tool for epidemiologic investigation. Modern Pathology 9:944-950, 1996.

Berman JJ, Moore GW, Hutchin GM. Maintaining patient confidentiality in the public domain internet autopsy database. Journal of the American Medical Informatics Association (JAMIA), Symposium Supplement, pp 328-332, 1996.

Clayton EW, Steinberg KK, Khoury MJ, Thomson E, Andrews L, Kahn MJ, Kopelman LM, Weiss JO. Informed consent for genetic research on stored tissue samples. JAMA 1996;1786-92.

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