{ "paper_id": "C82-1032", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T13:12:52.833467Z" }, "title": "ANALYSIS AND PROCESSING OF COMPACT TEXT", "authors": [ { "first": "Elaine", "middle": [], "last": "Marsh", "suffix": "", "affiliation": { "laboratory": "", "institution": "New York University New York", "location": { "region": "New York U.S.A" } }, "email": "" }, { "first": "Naomi", "middle": [], "last": "Sager", "suffix": "", "affiliation": { "laboratory": "", "institution": "New York University New York", "location": { "region": "New York U.S.A" } }, "email": "" } ], "year": "", "venue": null, "identifiers": {}, "abstract": "This paper describes the characteristics of compact text as revealed in computer analysis of a set of physician notes. Computer processing of the documents was performed using the LSP system for natural language analysis. A numerical breakdown of syntactic and semantic patterns found in the texts is pre~ented. It is found that four major properties of compact text make it possible to process the content of the documents with syntactic procedures that operate on full free text.", "pdf_parse": { "paper_id": "C82-1032", "_pdf_hash": "", "abstract": [ { "text": "This paper describes the characteristics of compact text as revealed in computer analysis of a set of physician notes. Computer processing of the documents was performed using the LSP system for natural language analysis. A numerical breakdown of syntactic and semantic patterns found in the texts is pre~ented. It is found that four major properties of compact text make it possible to process the content of the documents with syntactic procedures that operate on full free text.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Abstract", "sec_num": null } ], "body_text": [ { "text": "Linguistic mechanisms of compacting are common in situations where specialists record facts to be used by others in the same field. Compact text is found in notes and records within institutions (i), and in network messages among collaborators. It is found in a different form in everyday dialogue (2) , and in headings and headlines (3) . (i) Positive for heart disease and diabetes.", "cite_spans": [ { "start": 298, "end": 301, "text": "(2)", "ref_id": "BIBREF1" }, { "start": 334, "end": 337, "text": "(3)", "ref_id": "BIBREF2" } ], "ref_spans": [], "eq_spans": [], "section": "INTRODUCTION", "sec_num": null }, { "text": "(2) Q: How are you?", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "INTRODUCTION", "sec_num": null }, { "text": "Ri Fine.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "INTRODUCTION", "sec_num": null }, { "text": "(3) REAGAN NAMES WOMAN JUSTICE Similar shortened forms also occur in published research protocols and non-numeric tables (as in building codes, geological surveys).", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "INTRODUCTION", "sec_num": null }, { "text": "The NYU Linguistic String Project has developed a computer program to analyze and process the compact text of specialized technical areas, using a general parsing program and English grammar augmented by procedures specific to the given subject area [1, 2] .", "cite_spans": [ { "start": 250, "end": 253, "text": "[1,", "ref_id": "BIBREF0" }, { "start": 254, "end": 256, "text": "2]", "ref_id": "BIBREF1" } ], "ref_spans": [], "eq_spans": [], "section": "INTRODUCTION", "sec_num": null }, { "text": "In particular, the system has been tailored for the computer analysis of free-text medical records [3] . The note-taking style of medical records [4] uses a remarkable amount of reduced English sentence forms. For instance, in the documents reported on here, 49% of occurrences were incomplete sentences. Similar types of reductions have been described in other technical sublanguages [5] . In this paper, we report the results of using the LSP processor to obtain a precise description of the syntactic and semantic properties of a body of compact text.", "cite_spans": [ { "start": 99, "end": 102, "text": "[3]", "ref_id": "BIBREF2" }, { "start": 146, "end": 149, "text": "[4]", "ref_id": "BIBREF3" }, { "start": 385, "end": 388, "text": "[5]", "ref_id": "BIBREF4" } ], "ref_spans": [], "eq_spans": [], "section": "INTRODUCTION", "sec_num": null }, { "text": "The data of this study consist of eight medical discharge summaries, each 1-2 pages in length.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "DATA ANDMETHODS OF ANALYSIS", "sec_num": null }, { "text": "These physician reports were dictated and then transcribed by a medical typist into computer readable form. As a preliminary to machine processing, there was minimal pre-editing of the documents for such necessary formatting changes as inserting a blank between a word and its following punctuation mark and occasional spelling corrections.