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PubMed1993

PALM--a pattern language for molecular biology.

This paper presents a new pattern language, PALM, for describing patterns in molecular biology sequences. The language is intended for representing knowledge about such patterns in a declarative, clear and concise way. It is also shown that its expressive power enables the definition of any regular or context free language, and also higher languages in the Chomsky hierarchy by parameter attachment, variables and procedural attachment. It is also possible to define approximate patterns. The language is rigorously defined, and several examples of its use and expressive power are given.

DNA-Directed DNA Polymerase↗

PubMed1994

A model for structured data entry based on explicit descriptional knowledge.

Clinical narratives in patient records are usually recorded in free text, limiting the use of this information for research, quality assessment, and decision support. This study focuses on the capture of clinical narratives in a structured format by supporting physicans with structured data entry (SDE). We analyzed and made explicit which requirements SDE should meet to be acceptable for the physician on the one hand, and generate unambiguous patient data on the other. Starting from these requirements, we found that in order to support SDE, the knowledge on which it is based needs to be made explicit: we refer to this knowledge as descriptional knowledge. We articulate the nature of this knowledge, and propose a model in which it can be formally represented. The model allows the construction of specific knowledge bases, each representing the knowledge needed to support SDE within a circumscribed domain. Data entry is made possible through a general entry program, of which the behavior is determined by a combination of user input and the content of the applicable domain knowledge base. We clarify how descriptional knowledge is represented, modeled, and used for data entry to achieve SDE, which meets the proposed requirements.

Artificial Intelligence↗

PubMed1994

The component-based architecture of the HELIOS medical software engineering environment.

The constitution of highly integrated health information networks and the growth of multimedia technologies raise new challenges for the development of medical applications. We describe in this paper the general architecture of the HELIOS medical software engineering environment devoted to the development and maintenance of multimedia distributed medical applications. HELIOS is made of a set of software components, federated by a communication channel called the HELIOS Unification Bus. The HELIOS kernel includes three main components, the Analysis-Design and Environment, the Object Information System and the Interface Manager. HELIOS services consist in a collection of toolkits providing the necessary facilities to medical application developers. They include Image Related services, a Natural Language Processor, a Decision Support System and Connection services. The project gives special attention to both object-oriented approaches and software re-usability that are considered crucial steps towards the development of more reliable, coherent and integrated applications.

Computer Communication Networks↗

PubMed1994

Analysing and developing object-oriented medical applications with HELIOS.

The HELIOS project promotes systematic reuse of existing software in a valuable methodological context. In order to reach this goal, the Analysis and Design Development Environment (ADDE) has been realized as a HELIOS component. This component includes the Analysis and Design sub-component (ADT), which supports the Rumbaugh's object-oriented methodology and the Insertion Retrieval Tool (IRT), which implements the reuse. The ADT sub-component enhances the quality of software development permitting a correct analysis, and design and a satisfactory documentation. The IRT Tool is dedicated to reuse by retrieving parts of existing applications (retrieve) and by qualifying elements just created or updated (insertion). A faceted system adapted to the medical domain allows an efficient search among the object database. Both tools contribute to reducing the cost of software development. This paper presents the design and the implementation of these tools in the HELIOS framework.

Database Management Systems↗

PubMed1994

Lexical methods for managing variation in biomedical terminologies.

Access to biomedical terminologies is hampered by the high degree of variability inherent in natural language terms and in the terminologies themselves. The lexicon, lexical programs, databases, and indexes included with the 1994 release of the UMLS Knowledge Sources are designed to help users manage this variability. We describe these resources and illustrate their flexibility and usefulness in providing enhanced access to data in the UMLS Metathesaurus.

Biology↗

PubMed1994

A natural language understanding system combining syntactic and semantic techniques.

A large proportion of the medical record currently available in computerized medical information systems is in the form of free text reports. While the accessibility of this source of data is improved through inclusion in the computerized record, it remains unavailable for automated decision support, medical research, and management of medical delivery systems. Natural language understanding systems (NLUS) designed to encode free text reports represent one approach to making this information available for these uses. Below we describe an experimental NLUS designed to parse the reports of chest radiographs and store the clinical data extracted in a medical data base.

Bayes Theorem↗

PubMed1994

Generating patient-specific interactive natural language explanations.

Patient compliance is a significant problem and is strongly correlated with the patients' understanding of their condition and prescribed treatment. Since doctors typically do not have large amounts of time to educate patients, and impersonal, voluminous patient handouts are largely ineffective, we propose the use of a sophisticated computer-based information system to generate tailored, interactive handouts to communicate with patients. Our system uses text planning and user modeling techniques to generate natural language descriptions of migraine, its symptoms, triggering factors and prescriptions. The system is capable of handling follow-up questions requesting further information, and generating responses in the context of previously supplied information--a capability unavailable in previous patient information systems. The system tailors its interaction to: (i) the class of migraine patients, (ii) the individual patient, and (iii) the previous dialogue. Preliminary evaluation of the system indicates that patients find it useful and informative. More extensive evaluation is in progress.

