Terminologies, Classifications and Groupings

1
Terminologies, Classifications and Groupings

• Dr M N Kamel BoulosXaBpR (aka Dermatologist)E-ma
  il dk708_at_city.ac.uk
• 2001

MIM Centre,City University,London
2
Why Use a Clinical Terminology? Free-Text Search
Weaknesses

• Brute force free-text search techniques cannot
  locate relevant knowledge efficiently for three
  reasons
• The sought page might be using a different term
  (or synonym) that points to the same concept.
  Myocardial infarction and coronary thrombosis
  cannot be matched, although they are the same.
• Spelling mistakes and variants are considered as
  different terms in a computer environment. For
  example, psoriasis (correct spelling) and
  psoriaisis (typographical error) cannot be
  matched. Similarly, anaemia (correct UK spelling)
  and anemia (correct US spelling) cannot be
  matched.

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Why Use a Clinical Terminology? Free-Text Search
Weaknesses (Contd)

• Brute force free-text search techniques cannot
  locate relevant knowledge efficiently for three
  reasons (Contd)
• In the bibliographic world, e.g., MEDLINE, search
  engines cannot process HTML intelligently. For
  example, searching for resources on psoriasis
  will retrieve all the documents containing this
  word, but many of these resources might not be
  relevant, i.e., psoriasis was just mentioned by
  the way in these documents and is not their
  actual topic. For example, some documents might
  be mentioning psoriasis within a See also
  psoriasis sentence, e.g., at the bottom of a
  page covering another papulous squamous disease,
  or under the differential diagnosis section of a
  page covering another disease, e.g., Reiters
  disease or seborrhoeic dermatitis.

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You might be doing a search on the term stroke
(cerebral infarction) and end up with documents
that teach you about the workings of the
two-stroke motorcycle engine. The
non-discriminatory free-text method of document
retrieval inevitably produces a number of
irrelevant leads or noise (Kiley, 1999).
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Solution

• Structured (headings), coded data entry.
• It is essential that healthcare professionals
  agree on the nature and content of the component
  data sets of the different EPRs (e.g., record
  structures and headings related to the different
  medical, surgical and nursing specialities), so
  that consistent basic models of these records can
  be constructed and shared in a reliable way. (The
  framework of headings Web site http//www.nhsia.nh
  s.uk/headings/ is a good example of
  well-organised standardisation efforts.)

Headings are very important. For example, the
same term schizophrenia assumes different
meanings/implications depending on whether it
appears under diagnosis or present/past history
or family history headings in an electronic
patient record.
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Introduction to Clinical Coding

• Clinical coding lies at the heart of successful
  implementation of the EPR and integrated decision
  support modules.
• EPR coding is done using a fine granularity
  terminology (or controlled healthcare vocabulary)
  like Read CTV3 (Read Clinical Terms Version 3).
• A controlled healthcare vocabulary is a system of
  concepts to populate electronic healthcare
  applications. Controlled healthcare vocabularies
  are products of the electronic era, designed to
  support computer-based functionality.
• Read CTV3 allows not only the coding of diagnoses
  and drugs (treatment), but also the coding of
  symptoms and signs, and of different tests and
  investigations. Moreover, Read CTV3 is a
  compositional terminology, which means that
  concepts can be constructed from primitive
  building blocks with rules controlling different
  combinations.

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Introduction to Clinical Coding

• On the other hand, a clinical classification
  allows categorisation of clinical data according
  to intrinsic rules. Formal clinical
  classifications have existed for over 100 years,
  initially for mortality, but more recently for
  morbidity and interventions. Classifications like
  the WHO's ICD-10 (The International Statistical
  Classification of Diseases and Related Health
  Problems, tenth revision) offer a coarser
  granularity (1000s of entries vs. 100,000s of
  entries in clinical terminologies) and only
  single parentage (so that an item may not be
  counted twice under different headings), and are
  therefore more suitable for statistical reporting
  (national statistics and international
  comparisons) using aggregated data.

