Biomedical subjects

J Starren

Publications and source records attributed to J Starren.

7 recordsLinked to original sources

PubMed1997

Practical lessons in remote connectivity.

Community Health Information Networks (CHINs) require the ability to provide computer network connections to many remote sites. During the implementation of the Washington Heights and Inwood Community Health Management Information System (WHICHIS) at the Columbia-Presbyterian Medical Center (CPMC), a number of remote connectivity issues have been encountered. Both technical and non-technical issues were significant during the installation. We developed a work-flow model for this process which may be helpful to any health care institution attempting to provide seamless remote connectivity. This model is presented and implementation lessons are discussed.

Academic Medical Centers↗

PubMed1997

Expressiveness of the Breast Imaging Reporting and Database System (BI-RADS).

The Breast Imaging Reporting and Database System (BI-RADS) was developed by the American College of Radiology and is used by a number of computerized mammography tracking systems. The ability of BI-RADS to encode the data contained in 300 mammography reports at the Columbia-Presbyterian Medical Center was examined. BI-RADS was able to encode normal reports and "special masses" (such as lymph nodes) without difficulty. However, none of the general masses and only 17% of the calcifications could be encoded in BI-RADS. The implications of this for the design of mammography databases are discussed.

Databases, Factual↗

PubMed1996

Notations for high efficiency data presentation in mammography.

As a result of improvements in Medical Language Processing, the availability of categorical information (such as diagnoses or radiology findings) is increasing rapidly. This increased availability has created a need for more efficient methods for computer presentation. One method for developing such presentations would be to adapt the hand-written notation systems already used in paper-based records. We have characterized one such notation system, the Mammography Notation Sublanguage(MNS). The MNS is a true medical sublanguage, with a definable lexicon and syntax. Compared with text reports, it represents a 37-fold size compression. A single "base", sublanguage pattern is identified for possible computer presentation of mammography findings. The issues involved in using such sublanguages for data presentation are discussed.

Language↗

PubMed1995

Using intermediate states to improve the ability of the Arden Syntax to implement care plans and reuse knowledge.

The Arden Syntax is one of a few knowledge representation languages currently in use for clinical decision support. While some of these languages are being used in active patient care settings, none have gained widespread acceptance as a clinical tool. Prior attempts to represent temporally complex care plans in the Arden Syntax have revealed difficulties in representing and tracking series of consecutive time-oriented events and recommendations, in sharing and reusing knowledge and in dealing with unobtainable data. In an attempt to improve Arden's ability to deal with these problems and demonstrate the importance of these factors, the clinical event monitor has been adapted to store coded data representing Intermediate States in the Columbia Presbyterian Medical Center (CPMC) central data repository. The Intermediate States define the current state of the patient as laid out in the care plan. Four care plans were constructed. The findings include an improved ability to track complex series of events and recommendations over long periods of time. The knowledge generated by the electronic care plans was able to be reused by the care plan that generated it, by other elements of the knowledge base and by non-decision support applications. Modular development, facilitated by the changes, simplified dealing with data not available to the central data repository by aiding the implementation of those parts of the care plan for which sufficient data is available.

Artificial Intelligence↗

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

Description generation of abnormal densities found in radiographs.

In this paper we present a system for describing renal stones found in radiographs. The system generates descriptions that adhere to those generated by radiologists. The descriptions are formulated by discovering the spatial relationships that exist between the major organs and the renal stones. The system consists of three major components. The first is the image processing component which is responsible for locating the stone. The second component is the inference network minimization component which determines which spatial relationships, of all those that exist between the stone and the organs, is the most descriptive. The third component is the natural language generation component which is responsible for translating the spatial relationships into appropriate medical terminology. We will illustrate all these components on several examples.

Algorithms↗

PubMed1994

Comparison of three Knowledge Representation formalisms for encoding the NCEP Cholesterol Guidelines.

Although many Knowledge Representation (KR) formalisms have been used to encode care guidelines, there are few direct comparisons among different formalisms. In order to compare their suitability for encoding care guidelines, three different KR formalisms were used to encode the National Cholesterol Education Panel (NCEP) guideline. PROLOG, a First Order Logic system, CLASSIC, a frame-based representation system, and CLIPS, a production rule system, were used in the comparison. All three representations allowed accurate encoding of the guideline. PROLOG produced the most compact representation, but proved the most difficult to debug. The lack of arbitrary disjunction in CLASSIC greatly increased the complexity of the encoding. Overall, the CLIPS representation was the most intuitive and easiest to use.

Artificial Intelligence↗