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "DATA ANDMETHODS OF ANALYSIS", "sec_num": null }, { "text": "Abbreviations were maintained as used in the text and were treated as dictionary entries linked to their full form in the dictionary.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "DATA ANDMETHODS OF ANALYSIS", "sec_num": null }, { "text": "A computer dictionary of the document words was obtained by look-up in the medical parsing dictionary developed by the LSP, which was augmented for new words appearing in these texts.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "DATA ANDMETHODS OF ANALYSIS", "sec_num": null }, { "text": "The dictionary classifies words according to their major parts of speech (e.g. noun, verb, adjective), as well as certain English subclasses (e.g. plural, past) and special medical subfield classes.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "DATA ANDMETHODS OF ANALYSIS", "sec_num": null }, { "text": "The medical classes are based on cooccurrence properties of the words as seen in a larger survey of the material and are checked for semantic consistency by a physician-consultant.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "DATA ANDMETHODS OF ANALYSIS", "sec_num": null }, { "text": "In total, the medical classes currently number sixty-six.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "DATA ANDMETHODS OF ANALYSIS", "sec_num": null }, { "text": "These are used in selectional constraints applied during parsing to resolve syntactic ambiguities.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "DATA ANDMETHODS OF ANALYSIS", "sec_num": null }, { "text": "A smaller set of eighteen medical classes determine the major semantic sentence types discussed below.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "DATA ANDMETHODS OF ANALYSIS", "sec_num": null }, { "text": "Descriptions of the 18 medical classes are given in Table i , drawn from [6] , where all the medical classes are defined and details of the text processing procedure are described.", "cite_spans": [ { "start": 73, "end": 76, "text": "[6]", "ref_id": "BIBREF5" } ], "ref_spans": [ { "start": 52, "end": 59, "text": "Table i", "ref_id": "TABREF1" } ], "eq_spans": [], "section": "DATA ANDMETHODS OF ANALYSIS", "sec_num": null }, { "text": "The input sentences from the discharge summaries are structured by a three stage system of (i) parsing, (2) syntactic regularizat~on: and (3) mapping into an information format.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "DATA ANDMETHODS OF ANALYSIS", "sec_num": null }, { "text": "The first step is to parse the document sentences with the Linguistic String Project parser [7] .", "cite_spans": [ { "start": 92, "end": 95, "text": "[7]", "ref_id": "BIBREF6" } ], "ref_spans": [], "eq_spans": [], "section": "DATA ANDMETHODS OF ANALYSIS", "sec_num": null }, { "text": "This step begins with a dictionary lookup to associate the stored lexical information with each word occurrence im the sentence.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "DATA ANDMETHODS OF ANALYSIS", "sec_num": null }, { "text": "The parsing utilizes a medically tuned grammar, which includes productions for the compact sentence syntactic types discussed below, and also productions for special sublanguage constructions (e.g. dose expressions, penicillamine 250 MG PO (~D_). The sentence parse identifies grammatical relations which hold among parts of the sentence, principally subject-verb-object relations and host-modifier relations.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "DATA ANDMETHODS OF ANALYSIS", "sec_num": null }, { "text": "Also built into the grammar is a selectional mechanism which disambiguates multiply classified words based on the type of subject-verb-object or host-modifier relationships permitted in the sublanguage [8] .", "cite_spans": [ { "start": 202, "end": 205, "text": "[8]", "ref_id": "BIBREF7" } ], "ref_spans": [], "eq_spans": [], "section": "DATA ANDMETHODS OF ANALYSIS", "sec_num": null }, { "text": "The second step is syntactic regularization.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "DATA ANDMETHODS OF ANALYSIS", "sec_num": null }, { "text": "Each sentence undergoes a series of paraphrastic English transformations regularizing the syntax within the sentences in order to reduce the variety of syntactic structures to a set of basic syntactic relations [ 9 ] . The syntactic regularization does not alter the information content of the sentences.", "cite_spans": [ { "start": 211, "end": 216, "text": "[ 9 ]", "ref_id": "BIBREF8" } ], "ref_spans": [], "eq_spans": [], "section": "DATA ANDMETHODS OF ANALYSIS", "sec_num": null }, { "text": "In addition, reduced word forms, such as abbreviations, are replaced by their full forms.