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Introduction to Clinical Coding

• Groupings like HRGs (Health Resource Groups) have
  an even much coarser granularity, lumping
  together tens of different conditions in single
  groups according to their resource consumption
  (100s of groups). Grouping information from
  aggregated EPRs helps in resource management,
  planning and budget negotiations.

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Introduction
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How Do Concept Codes Help in Decision Support and
Research?
All the concepts represented by the terms in Read
CTV3 are arranged in a hierarchy (multiple
parentage allowed), i.e., they are semantically
defined within the clinical vocabulary. The
hierarchy describes which concepts are types of
something else. Consider this example Myocardial
infarction (and its synonyms) is a type
ofIschaemic heart disease (and its
synonyms) which is a type ofDisorder of heart
(and its synonyms) which is a type
ofCardiovascular disorder (and its
synonyms) which is a type ofDisorders (or
diseases) which is a type ofClinical findings
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How Do Concept Codes Help in Decision Support and
Research?
Now suppose that a doctor wishes to prescribe a
drug that must not be used by anyone having a
heart disorder. Because the clinical terminology
knows every condition that is a type of heart
disorder, it can automatically check the
patients record to see whether the patient has
any of these conditions. This could not have been
achieved with a free text patient record. For
instance, Angina is a type of heart disorder, but
this could not have been detected in a text-based
patient record, where the best that can be
achieved is to search for the word "heart" in the
text. Nor would it have been possible to search
for words "heart OR angina OR coronary OR
myocardial OR ... etc.", as there are over 1000
types of heart disorder listed in the Read Codes
for example. Read Codes hierarchy of concepts
allows all sorts of research questions such as
"list all my patients who have eczema, and list
these according to the type of eczema that they
have." The possibilities are endless...
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Controlled Clinical Terminology Desirable
Features (Cimino)

• Concept based
• Completeness (the compositional feature of a
  terminology ensures completeness)
• Synonymy (in this way the terminology is less
  restrictive and richer all synonyms of a concept
  point to it and are semantically associated with
  it)
• Hierarchical
• Multiple classification and multiple parentage
• Compositional
• Semantic definition of concepts
• Mapped to classifications (e.g., Read -gt ICD10
  can be one-to-one or one-to-many maps usually
  some detail is lost as classifications have
  coarser granularity compared to terminologies)
• Language-independent model

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No Ambiguity or Redundancy

• No duplicate concepts are allowed, i.e., cannot
  allow two different ways of coding the same thing
  or concept, e.g., "Heart attack" and "Myocardial
  infarction" cannot be considered two different
  concepts and given two concept codes they are
  just synonyms.
• "Paget disease" cannot be a concepts preferred
  term or label, because it is ambiguous it can
  point to "Paget disease of the breast" as well as
  "Paget disease of bone".
• Each concept has one unambiguous preferred term
  and any number of synonyms. Synonyms may be
  shared with other concepts, e.g., "Ventricle" is
  a synonym (but cannot be the preferred term) of
  both "Cardiac ventricle" and "Brain ventricle".

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Concept and Term Codes
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Concept and Term Codes

• "Plaque psoriasis" (concept label) has a Read
  concept code M1614 while the codes (TermIds) of
  the preferred term Plaque psoriasis (again) is
  Y50HZ and synonymous terms "Discoid psoriasis"
  Y50Ha and "Nummular psoriasis" Y50Hc,
  i.e., four codes for this example one concept
  code and three TermIds.

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Arranging Concepts

• Concepts can be arranged orthographically (by
  spelling, i.e., A to Z), like a dictionary (e.g.,
  Apple, Dog, Orange, Zebra). However, arranging
  concepts semantically (by meaning) like a
  thesaurus is much more useful (e.g., Fruits
  Apple, Orange, Animal Dog, Zebra).

