Biomedical subjects

P J Haug

Publications and source records attributed to P J Haug.

At least 19 recordsLinked to original sources

PubMed1998

Evaluation of a semantic data model for chest radiology: application of a new methodology.

An essential step toward the effective processing of the medical language is the development of representational models that formalize the language semantics. These models, also known as semantic data models, help to unlock the meaning of descriptive expressions, making them accessible to computer systems. The present study tries to determine the quality of a semantic data model created to encode chest radiology findings. The evaluation methodology relied on the ability of physicians to extract information from textual and encoded representations of chest X-ray reports, whilst answering questions associated with each report. The evaluation demonstrated that the encoded reports seemed to have the same information content of the original textual reports. The methodology generated useful data regarding the quality of the data model, demonstrating that certain segments were creating ambiguous representations and that some details were not being represented.

Adult↗

PubMed1998

Bayesian modeling for linking causally related observations in chest X-ray reports.

Our natural language understanding system outputs a list of diseases, findings, and appliances found in a chest x-ray report. The system described in this paper links those diseases and findings that are causally related. Using Bayesian networks to model the conceptual and diagnostic information found in a chest x-ray we are able to infer more specific information about the findings that are linked to diseases.

Algorithms↗

PubMed1998

Diagnosing community-acquired pneumonia with a Bayesian network.

We present the development and the evaluation of a Bayesian network for the diagnosis of community-acquired pneumonia. The Bayesian network is intended to be part of a larger decision support system which assists emergency room physicians in the management of pneumonia patients. Minimal data entry from the nurse or the physician, timely availability of clinical parameters, and high accuracy were requirements we tried to meet. Data from more than 32,000 emergency room patients over a period of 2 years (June 1995-June 1997) were extracted from the clinical information system to train and test the Bayesian network. The network performed well in discriminating patients with pneumonia from patients with other diseases. The Bayesian network achieved a sensitivity of 95%, a specificity of 96.5%, an area under the receiver operating characteristic of 0.98, and a predictive value positive of 26.8%. Our feasibility study demonstrates that the proposed Bayesian network is an appropriate method to detect pneumonia patients with high accuracy. The study suggests that the proposed Bayesian network may represent a successful component within a larger decision support system for the management of community-acquired pneumonia.

Adult↗

PubMed1998

Automatic extraction of PIOPED interpretations from ventilation/perfusion lung scan reports.

Free-text documents are the main type of data produced by a radiology department in a hospital information system. While this type of data is readily accessible for clinical data review it can not be accessed by other applications to perform medical decision support, quality assurance, and outcome studies. In an attempt to solve this problem, natural language processing systems have been developed and tested against chest x-rays reports to extract relevant clinical information and make it accessible to other computer applications. We have used a natural language processing tool called SymText to extract relevant clinical information from a different type of radiology report, the Ventilation/Perfusion lung scan report. Results of this effort can be analyzed in terms of precision and recall. The overall precision was 0.88 and recall was 0.92. In addition, the natural language processing system functions differently in reports with and without an impression section. If this type of information can be successfully extracted from radiology reports, one can develop quality monitors for the diagnostic performance of the radiologist by correlating the impressions with gold standard data present in a hospital information system. Avoiding the manual effort previously necessary to create quality assurance data, can lead to a higher frequency of quality review in a radiology department.

Bayes Theorem↗

PubMed1997

Developing a radiology data base for quality assurance.

Radiology Information Systems (RIS) are designed to capture and manage the data associated with ordering, executing, reporting, and billing x-ray procedures. The HELP Hospital Information System contains a radiology subsystem that supports these functions. In an effort to enhance quality assurance initiatives, we have created a supplemental data base. This data base contains not only the data traditionally generated by RISs but also data from the hospital system that is relevant to quality assurance. One of the goals associated with this data base is to use techniques from the discipline of Continuous Quality Improvement (CQI) in the radiology department. A focus of our initial efforts has been the time necessary to provide x-ray reports to ordering physicians once the imaging examination has been performed. Efforts to manage the portion of this time interval caused by transcription have resulted in a substantial decrease in the time required for this function. A second goal of this project is to evaluate the quality of x-ray ordering. This objective requires a computerized record of the outcome of the x-ray procedure. Initial analysis of data derived from this data base indicates significant differences in the ordering behavior for computed tomography (CT) examinations among a test group of physicians. A third goal is to do quality assurance on x-ray reports. Experience with pilot systems has shown promising results using a mathematical model of report quality. We hope to leverage these techniques and this quality assurance data base to define a COI process for medical reports in general and for x-ray reports in particular.