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "DATA ANDMETHODS OF ANALYSIS", "sec_num": null }, { "text": "The final processing step is information formatting, which maps the words of the parsed, transformed sentence into a tabular representation of the information contained in the sentences.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "DATA ANDMETHODS OF ANALYSIS", "sec_num": null }, { "text": "A word is mapped into the format column which corresponds to the information content of the word.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "DATA ANDMETHODS OF ANALYSIS", "sec_num": null }, { "text": "In general, there is a l-to-i correspondence between the sublanguage word class and a particular format column. For example, a word of the medical class DIAG(nosis), e.g. rae~, would be mapped into the DIAG column of the format.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "DATA ANDMETHODS OF ANALYSIS", "sec_num": null }, { "text": "Formatting is based on cooccurrence patterns found in the text and on the lexical information obtained from the computer dictionary of medical vocabulary.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "DATA ANDMETHODS OF ANALYSIS", "sec_num": null }, { "text": "The sets of filled format columns represent the semantic patterns found within the data.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "DATA ANDMETHODS OF ANALYSIS", "sec_num": null }, { "text": "These semantic patterns will be discussed below.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "DATA ANDMETHODS OF ANALYSIS", "sec_num": null }, { "text": "The data was run on the LSP natural language processing system as implemented in FORTRAN and run on a Control Data 6600, requiring about 75,000 words of memory. The LSP system also runs on the CDC C~ER, the VAX, and UNIVAC ii00 machines. In a test set of narrative hospital discharge summaries that were computer-parsed, 49% of the sentences were syntactically incomplete sentence forms (\"fragments\"). The fragments were of six types that can be related to full sentence forms on the basis of the elements which were regularly deleted: (i) deleted verb and object (or subject and verb), leaving a noun phrase (4); (ii) deleted tense and verb b__ee (5); (iii) deleted subject, tense, and verb be (6); (iv) deleted subj'ect (7); (v) deleted subject, tense, and verb be (passive predicate) (8); and (vi) deleted subject, tense, and verb be (infinitival complement) (9). (2%) (9) To be followed in hematology clinic.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "DATA ANDMETHODS OF ANALYSIS", "sec_num": null }, { "text": "V-MANAGE V-RESPOND V", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "DATA ANDMETHODS OF ANALYSIS", "sec_num": null }, { "text": "(i instance = .08%)", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "DATA ANDMETHODS OF ANALYSIS", "sec_num": null }, { "text": "Viewing these fragments as deletion forms, it is possible to fill them out to full sentences that would be accepted as paraphrastic by \"native speakers\" of medical language. For example, occurrences (4)-(9) can be related to sentences (10)-(15) respectively. In a set of 8 analyzed hospital discharge summaries, we found 41 cooccurrence patterns of the subject-verb-object (SVO) type, stated in terms of 18 participating word classes. (There were further patterns of the host-modifier type and some larger patterns involving connective between SVO types.) The SVO patterns could begrouped into six more general types by defining a \"superclass\" (RESULT) consisting of the classes SIGN-SYMPTOM, LAB-RES, QUANT, NORMALCY, DIAGNOSIS, and DESCRIPTION that occurred with one of the subject classes PATIENT, BODY-P#~T, TEST. For example, sentence (5) is of the type TEST RESULT, where scan is the name of a test (occurring with BODY-PART word brain as modifier), and ~ (in the class NORMALCY in the superclass RESULT) is the finding of the test. Examples of the sentence patterns are provided in Table 2 .", "cite_spans": [], "ref_spans": [ { "start": 1089, "end": 1096, "text": "Table 2", "ref_id": "TABREF2" } ], "eq_spans": [], "section": "DATA ANDMETHODS OF ANALYSIS", "sec_num": null }, { "text": "The computer results presented above, considered along with manual analysis of other document sets, lead to several major conclusions about the characteristics of compact text: I. Repetitive ungranmlaticality is grarmnatical for the text set.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "CONCLUSIONS", "sec_num": null }, { "text": "Within a given set of data, there are recurrent ungrammatical constructions. These forms can be characterized and made a part of the parsing grammar. The departures from grammaticality are limited and can be related in a regular way to full sentente types in English.