Faculty Bibliography

OBJECTIVE:Development of a general natural-language processor that identifies clinical information in narrative reports and maps that information into a structured representation containing clinical terms. DESIGN/METHODS:The natural-language processor provides three phases of processing, all of which are driven by different knowledge sources. The first phase performs the parsing. It identifies the structure of the text through use of a grammar that defines semantic patterns and a target form. The second phase, regularization, standardizes the terms in the initial target structure via a compositional mapping of multi-word phrases. The third phase, encoding, maps the terms to a controlled vocabulary. Radiology is the test domain for the processor and the target structure is a formal model for representing clinical information in that domain. MEASUREMENTS/METHODS:The impression sections of 230 radiology reports were encoded by the processor. Results of an automated query of the resultant database for the occurrences of four diseases were compared with the analysis of a panel of three physicians to determine recall and precision. RESULTS:Without training specific to the four diseases, recall and precision of the system (combined effect of the processor and query generator) were 70% and 87%. Training of the query component increased recall to 85% without changing precision.

The Integrated Academic Information Management System (IAIMS) concept is about sharing resources and information, and about improving the decision-making ability of health care professionals by integrating information. At Columbia-Presbyterian Medical Center, the IAIMS project has established an information architecture based on common, shared computing and networking resources. The institutional computing culture has been changed with increased sharing of information and, consequently, improved quality of information. Several classes of information in the areas of clinical, scholarly, administrative, basic research, and core resources have been identified for better understanding of information responsibility. Technical problems such as heterogeneity on workstation platforms and lack of universal syntactic and semantic standards for health care information exchange still impede inter-institutional sharing of information.

The Columbia Clinical Repository is the foundation of the Clinical Information System at the Columbia Presbyterian Medical Center (CPMC). The Repository is implemented as a relational database on an IBM mainframe, using a generic design that employs a small number of tables. Client applications on remote platforms send and receive data through Database Access Modules (DAMs), which support the HL7 protocol, while applications on the mainframe manipulate data through DAMs supporting a locally defined "query template". Implementation using static (compiled) SQL is compared to dynamic (ad hoc) SQL in terms of efficiency and flexibility.

The overall design of the CIS at CPMC is heavily influenced by the decision support component. The type of automated decision support being implemented dictates the need for highly structured or coded data. The value of decision support systems has been well documented. The current reliance on free-text documents is natural and a rewarding first step to a more valuable mix of coded and free text. While the health care provider might find the textual comments of the various reports extremely useful, the capability of an automated system to vigilantly review every data element for trends and anomalies is becoming invaluable in today's ever more complex health care delivery environment. Other approaches such as optical imaging systems would facilitate human decision support, but do not supply data in a format that can be processed by automated decision support systems. The developers of the CIS at CPMC believe that data are most valuable when available for both human and automated decision support.

This paper describes a model for automated information retrieval in which questions posed by clinical users are analyzed to establish common syntactic and semantic patterns. The patterns are used to develop a set of general-purpose questions called generic queries. These generic queries are used in responding to specific clinical information needs. Users select generic queries in one of two ways. The user may type in questions, which are then analyzed, using natural language processing techniques, to identify the most relevant generic query; or the user may indicate patient data of interest and then pick one of several potentially relevant questions. Once the query and medical concepts have been determined, an information source is selected automatically, a retrieval strategy is composed and executed, and the results are sorted and filtered for presentation to the user. This work makes extensive use of the National Library of Medicine's Unified Medical Language System (UMLS): medical concepts are derived from the Metathesaurus, medical queries are based on semantic relations drawn from the UMLS Semantic Network, and automated source selection makes use of the Information Sources Map. The paper describes research currently under way to implement this model and reports on experience and results to date.

One purpose of the Unified Medical Language System (UMLS) is to facilitate conversion of terms from one controlled medical vocabulary to another. We examined our ability to convert International Classification of Diseases, 9th Edition, Clinical Modifications (ICD9-CM) to Medical Subject Headings (MeSH) using the UMLS. We describe a method which mapped 30.4% of ICD9-CM to UMLS. Of these, 95.0% were linked to MeSH, of which translation was straightforward in 90.4%. We discuss the use of these translations for retrieval from MeSH-indexed databases, such as Medline.

Linking a knowledge-based system (KBS) to a clinical database is a difficult task, but critical if such systems are to achieve widespread use. The Columbia-Presbyterian Medical Center's clinical event monitor provides alerts, interpretations, research screening, and quality assurance functions for the center. Its knowledge base consists of Arden Syntax Medical Logic Modules (MLMs). The knowledge base was analyzed in order to quantify the use and impact of KBS-database links. The MLM data slot, which contains the definition of these links, had almost as many statements (5.8 vs. 8.8, ns with p = 0.15) and more tokens (122 vs. 76, p = 0.037) than the logic slot, which contains the actual medical knowledge. The data slot underwent about twice as many modifications over time as the logic slot (3.0 vs. 1.6 modifications/version, p = 0.010). Database queries and updates accounted for 97.2% of the MLM's total elapsed execution time. Thus, KBS-database links consume substantial resources in an MLM knowledge base, in terms of coding, maintenance, and performance.

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.

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.

Over the past seven years, Columbia-Presbyterian Medical Center has been planning and implementing an integrated academic information management system. Accomplishments to date include establishing an institutional information architecture, installing a campus-wide network of workstations, recruiting the staff needed to develop and implement the system, and developing various applications. This paper presents the rationale and steps involved in these accomplishments, as well as data on use of the system so far.