Database Management Systems↗

PubMed1997

Inducing practice guidelines from a hospital database.

Improving health care quality requires the elimination of unnecessary variation in the care process. Decision support applications already exist that can foster adherence to standards. The challenge resides in developing standards consistent with good medical practice. In this paper we present our efforts in determining where sufficient clinical data are captured electronically to automatically define a care process, and what analyses can be done to identify additional data that would allow a care process to be defined. Data routinely collected by a hospital information system have been examined. The analysis tools utilized include logistic regression, a neural network, a Bayesian network, and a rule induction program.

Bayes Theorem↗

PubMed1997

A natural language parsing system for encoding admitting diagnoses.

Free-text or natural language documents make up an increasing part of the computerized medical record. While they do provide accessible clinical information to health care personnel, they fail to support processes that require clinical data coded according to a shared lexicon and data structure. We have developed a natural language parser that converts free-text admitting diagnoses into a coded form. This application has proven acceptably accurate in the experimental laboratory to warrant a test in the target clinical environment. Here we describe an approach to moving this research application into a production environment where it can contribute to the efforts of the Health Information Services Department. This transition is essential if the products of natural language understanding research are to contribute to patient care in a routine and sustainable way.

Diagnosis-Related Groups↗

PubMed1996

Development and evaluation of a computerized admission diagnoses encoding system.

Hospital information systems designed to support the needs of health care professionals include patient data entered using both freetext and precoded storage schemes. A major disadvantage of freetext storage schemes is that data captured in this format can only be presented as is to the user for review tasks. In the view of many health care scientists, natural language understanding systems capable of identifying, extracting, and encoding information contained in freetext data may provide the necessary tools to overcome this weakness. This paper describes the development and evaluation of a such a system designed to encode freetext admission diagnoses. This system combines both semantic and syntactic linguistic analysis techniques. Evaluation results demonstrate the overall performance of this system to be reasonable, accurately encoding approximately 76% of admission diagnoses. Inefficiencies are primarily due to the inability of this system to generate encodings in roughly 15% of test cases. When encodings are produced, however, accuracy equals that of the current manual coding method. With further modification, this application can partially automate the coding process.

Algorithms↗

PubMed1995

Experience with a mixed semantic/syntactic parser.

The value of the computerized medical record is derived in part from the availability of medical information in a coded form accessible to manipulation by processes designed for automated decision support, medical research, and computer assistance in the management of health care delivery. To meet these needs medical reports captured and stored as natural language documents must be encoded. Below we discuss an ongoing formative process aimed at developing a natural language understanding system for chest x-ray reports. Comparative data showing the progress of this process is presented.

Hospital Information Systems↗

PubMed1995

Assessing the behavioral impact of a diagnostic decision support system.

This paper describes a prototype for research to evaluate the impact of diagnostic decision support systems on the behavior of physicians. Several indices that can be used to quantify the magnitude of impact are proposed. A large medical diagnostic knowledge base in internal medicine (the Iliad knowledge base) was used in this evaluation. The impact on behavior when different inference models are run against this knowledge base is evaluated for two different case domains and physician's specialties.

Analysis of Variance↗

PubMed1994

Interfacing a stand-alone diagnostic expert system with a hospital information system.

Few diagnostic decision-support systems are in routine clinical use, mainly because these systems typically require time-consuming manual data entry. This research investigated the feasibility of reducing manual data entry by integrating a stand-alone diagnostic expert system with an existing comprehensive hospital information system (HIS). A knowledge-based intervocabulary mapping technique was developed to map disparate vocabularies. The results of a retrospective study indicate that transferring clinical data from the HIS to the diagnostic expert system at the beginning of workup significantly reduces the manual data entry required for generating the correct diagnoses for patients.

Diagnosis, Computer-Assisted↗

PubMed1994

Decision support in medicine: examples from the HELP system.