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "CONCLUSIONS", "sec_num": null }, { "text": "In repetitive single-topic text, word subclasses that are specific to the subject While it would be improper to say \"when you've seen one, you've seen them all,\" compact te~ts within a given area are remarkably similar. In the set of eight documents referred to above, six generalized semantic patterns occurred in the first document processed. No new types were recognized in the remaining seven documents.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "II. Word choice is quasi-grammatical.", "sec_num": null }, { "text": "On'the lexical level, while new vocabulary is found in each new document (socalled \"seepage\"), this tends to taper off. In a prior study of journal articles, it was found that sees,age after the 7th article remained at about 20%. In the processing of medical records, we found that, when changing from one medical subfield to another, the new vocabulary in a set of documents containing 2200 distinct lexical items was 27%.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "II. Word choice is quasi-grammatical.", "sec_num": null }, { "text": "The above four properties of compact text: grammaticallty despite syntactic deviation, regular patterning of subject-specific vocabulary, recoverable deletions, and convergence as new texts are analyzed, make it possible to process the content of documentS with syntactic procedures that operate on the full free text.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "II. Word choice is quasi-grammatical.", "sec_num": null } ], "back_matter": [], "bib_entries": { "BIBREF0": { "ref_id": "b0", "title": "Natural Language Information Processing: A Computer Grammar of English and Its Applications", "authors": [ { "first": "N", "middle": [], "last": "Sager", "suffix": "" } ], "year": 1981, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Sager, N. (1981). Natural Language Information Processing: A Computer Grammar of English and Its Applications. Addison-Wesley, Reading, Massachusetts.", "links": null }, "BIBREF1": { "ref_id": "b1", "title": "Natural Language Information Formatting", "authors": [ { "first": "N", "middle": [], "last": "Sager", "suffix": "" } ], "year": 1978, "venue": "Adv. Comput", "volume": "17", "issue": "", "pages": "89--162", "other_ids": {}, "num": null, "urls": [], "raw_text": "Sager, N. (1978). Natural Language Information Formatting. Adv. Comput. 17, (M.C. Yovits, ed.), 89-162. Academic Press, New York.", "links": null }, "BIBREF2": { "ref_id": "b2", "title": "Automatic Information Formatting of a Medical Sublanguage. Sublanguage: Studies of Language in Restricted Semantic Demains", "authors": [ { "first": "L", "middle": [], "last": "Hirschman", "suffix": "" }, { "first": "N", "middle": [], "last": "Sager", "suffix": "" } ], "year": 1982, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Hirschman, L. and N. Sager (1982). Automatic Information Formatting of a Medi- cal Sublanguage. Sublanguage: Studies of Language in Restricted Semantic De- mains (R\u00b0 Kittredge and J. Lehrberger, eds.). Walter de Gruyter, Berlin.", "links": null }, "BIBREF3": { "ref_id": "b3", "title": "Grammatical Compression in Notes and Records", "authors": [ { "first": "B", "middle": [], "last": "Anderson", "suffix": "" }, { "first": "I", "middle": [ "D J" ], "last": "Bross", "suffix": "" }, { "first": "N", "middle": [], "last": "Sager", "suffix": "" } ], "year": 1975, "venue": "Am. J. Comput. Linguist", "volume": "2", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Anderson, B., Bross, I.D.J. and N. Sager (1975). Grammatical Compression in Notes and Records. Am. J. Comput. Linguist. 2:4.", "links": null }, "BIBREF4": { "ref_id": "b4", "title": "Automatic Translation and the Concept of Sublanguage", "authors": [ { "first": "J", "middle": [], "last": "Lehrberger", "suffix": "" } ], "year": 1982, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Lehrberger, J. (1982). Automatic Translation and the Concept of Sublanguage. Sublan_g~ (as in ref. 3, above).", "links": null }, "BIBREF5": { "ref_id": "b5", "title": "Information Structures in the Language of Science", "authors": [ { "first": "N", "middle": [], "last": "Sager", "suffix": "" }, { "first": "L", "middle": [], "last": "Hirschman", "suffix": "" } ], "year": 1978, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Sager, N. and L. Hirschman (1978). Information Structures in the Language of Science. String Program Repts. 