Computer-Assisted Instruction↗

PubMed1993

Computer auditing of surgical operative reports written in English.

We developed a script-based scheme for automated auditing of natural language surgical operative reports. Suitable operations (appendectomy and breast biopsy) were selected, then audit criteria and operation scripts conforming with our audit criteria were developed. Our LISP parser was context and expectation sensitive. Parsed sentences were represented by semigraph structures and placed in a textual database to improve efficiency. Sentence ambiguities were resolved by matching the narrative textual database to the script textual database and employing the Uniform Medical Language System (UMLS) Knowledge Sources. All audit criteria questions were successfully answered for typical operative reports by matching parsed audit questions to the textual database.

Appendectomy↗

PubMed1993

Modelling for natural language understanding.

Natural Language Understanding (NLU) is a rapidly growing field in medical informatics. Its potential for tomorrow's applications is important. However, it is limited by its ability to ground its components on a solid model of the domain. This opens the way for the emergence of the discipline of medical domain modelling, as part of the vast field of Knowledge Base (KB) engineering. This article aims at describing the current development of a multilingual natural language system, strongly oriented towards the semantics of the domain. Special emphasis is presently given to the task of building a domain model, and to establish direct links with the language platform. The result is a model-driven NLU system. Numerous benefits are expected in the long term.

Artificial Intelligence↗

PubMed1993

Interpreting natural language queries using the UMLS.

This paper describes AQUA (A QUery Analyzer), the natural language front end of a prototype information retrieval system. AQUA translates a user's natural language query into a representation in the Conceptual Graph formalism. The graph is then used by subsequent components to search various resources such as databases of the medical literature. The focus of the parsing method is on semantics rather than syntax, with semantic restrictions being provided by the UMLS Semantic Net. The intent of the approach is to provide a method that can be emulated easily in applications that require simple natural language interfaces.

Humans↗

PubMed1993

Clinical, scholarly & campus information hypertext tools at Columbia-Presbyterian Medical Center.

In conjunction with other researchers at Columbia-Presbyterian Medical Center (CPMC) we have developed a number of hypertext and free text retrieval computer applications aimed at an extremely diverse audience which includes students and faculty in a university setting as well as health care providers and patients in hospital and clinic settings. Hypertext and free text systems offer features which make them ideal for presenting information in a wide variety of contexts; however, they also have several major weaknesses which must be addressed before these applications can be useful tools. We have learned to maximize the strengths and minimize the weaknesses to present material in a manner that is individualized to the needs of each user from the research scientist in the lab to the patient at the bedside.

Academic Medical Centers↗

PubMed1993

Generating MEDLINE search strategies using a librarian knowledge-based system.

We describe a librarian knowledge-based system that generates a search strategy from a query representation based on a user's information need. Together with the natural language parser AQUA, the system functions as a human/computer interface, which translates a user query from free text into a BRS Onsite search formulation, for searching the MEDLINE bibliographic database. In the system, conceptual graphs are used to represent the user's information need. The UMLS Metathesaurus and Semantic Net are used as the key knowledge sources in building the knowledge base.

Artificial Intelligence↗

PubMed1993

Words or concepts: the features of indexing units and their optimal use in information retrieval.

Words or Concepts, which are a better choice for indexing the contents of documents? The answer depends on what method is used for retrieval. This paper studies the effects of using canonical concepts versus document words in different retrieval systems with a testing collection of MEDLINE documents. In our tests, for a retrieval system which does not use any human knowledge, using words yielded better retrieval results, while using concepts suffered from a vocabulary difference between canonical expressions of concepts and non-canonical words in queries or documents. For a system which depends on the UMLS synonym set for a mapping from queries or documents to canonical concepts, the retrieval results were slightly better than the case of not using the synonyms, but still worse than the systems using words. For the systems which automatically "learn" empirical connections between words and concepts from examples in the testing collection, the vocabulary problem was effectively solved, and the results of using concepts were competitive or better, compared to those using words.

Abstracting and Indexing↗

PubMed1993

Semantic analysis of medical records.