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Directed Acyclic Graph (DAG)

• DAG allows multiple parentage and allows concepts
  to be moved and reclassified as medical knowledge
  changes (cf. rigid code-dependent hierarchy of
  ICD). With DAG, unlimited hierarchy depths can be
  reached (cf. Only four levels in ICD), but all
  these features of DAG come on the expense of
  increased complexity for implementers.

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Directed Acyclic Graph (DAG)
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Enumerative Vs Compositional Terminologies

• Enumerative (pre-coordinated) terminologies,
  where every possible concept is listed
  explicitly, result in compositional explosion and
  you can never be sure that you have listed all
  the possibilities.
• A compositional (post-coordinated) terminology on
  the other hand, like Read CTV3, seeks to
  construct concepts from primitive building
  blocks, governed by validation rules.
• OAV (Object-Attribute-Value) triples constitute
  the description logic scheme used in Read CTV3,
  and help achieving semantic definition of
  concepts.
• In SNOMED (Systematised Nomenclature of Human and
  Veterinary Medicine - College of American
  Pathologists), the description logic is KRSS
  (Knowledge Representation System Specification)
  while in GALEN it is GRAIL.
• N.B. Read Codes are due to be merged with SNOMED
  by 2002, to create a new worldwide standard
  clinical coding scheme. This will be called
  SNOMED Clinical Terms (SNOMED-CT).

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Enumerative Vs Compositional Terminologies
21
Description Logics

• Description logics (DLs) lie at the heart of any
  clinical terminology. DLs are languages that
  allow reasoning about information, in particular
  supporting the classification of descriptions by
  working out how concepts and their instances
  relate to one another based on their roles. They
  can thus infer knowledge implied by an ontology.

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Terminology Servers

• Medical terminologies are foundational ontologies
  used by many applications, and hence they should
  not be embedded in client applications, but
  should be shared and reused as distributed
  resources by implementing them as services
  through terminology servers.
• A terminology server is a special type of
  ontology servers that allows retrieval of related
  concepts (parent, child, sibling, cousin and
  uncle concepts) and synonyms, and querying and
  cross-mapping multiple terminologies/
  classifications at the same time. Ideally, it
  should also support concept mapping, which
  involves processing free text queries to identify
  corresponding terms from a controlled vocabulary
  this relieves users from any restrictions while
  ensuring accurate results (contextual relevancy)
  and can also support multiple languages.

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Terminology Servers (Contd)

• Chute et al mention the following desiderata for
  a clinical terminology server word
  normalisation, word completion, target
  terminology specification, spelling correction,
  lexical matching, term completion, semantic
  locality, term composition and decomposition.
• Examples of terminology servers include Saphire
  International (http//www.ohsu.edu/cliniweb/saphin
  t/) and jTerm (http//www.jterm.org), a
  Java-based open source terminology server.

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Classifications

• A classification is a system of categories to
  which entities are assigned according to some
  established criteria, e.g., anatomy, disease
  process or pathology, aetiology, clinical (like
  obstetrics), or a combination of these.
  Categories are limited in number, all
  encompassing and stable over time. Common and
  important entities are assigned to specific
  categories, while uncommon and less significant
  entities are included within other categories.

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Classifications
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Entities rules of engagement in ICD

• Index, e.g., 443.1 Buergers disease (ICD9)
• Inclusions (when an entity is less significant),
  e.g., 443.6 Other (incl. Acrocyanosis, Diabetic
  peripheral angiopathy) excl. Chilblains,
  Frostbite, Immersion foot (ICD9)
• Exclusions (see above example tells you not to
  count an entity under this code, as it is listed
  elsewhere within the classification with another
  code)
• Otherwise specified categories (OS) include
  other specific but less significant entities
• Unspecified (NOS Not Otherwise Specified),
  e.g., 443.9 Unspecified (incl. Intermittent
  claudication, Spasm of artery) excl. Spasm of
  cerebral artery (435) (ICD9)
• Extensions (5th digit), e.g., to differentiate
  between closed and open fracture neck of femur as
  an open fracture is much more liable to infection
  and complications.
• Dagger asterisk, e.g., Cause 265.0 Beriberi
  Effect 425.7 Nutritional cardiomyopathies