12. Linguistic String Project, New York Univ.", "links": null }, "BIBREF6": { "ref_id": "b6", "title": "The Linguistic String Parser. Prec", "authors": [ { "first": "R", "middle": [], "last": "Grishman", "suffix": "" }, { "first": "N", "middle": [], "last": "Sager", "suffix": "" }, { "first": "C", "middle": [], "last": "Raze", "suffix": "" }, { "first": "B", "middle": [], "last": "Bookchin", "suffix": "" } ], "year": 1973, "venue": "Nat. Comput. Conf", "volume": "", "issue": "", "pages": "427--434", "other_ids": {}, "num": null, "urls": [], "raw_text": "Grishman, R., Sager, N., Raze, C. and B. Bookchin (1973). The Linguistic String Parser. Prec. 1973 Nat. Comput. Conf., 427-434, AFIPS Press, Montvale, N.J.", "links": null }, "BIBREF7": { "ref_id": "b7", "title": "Natural Language Interfaces Using Limited Semantic Information. Prec. 9th Int. Conf. Comput. Ling", "authors": [ { "first": "R", "middle": [], "last": "Grishman", "suffix": "" }, { "first": "L", "middle": [], "last": "Hirschman", "suffix": "" }, { "first": "C", "middle": [], "last": "Friedman", "suffix": "" } ], "year": 1982, "venue": "", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "Grishman, R., Hirschman, L., and Friedman, C. (1982). Natural Language Inter- faces Using Limited Semantic Information. Prec. 9th Int. Conf. Comput. Ling.", "links": null }, "BIBREF8": { "ref_id": "b8", "title": "The Automatic Transformational Analysis of English Sentences", "authors": [ { "first": "J", "middle": [], "last": "Hobbs", "suffix": "" }, { "first": "R", "middle": [], "last": "Grishman", "suffix": "" } ], "year": 1976, "venue": "Int. J. Comput. Math., Sect. A", "volume": "5", "issue": "", "pages": "267--283", "other_ids": {}, "num": null, "urls": [], "raw_text": "Hobbs, J. and R. Grishman (1976). The Automatic Transformational Analysis of English Sentences. Int. J. Comput. Math., Sect. A, Vol. 5, pp. 267-283.", "links": null } }, "ref_entries": { "FIGREF0": { "num": null, "type_str": "figure", "text": "The English grammar, regularlzation component, and information formatting component are written in Restriction Language, a special high level language developed for writing natural language grammars.", "uris": null }, "FIGREF1": { "num": null, "type_str": "figure", "text": "Stiff neck and fever. (63%) (5) Brain scan negative. (22%) (6) Positive for heart disease and diabetes. (8%) (7) Was seen by local doctor. (5%) (8) Treated for meningitis.", "uris": null }, "FIGREF2": { "num": null, "type_str": "figure", "text": "Patient had stiff neck and fever. (ii) Brain scan was negative. (12) Patlent/test/exam [depending on context] is positive for heart disease. (13) Patient was seen by local doctor. (14) Patient was treated for meningitis. (15) Patient is to be followed in hematology clinic.", "uris": null }, "TABREF1": { "num": null, "html": null, "text": "", "content": "
RESULTS
Medical Word Subclasses
", "type_str": "table" }, "TABREF2": { "num": null, "html": null, "text": "Deletions are recon~tructable on the basis of both syntax and regularity of subclass patterning. It was seen above that deleted (reconstructable) elements are either function words known to be deleted in other English'forms (e.g. be), or \u2022 distinguished words of the sublanguage (e.g. ~).", "content": "
Medical Semantic Patterns
PATTERN
I (22%) B-PART + TEST V RESULT = {LAB-RES/NORM/QUANT/S-S/DIAG/DESCR}
LAB-RESH. Influenzae type B from CSF.
NORMSpinal fluid was negative.
QUANTHematocrit 22.6 percent.
S-SChest x-rays suggest pleural effusion.
DIAGChest x-rays revealed bilateral pneumonia.
II (53%) EXAM/FUNCTION VRESULT (excluding LAB-RES)
B-PARTS-SDeveloped swollen hands; Abdomen showed no
organomegaly.
DIAGLingular pneumonia.
NORMENT negative; Lungs clear.
DESCRConjunctivae were pale.
B-FUNCNORMAppetite is good; She slept well; Eating well.
S-SPain on dorsiflexion of left foot.
B-MEASQUANTTemp 99.6, Pulse 120, Respiration Rate 16,
Weight 19.5 ibs.
NORMTemp normal.
DESCRLow grade temp finally cleared.
B-PART-EXAMNORMDTR's are normal.
S-SHeart murmur heard; Slight tenderness to touch,
QUANTLiver palpable 6 cm.
DIAGMeningitis; Has sickle cell disease.
S-SPatient began to vomit; Patient developed
mild cold.
NORMShe remained well; Pt had a complete recovery.
DESCRPatient was active; Occasionally rubs hands.
III (2%) DEVELRESULT
NORMWell developed; Growth and development of
patient was normal.
S-SPt is product of complicated pregnancy.
DESCRDelivery was spontaneous.
QUANTProduct of gravida i0 para 9 pregnancy.
IV (9%) INST V-MANAGE(DIAG/S-S}
DIAGist admission to BH for meningitis.
S-SShe was seen in Emergency Room because of a
temp of 105.
To be followed in hematology; Seen in
Pediatric Emergency Service.
V (13%) (INST) V-TREAT-with MED Treatment with ampicillin; Partial exchange
transfusion Was given; Was given phenobarbitol;
Resume prophylactic penicillin orally;
VI (1%) {PT/S-S} V-RESPOND-to MED
PTShe responded well to penicillin.
S-SSeizures were controlled by valium.
Notes:i) Examples for each pattern are illustrative, not exhaustive.
2) In many cases B-PART word, TEST word are deleted when reconstructable
from context, e.g. CSF grew out pneumococeus = CSF culture grew out
pneumococcus.
3) Mention of PATIENT is omitted in Patterns I-V.
4) Key: ( ) = optional element
{ } = choice of one among elements in braces
", "type_str": "table" } } } }