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Biomedical subjects

L M Fagan

Publications and source records attributed to L M Fagan.

18 recordsLinked to original sources
PubMed1992

Anticoagulation therapy advisor: a decision-support system for heparin therapy during ECMO.

We present a case study describing our development of a mathematical model to control a clinical parameter in a patient--in this case, the degree of anticoagulation during extracorporeal membrane oxygenation (ECMO) support. During ECMO therapy, an anticoagulant agent (heparin) is administered to prevent thrombosis. Under- or over-coagulation can have grave consequences. To improve control of anticoagulation, we developed a pharmacokinetic-pharmacodynamic (PK-PD) model that predicts activated clotting times (ACT) using the NONMEM program. We then integrated this model into a decision-support system, and validated it with an independent data set. The population model had a mean absolute error of prediction for ACT values of 33.5 seconds, with a mean bias in estimation of -14.3 seconds. Individualization of model-parameter estimates using nonlinear regression improved the absolute error prediction to 25.5 seconds, and lowered the mean bias to -3.1 seconds. The PK-PD model is coupled with software for heuristic interpretation of model results to provide a complete environment for the management of anticoagulation.

Blood Coagulation Disorders↗
PubMed1992

T-HELPER: automated support for community-based clinical research.

There are increasing expectations that community-based physicians who care for people with HIV infection will offer their patients opportunities to enroll in clinical trials. The information-management requirements of clinical investigation, however, make it unrealistic for most providers who do not practice in academic centers to participate in clinical research. Our T-HELPER computer system offers community-based physicians the possibility of enrolling patients in clinical trials as a component of primary care. T-HELPER facilitates data management for patients with HIV disease, and can offer patient-specific and situation-specific advice concerning new protocols for which patients may be eligible and the treatment required by those protocols in which patients currently are enrolled. We are installing T-HELPER at three county-operated AIDS clinics in the San Francisco Bay Area, and plan a comprehensive evaluation of the system and its influence on clinical research.

Clinical Trials as Topic↗
PubMed1992

Augmented transition networks as a representation for knowledge-based history-taking systems.

Numerous history-taking systems have been built to automate the medical history-taking process. These systems differ in their control methods, input and output modalities, and kinds of questions asked. Thus, there has emerged no standard way of representing interviewing knowledge--the expert knowledge used to govern the sequence of questions asked in an interview. This paper discusses how we use an augmented transition network (ATN) to represent the knowledge of a speech-driven automated history-taking program, Q-MED, and how, more generally, ATNs could be used as a representation for any knowledge-based history-taking system. We identify three characteristics of ATNs that facilitate the use of ATNs in interviewing systems: explicitness, hierarchical structure, and generality.

Artificial Intelligence↗
PubMed1992

The integration of a continuous-speech-recognition system with the QMR diagnostic program.

We describe a continuous-speech interface for Quick Medical Reference (QMR), which allows physicians to input spoken descriptions of physical-examination findings, or observations. We analyze the difficulties in designing a continuous-speech interface for systems that use medical terminology. We present a method for matching spoken findings names expressed in natural language to QMR terms. The method is based on a semantic representation of findings that both minimize the effect of misrecognition and derive grammars that are necessary for supporting the recognition process.

Diagnosis, Computer-Assisted↗
PubMed1991

TQuery: a context-sensitive temporal query language.

Users of electronic medical databases request pertinent information by recasting their clinical questions into a formal database query language. Because the query language is the user's only access to the data, the query language must be powerful enough to enable users to express their data requirements. However, a competing need is for the query language to be restrictive enough so that queries can have unambiguous semantics and the query processor can generate correct answers. We describe a query language, called TQuery , that was designed specifically to formulate database queries that are dependent on temporal and contextual relationships. TQuery specifications express contextual constraints without the need to explicitly reference calendar dates. TQuery is the database query language used to retrieve patient data from an object-oriented electronic patient medical-record system called the temporal network (TNET). TNET and TQuery were developed to support the real-time temporal reasoning and representation needs of a LISP workstation-based medical expert system.

Databases, Factual↗
PubMed1991

The design of a user interface for a ventilator-management advisor.

The lack of user acceptance for many medical decision-support systems should force medical software developers to rethink strategies for user interaction with decision-support programs. Participatory design is an emerging method for the development for computer applications that emphasizes user involvement in both the design and implementation phases. We have applied participatory design to the development of a user interface for VentPlan, an application that assists physicians in the management of artificial respiration of critically ill patients. In this paper, we present a case history of the participatory design process and describe the resulting interface for the VentPlan program. As a result of applying participatory design ideas, we gained insight as to how to implement VentPlan more effectively.