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Characteristics of a Classification

• All concepts can find a single place in a
  suitable classification (i.e., all-inclusive,
  mutually exclusive). A single concept cannot be
  classified under two different headings (i.e., no
  multiple parentage) this prevents double
  counting of a condition, which is essential for
  reliable statistics and central returns
  (remember statistics are the main raison dêtre
  of classifications).
• In classifications, you loose detail (related
  concepts are aggregated and counted together no
  distinction between them is made on the code and
  statistics levels).

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Characteristics of a Classification

• Classifications become less accurate with time,
  and will eventually need revision at some stage,
  e.g., when new diseases are discovered (where to
  put these diseases, and if we put them under
  existing categories the meaning of these
  categories will drift with time making
  comparisons with previous years statistics done
  using the same classification less accurate and
  reliable). Updating a classification also implies
  preparing an equivalence-mapping table (to
  compare statistics done using different versions
  of the classification).
• In addition to ICD (for primary diagnosis),
  OPCS-4 (a surgical operations and procedure
  classification) is used in the UK.

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The Pyramid
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Mapping and Grouping Tools
CamsCoder is a good example of a mapping tool
that translates Read CTV3 terms into ICD-10 and
OPCS-4 terms and codes.
CamsCoder presents us with a review screen
showing the diagnostic and operative procedures
entered.
CamsCoder allows the entered statements (which
may contain multiple terms and codes) to be
re-ordered/deleted using these buttons.
The coder can set the episode coding to be
complete when they are happy with it.
The episodes HRG is displayed here.
CamsCoder automatically validates the information
presented. If there are any problems, a message
is displayed here explaining what the coder needs
to do.
If the coding was invalid clicking on the
Action button would take the coder through the
process of making the episode valid.
The coder now clicks on OK to finish this coded
episode.
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The LOINC Codes

• The LOINC database provides a set of universal
  names and ID codes for identifying laboratory and
  clinical observations. The goal of LOINC is to
  facilitate the exchange and pooling of clinical
  laboratory results, such as blood haemoglobin or
  serum potassium, for clinical care, outcomes
  management, and research.
• Currently, many laboratories are using HL7 or
  similar standards, to send laboratory results
  electronically from producer laboratories to
  clinical care systems in hospitals. Most
  laboratories identify tests in these messages by
  means of their internal (and idiosyncratic) code
  values, so the receiving systems cannot fully
  "understand" the results they receive unless they
  either adopt the producer's laboratory codes
  (which is impossible if they receive results from
  multiple source laboratories), or invest in work
  to map each laboratory's code system to the
  receiver's internal code system.

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The LOINC Codes
You may download and use to LOINC database
browser free of charge from http//www.regenstrief
.org/loinc/loinc.htm
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The LOINC Codes

• If laboratories all used the LOINC codes to
  identify their results in data transmissions,
  this problem would disappear. The receiving
  system with LOINC codes in its master vocabulary
  file would be able to understand and properly
  file HL7 results messages that also use the LOINC
  code. Similarly, government agencies would be
  able to pool and analyse results for tests from
  many sites if they were reported electronically
  using the LOINC codes.

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2001 MeSH (Medical Subject Headings)

• MeSH was originally developed by United States
  National Library of Medicine (NLM) to index the
  world medical literature in MEDLINE (MeSH
  provides bibliographic headings for indexing)
  the latest MeSH version is 2001 MeSH. MeSH also
  forms an essential part of the NLMs Unified
  Medical Language System (UMLS).
• MeSH qualifiers or subheadings are used to better
  define a topic, narrow retrieval, or express a
  certain aspect of a main heading.
• It should be noted that MeSH is not an efficient
  indexing language for tasks such as classifying
  episodes of patient care. The more efficient
  clinical coding systems (e.g., Read
  Codes/Clinical Terms Version 3) are more suited
  to coding the Electronic Patient Record.