A program (LogStory) is described that was developed for the automatic semantic analysis of clinical narratives, stored in a computerized problem-oriented medical record (PROMED). The diagnoses were written in a free-text format during consultation, and later collected into diagnostic classes, e.g., diseases. A lexical parser automatically created dictionaries from the clinical narrative associated with each disease. Automatic (fuzzy) set operations were performed on the words associated with each class. The manifestations of 16 diseases were automatically extracted by pairwise operations on the word sets. The correlation between diseases and corresponding signs, symptoms and treatment was highly significant (p < 0.001). Applying the difference operation on diseases with disjunct sets of clinical findings allowed the recovery of disease-specific knowledge. The evolution of a disease was accounted for, and the system was able to generalize its findings. The PROMED-LogStory concept enables the processing of natural language and may be a powerful tool for knowledge acquisition and clinical research.

Expert Systems↗

PubMed1995

Architectural requirements for a multipurpose natural language processor in the clinical environment.

A considerable amount of research has been concerned with the development of natural language systems to automate the encoding of clinical information that occurs in textual form. The task is very complex, and not many language processors are used routinely within clinical information systems. Those systems that are operational, have been implemented in narrow domains for particular applications. For a system to be truly useful, it should be designed so that it could be widely used within the clinical environment. This paper examines architectural requirements we have identified as being necessary for portability and describes the architecture of the system we developed. Our system was designed so that it could be used in different domains to serve a variety of applications. It has been integrated with the clinical information system at Columbia-Presbyterian Medical Center where it routinely encodes clinical information from radiological reports of patients.

Computer Systems↗

PubMed1995

A multi-lingual architecture for building a normalised conceptual representation from medical language.

The overall goal of MENELAS is to provide better access to the information contained in natural language patient discharge summaries (PDSs), through the design and implementation of a prototype able to analyse medical texts. The approach taken by MENELAS is based on the following key principles: (i) to maximise the usefulness of natural language analysis and the usability of its results, the output of natural language analysis must be a normalised conceptual representation of medical information; and (ii) to maximise the reuse of resources, language analysis should be domain-independent and conceptual representation should be language-independent. This paper discusses the results obtained and the issues raised when implementing these principles during the project.

Artificial Intelligence↗

PubMed1995

Multilingual natural language generation as part of a medical terminology server.

Re-usable and sharable, and therefore language-independent concept models are of increasing importance in the medical domain. The GALEN project (Generalized Architecture for Languages Encyclopedias and Nomenclatures in Medicine) aims at developing language-independent concept representation systems as the foundations for the next generation of multilingual coding systems. For use within clinical applications, the content of the model has to be mapped to natural language. A so-called Multilingual Information Module (MM) establishes the link between the language-independent concept model and different natural languages. This text generation software must be versatile enough to cope at the same time with different languages and with different parts of a compositional model. It has to meet, on the one hand, the properties of the language as used in the medical domain and, on the other hand, the specific characteristics of the underlying model and its representation formalism. We propose a semantic-oriented approach to natural language generation that is based on linguistic annotations to a concept model. This approach is realized as an integral part of a Terminology Server, built around the concept model and offering different terminological services for clinical applications.

Language↗

PubMed1995

Arden syntax and GALEN terminology support: a powerful combination to represent medical knowledge.

Arden syntax is an ASTM standard defined a few years ago to represent rule-based knowledge in the medical domain in an application- and implementation-independent way. Knowledge is expressed as Medical Logic Modules (MLMs). So far, it has mainly been used for providing critique and reminders in data driven, medical decision support systems, where typically rules are evoked by certain database operations (e.g., "store systolic blood pressure"). The syntax is an important step towards a general medical knowledge representation, with version control, maintenance information, arithmetic operations, logic rule syntax, and temporal operations, but it is not yet complete. The standard does not define how medical concepts (e.g., "systolic blood pressure") are to be referenced. Existing coding schemes such as SNOMED and ICD10 may be used for this, but they have generally failed to provide the required level of detail. The use of Arden syntax to transfer knowledge between centers in the form of MLMs has been shown to be non-trivial [1,2]. GALEN (Generalized Architecture for Languages, Encyclopedias, and Nomenclatures in Medicine) is an EC AIM project aiming at a machine-readable system to represent medical concepts in a semantic network. The network allows for the combination and specialization of concepts to the required level of detail. The project has resulted in a notation (GRAIL: GALEN Representation and Integration Language) and several tools, such as browsers for manipulating medical concepts. In a GALEN subproject, we are exploring the potential utility of combining Arden syntax with semantics from the GALEN terminology, and we are developing tools to facilitate the creation of MLMs with terminology support. Our impression is that the combination is very useful: GALEN tools allow a domain expert to express her knowledge in purely medical terms, independent of an actual or intended patient database structure and data representation. They can also help a knowledge engineer or quite possibly an automatic interpreter to translate a well defined medical concept (expressed in GRAIL) to actual database queries (in SQL or other notation). This translation can be facilitated by a Data Dictionary describing the local patient database.

Expert Systems↗