Critical Care↗
PubMed1991

Combining physiologic models and symbolic methods to interpret time-varying patient data.

This paper describes a methodology for representing and using medical knowledge about temporal relationships to infer the presence of clinical events that evolve over time. The methodology consists of three steps: (1) the incorporation of patient observations into a generic physiologic model, (2) the conversion of model states and predictions into domain-specific temporal abstractions, and (3) the transformation of temporal abstractions into clinically meaningful descriptive text. The first step converts raw observations to underlying model concepts, the second step identifies temporal features of the fitted model that have clinical interest, and the third step replaces features represented by model parameters and predictions into concepts expressed in clinical language. We describe a program, called TOPAZ, that uses this three-step methodology. TOPAZ generates a narrative summary of the temporal events found in the electronic medical record of patients receiving cancer chemotherapy. A unique feature of TOPAZ is its use of numeric and symbolic techniques to perform different temporal reasoning tasks. Time is represented both as a continuous process and as a set of temporal intervals. These two temporal models differ in the temporal ontology they assume and in the temporal concepts they encode. Without multiple temporal models, this diversity of temporal knowledge could not be represented.

Adult↗
PubMed1991

Extensions to the time-oriented database model to support temporal reasoning in medical expert systems.

Physicians faced with diagnostic and therapeutic decisions must reason about clinical features that change over time. Database-management systems (DBMS) can increase access to patient data, but most systems are limited in their ability to store and retrieve complex temporal information. The Time-Oriented Databank (TOD) model, the most widely used data model for medical database systems, associates a single time stamp with each observation. The proper analysis of most clinical data requires accounting for multiple concurrent clinical events that may alter the interpretation of the raw data. Most medical DBMSs cannot retrieve patient data indexed by multiple clinical events. We describe two logical extensions to TOD-based databases that solve a set of temporal reasoning problems we encountered in constructing medical expert systems. A key feature of both extensions is that stored data are partitioned into groupings, such as sequential clinical visits, clinical exacerbations, or other abstract events that have clinical decision-making relevance. The temporal network (TNET) is an object-oriented database that extends the temporal reasoning capabilities of ONCOCIN, a medical expert system that provides chemotherapy advice. TNET uses persistent objects to associate observations with intervals of time during which "an event of clinical interest" occurred. A second object-oriented system called the extended temporal network (ETNET), is both an extension and a simplification of TNET. Like TNET, ETNET uses persistent objects to represent relevant intervals; unlike the first system, however, ETNET contains reasoning methods (rules) that can be executed when an event "begins", and that are withdrawn when that event "concludes". TNET and ETNET capture temporal relationships among recorded information that are not represented in TOD-based databases. Although they do not solve all temporal reasoning problems found in medical decision making, these new structures enable patient database systems to encode complex temporal relationships, to store and retrieve patient data based on multiple clinical contexts and, in ETNET, to modify the reasoning methods available to an expert system based on the onset or conclusion of specific clinical events.

Diagnosis, Computer-Assisted↗
PubMed1989

Validating the knowledge base of a therapy planning system.

Validation of expert system knowledge bases has proved to be difficult. This paper presents a description of a system called ScriptGen that generates test data for validating the knowledge base of the ONCOCIN cancer therapy planning system. Because of the size and complexity of the ONCOCIN knowledge base, we require tools for automated validation. ScriptGen, which applies techniques developed in testing both traditional software and expert systems, uses a parallel model of the ONCOCIN knowledge base and its own inference engine to generate test cases. We derived the limits of the system from a study that seeded errors into an existing knowledge base.

Antineoplastic Combined Chemotherapy Protocols↗
PubMed1987

A therapy planning architecture that combines decision theory and artificial intelligence techniques.

Through our experience with the ONCOCIN cancer therapy consultation system, we have identified a set of medical planning problems to which no single existing computer-based reasoning technique readily applies. In response to the need for automated assistance with this class of problems, we have devised a computer program called ONYX that combines decision-theoretic and artificial intelligence approaches to planning. We discuss our rationale for devising a new planning architecture and describe in detail how that architecture is implemented. The program's planning process consists of three steps: (i) the use of rules derived from therapy planning strategies to generate a small set of plausible plans, (ii) the use of knowledge about the structure and behavior of the human body to create simulations that predict possible consequences of each plan for the patient, and (iii) the use of decision theory to rank the plans according to how well the results of each simulation meet the treatment goals. This architecture explicitly manages the uncertainty inherent in many planning tasks, introduces a possible mechanism for the dissemination of decision-theoretic therapy advice, and potentially increases the number of problem solving domains in which expert system techniques can be effectively applied.