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2001 MeSH (Medical Subject Headings)
MeSH Descriptor Data for Psoriasis, a skin
disease.
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2001 MeSH Tree Structures

• MeSH hierarchy allows broader (parents or
  ancestors and siblings) and narrower (children or
  successors) concept relationships. Moreover,
  within this hierarchy, a single concept may
  appear as narrower concepts of more than one
  broader concept, e.g., "Psoriatic Arthritis"
  appears under both "Joint Diseases" and "Skin
  Diseases.

cf. ICD remember each coding language or scheme
is most suited to particular purpose(s).
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2001 MeSH Tree Structures
http//www.nlm.nih.gov/mesh/MBrowser.html
38
UMLS (Unified Medical Language System)

• The UMLS project (http//umls.nlm.nih.gov/) is a
  long-term research and development project at the
  United States' National Library of Medicine (NLM)
  whose goal is to help health professionals and
  researchers to intelligently retrieve and
  integrate information from a wide range of
  disparate electronic biomedical information
  sources. It can be used to overcome variations in
  the way similar concepts are expressed in
  different sources. This makes it easier for users
  to link information from patient record systems,
  bibliographic databases, factual databases,
  expert systems, etc. The UMLS Knowledge Services
  can also assist in data creation and indexing
  applications.

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UMLS (Unified Medical Language System)

• The UMLS includes machine-readable "Knowledge
  Sources" that can be used by a wide variety of
  applications programs to compensate for
  differences in the way concepts are expressed in
  different machine-readable sources and by
  different users, to identify the information
  sources most relevant to a user inquiry.
• The Metathesaurus contains mappings to MeSH,
  ICD-9-CM, SNOMED, CPT, and a number of other
  coding systems.

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UMLS (Unified Medical Language System)

• The UMLS is not itself a standard it is a
  cross-referenced collection of standards and
  other data and knowledge sources. It is a very
  valuable resource for solving the most difficult
  problem in exchanging healthcare information the
  multiplicity of coding systems in use today.
• One on-line use of the UMLS is the Medical World
  Search site (http//www.mwsearch.com/). When a
  user searches the Web for a medical concept,
  Medical World Search uses UMLS to include
  synonyms in the query.

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The UMLS Project and its Components

• The project is directed by a multidisciplinary
  team, including clinicians, computer and
  information scientists, and linguists, and
  involves collaboration with many medical
  informatics research groups. The project work has
  resulted in a set of knowledge sources and
  accompanying programs that are updated and
  distributed regularly on CD-ROM. Online access to
  the UMLS knowledge sources is provided through
  the Internet-based UMLS Knowledge Source Server,
  which includes an application programming
  interface (API) and a World Wide Web interface.
  The Web site requires registration
  (http//umlsks.nlm.nih.gov/).

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UMLS Metathesaurus

• The Metathesaurus contains information about
  biomedical concepts and terms from a large number
  of controlled terminologies and thesauri. The
  Metathesaurus preserves the information encoded
  in the source vocabularies, such as the
  hierarchical contexts of the terms, their
  meanings and other attributes. The Metathesaurus
  is organised by concepts, which means that
  alternate names (synonyms, lexical variants, and
  translations) for the same meaning are all linked
  together as one concept. The Metathesaurus adds
  information to the concepts, including semantic
  types, definitions, and inter-concept
  relationships.