Computerized health information systems can contribute to the care received by patients in a number of ways. Not the least of these is through interactions with health care providers to modify diagnostic and therapeutic decisions. Since its beginning, developers have used the HELP hospital information system to explore computerized interventions into the medical decision making process. By their nature these interventions imply a computer-directed interaction with the physicians, nurses, and therapists involved in delivering care. In this paper we describe four different approaches to this intervention. These include: (1) processes that respond to the appearance of certain types of clinical data by issuing an alert informing caregivers of these data's presence and import, (2) programs that critique new orders and propose changes in those orders when appropriate, (3) programs that suggest new orders and procedures in response to patient data suggesting their need, and (4) applications that function by summarizing patient care data and that attempt to retrospectively assess the average or typical quality of medical decisions and therapeutic interventions made by health care providers. These approaches are illustrated with experience from the HELP system.

Adult↗

PubMed1994

Designing a controlled medical vocabulary server: the VOSER project.

The authors describe their experience designing a controlled medical vocabulary server created to support the exchange of patient data and medical decision logic. The first section introduces practical and theoretical premises that guided the design of the vocabulary server. The second section describes a series of structures needed to implement the proposed server, emphasizing their conformance to the design premises. The third section introduces potential applications that provide services to end users and also a group of tools necessary for maintaining the server corpus. In the fourth section, the authors propose an implementation strategy based on a common framework and on the participation of groups from different health-related domains.

Clinical Laboratory Information Systems↗

PubMed1994

Clinical evaluation of a renal mass diagnostic expert system.

In this paper, we describe our clinical evaluation of the diagnostic accuracy of the renal mass diagnostic system (RMDS) and of seven physicians. To investigate the value of intravenous urography (IVU) and/or retrograde urography (RU) in diagnosing renal parenchymal tumors and tumors of the renal pelvis, RMDS and the seven physicians were tested with and without the information regarding IVU/RU at two different times. From this study we believe that RMDS can help residents in making more accurate presurgical renal mass diagnosis, and may eliminate the need for IVU/RU in the diagnosing process for a specific group of patients.

Adolescent↗

PubMed1994

HELP the next generation: a new client-server architecture.

A new client-server based system which is centered around a lifetime data repository (LDR) is under construction. The goal of the new system is to maintain the patient centered decision support aspects of the existing HELP* system while providing an open architecture that supports faster application development and allows execution of applications to be distributed across many computers. These goals are achieved by implementing the system with software components that are commercially available or by adhering to national and international standards for software integration. Keys to successful integration include the use of MS-DOS @, OS/2#, and UNIX Section as operating systems, Microsoft OLE 2.0 as a standard interface to the clinical database, the use of TUXEDO as a transaction/communication manager, and the use of ORACLE [symbol: see text] RDBMS as the underlying database management system.

Computer Communication Networks↗

PubMed1994

An automated Computerized Severity Index.

The Computerized Severity Index (CSI) is a commercially available scoring system for hospital inpatients. Trained abstractors review the patient's paper medical record and enter the diagnoses and relevant physiological attributes. The HELP (Health Evaluation through Logical Processing) System at LDS Hospital stores patient data in discrete codes. This paper describes the development of an automatic interface between the standalone, personal-computer-based severity system and the mainframe-based hospital information system. The interface scores patient severity without the need for manual chart review. Severity scores from the automated and manual methods were identical for 70% of 222 general medical patients scored retrospectively. An evaluation of the causes for differing scores between the two methods is presented.

Computers, Mainframe↗

PubMed1994

Automated transformation of probabilistic knowledge for a medical diagnostic system.

Iliad is a large medical diagnostic system that covers more than 2000 diagnoses and 9000 findings. Due to the size and the complexity of this system, a robust knowledge representation is essential to consistently and efficiently model the medical knowledge involved. In this paper, we describe the knowledge representation currently used in Iliad and a probabilistic representation based on the Bayesian network formalism which can be derived using the information that the Iliad knowledge base contains.

Algorithms↗

PubMed1993

Uses of diagnostic expert systems in clinical care.

The development and testing of computerized systems to assist in the diagnostic process is a time honored research activity in medical information science. The focus of the majority of the applications produced is on providing accurate diagnostic suggestions when appropriate clinical information is entered. We believe that diagnostic knowledge has a much wider range of uses than that of simply assigning diagnostic labels. Below we describe three applications which illustrate alternate uses for diagnostic systems. Applications that assist in data collection, assess the quality of medical reports, and extract relevant clinical data from natural language x-ray reports are discussed. We believe that more effort should be directed toward studying the use of diagnostic knowledge bases in processes that help plan diagnostic strategies, in quality assurance applications, and in processes that facilitate all aspects of medical communication.

Clinical Medicine↗