Artificial Intelligence↗
PubMed1987

Knowledge engineering for a clinical trial advice system: uncovering errors in protocol specification.

ONCOCIN is an expert system that provides advice to physicians who are treating cancer patients enrolled in clinical trials. The process of encoding oncology protocol knowledge for the system has revealed serious omissions and unintentional ambiguities in the protocol documents. We have also discovered that many protocols allow for significant latitude in treating patients and that even when protocol guidelines are explicit, physicians often choose to apply their own judgment on the assumption that the specifications are incomplete. Computer-based tools offer the possibility of insuring completeness and reproducibility in the definition of new protocols. One goal of our automated protocol authoring environment, called OPAL, is to help physicians develop protocols that are free of ambiguity and thus to assure better compliance and standardization of care.

Clinical Trials as Topic↗
PubMed1979

Antimicrobial selection by a computer. A blinded evaluation by infectious diseases experts.

An evaluation of a computer-based consultation system called MYCIN was made. Eight independent evaluators with special expertise in the management of meningitis compared MYCIN's choice of antimicrobials with the choices of nine human prescribers for ten test cases of meningitis. MYCIN received an acceptability rating of 65% by the evaluators; the corresponding ratings for acceptability of the regimen prescribed by the five faculty specialists ranged from 42.5% to 62.5%. The system never failed to cover a treatable pathogen while demonstrating efficiency in minimizing the number of antimicrobials prescribed. The study design may be useful in assessing the performance of other computer-based clinical decision-making systems.

Adult↗
PubMed1976

Computerized consultation system for selection of antimicrobial therapy.

Mycin, a computer-based consultation system which provides to physicians antimicrobial therapy recommendations for patients with bacterial infections, is described. The consultation program arrives at therapeutic decisions using a built-in knowledge base as well as patient data entered by the physician. The system is capable of explaining its recommendations and answering questions about its reasoning process. The system's knowledge can be updated and corrected easily by infectious disease experts. At present the system is operational within a research setting; its routine use in a clinical setting will require further evaluation of its reliability and effectiveness.

Anti-Infective Agents↗
PubMedDate not supplied

A methodology for generating computer-based explanations of decision-theoretic advice.

Decision analysis is an appealing methodology with which to provide decision support to the practicing physician. However, its use in the clinical setting is impeded because computer-based explanations of decision-theoretic advice are difficult to generate without resorting to mathematical arguments. Nevertheless, human decision analysts generate useful and intuitive explanations based on decision trees. To facilitate the use of decision theory in a computer-based decision support system, the authors developed a computer program that uses symbolic reasoning techniques to generate nonquantitative explanations of the results of decision analyses. A combined approach has been implemented to explain the differences in expected utility among branches of a decision tree. First, the mathematical relationships inherent in the structure of the tree are used to find any asymmetries in tree structure or inequalities among analogous decision variables that are responsible for a difference in expected utility. Next, an explanation technique is selected and applied to the most significant variables, creating a symbolic expression that justifies the decision. Finally, the symbolic expression is converted to English-language text, thereby generating an explanation that justifies the desirability of the choice with the greater expected utility. The explanation does not refer to mathematical formulas, nor does it include probability or utility values. The results suggest that explanations produced by a combination of decision analysis and symbolic processing techniques may be more persuasive and acceptable to clinicians than those produced by either technique alone.

Artificial Intelligence↗
PubMedDate not supplied

Managing the data from respiratory measurements.

Clinical decisionmaking depends upon properly interpreting the significance of physiological and other clinical data. Our experience, summarized in six case studies, suggests that no one variable is sufficient for making clinical decisions. Rather, different parameters are relevant in different situations. This article summarizes two techniques for improving the effectiveness of clinical decisionmaking in the ICU using quantitative physiological monitoring data. First, mathematical modeling has been used for measuring the volume of gas in the lungs of patients receiving mechanical ventilation. The technique analyzes the transient response to oxygen change; thus it is suitable for routine use in the ICU. Second, symbolic processing has been used for interpreting the clinical significance of measured data. This symbolic processing is used for recognizing artifact in measured data, determining expected physiological meaning of measured data in different clinical situations, identifying physiological status, and identifying therapy that may be appropriate for meeting therapeutic goals or correcting physiological problems in patients in the intensive care unit.

Aged↗