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UMLS Metathesaurus (Contd)

• The Metathesaurus contains hundreds of thousands
  of concepts from a broad range of vocabularies.
  These include, for example, all or portions of
  the following terminologies
• the Systematised Nomenclature of Medicine (SNOMED
  International),
• the Read Thesaurus,
• the International Classification of Diseases -
  Clinical Modification (ICD9-CM),
• the Universal Medical Device Nomenclature System,
• the WHO Adverse Drug Reaction Terminology,
• the Classification of Nursing Diagnoses (NANDA),
• the Home Health Care Classification of Nursing
  Diagnoses and Interventions,
• the Physicians' Current Procedural Terminology
  (CPT),
• the Medical Subject Headings (MeSH),
• the Diagnostic and Statistical Manual of Mental
  Disorders (DSMIV), and
• the Thesaurus of Psychological Index Terms.
• In addition, translations of some of the
  terminologies into languages other than English
  are included.

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UMLS Semantic Network

• The Semantic Network, through its high-level
  semantic types, or categories, provides a
  consistent categorisation of all concepts
  represented in the Metathesaurus. The links
  between the semantic types provide the structure
  for the Network and represent important
  relationships in the biomedical domain. There are
  semantic types for organisms, anatomical
  structures, biologic function, chemicals, events,
  physical objects, and concepts or ideas. The
  primary relationship is the "is_a" link, and
  there are five major categories of additional
  relationships physical, spatial, temporal,
  functional, and conceptual relationships.

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UMLS (Unified Medical Language System)
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UMLS (Unified Medical Language System)
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UMLS (Unified Medical Language System)
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Software Implementations You Can Experiment With

• Agoras Web-based READ 3.1 browser that you can
  play with http//www.agora.co.uk1080/read/gp.htm

Lightweight browser and search engine for the
Read Codes clinical thesaurus by Agora.
51
Software Implementations You Can Experiment With

• CLUE (CIC Look Up Engine from The Clinical
  Information Consultancy, UK) is a freeware
  clinical coding solution that helps you add NHS
  Clinical Terms Version 3 capabilities to clinical
  applications in hours rather than months (you may
  use Visual Basic for example to access CLUE's
  API). CLUE also offers a ready-to-use Read Codes
  browser. You may download the full CLUE package
  free of charge, but beware that a terminology
  like Read CTV3 with more than 200,000 concepts,
  nearly 300,000 terms and over a million access
  keys is not a small download, so be prepared for
  this one (over 20MB).http//www.clinical-info.co.
  uk/ClueDownload.htm

52
Software Implementations You Can Experiment With

• CLUE (CIC Read CTV3 Look Up Engine from The
  Clinical Information Consultancy, UK)

53
Software Implementations You Can Experiment With
ICD9 CodeFinder lets you search and browse ICD9
categories and codes. You may download CodeFinder
free of charge (298 KB).http//www.winsite.com/in
fo/pc/win95/misc/cfind20.zip/
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Software Implementations You Can Experiment With

• e-MDs Online ICD-9 Search http//www.e-mds.com/ic
  d9/index.html

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Recommended Web Links and Papers

• Bechhofer SK, Goble CA, Rector AL, Solomon WD,
  and Nowlan WA. Terminologies and Terminology
  Servers for Information Environments. In
  Proceedings of STEP '97 Software Technology and
  Engineering Practice, 1997. URI
  http//citeseer.nj.nec.com/354766.html
• Chute CG, Elkin PL, Sheretz DD and Tuttle MS.
  Desiderata for a Clinical Terminology Server. In
  Proceedings of AMIA'99 Annual Symposium, 1999.
  URI http//www.amia.org/pubs/symposia/D005782.PDF
  
• Rector AL. Clinical Terminology Why Is it so
  Hard? Methods Inf Med. 199938(4-5)239-52
• The British Association of Clinical Terminology
  Specialists http//www.bacts.org.uk/
• OpenGALEN http//www.opengalen.org/
• Read Codes Engines http//www.cams.co.uk/ and
  http//www.visualread.com
• See also Related Web Links section
  athttps//wwws.soi.city.ac.uk/intranet/students/
  courses/mim/mi/lect2_2.htm