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Biomedical subjects

M A Musen

Publications and source records attributed to M A Musen.

At least 37 records · Page 2Linked to original sources
PubMed1999

Applying temporal joins to clinical databases.

Clinical databases typically contain a significant amount of temporal information, information that is often crucial in medical decision-support systems. Most recent clinical information systems use the relational model when working with this information. Although these systems have reasonably well-defined semantics for temporal queries on a single relational table, many do not fully address the complex semantics of operations involving multiple temporal tables. Such operations can arise frequently in queries on clinical databases. This paper describes the issues encountered when joining a set of temporal tables, and outlines how such joins are far more complex than non-temporal ones. We describe the semantics of temporal joins in a query management system called Chronus II, a system we have developed to assist in evaluating patients for clinical trials.

Database Management Systems↗
PubMed1999

Tool support for authoring eligibility criteria for cancer trials.

A critical component of authoring new clinical trial protocols is assembling a set of eligibility criteria for patient enrollment. We found that clinical protocols in three different cancer domains can be categorized according to a set of clinical states that describe various clinical scenarios for that domain. Classifying protocols in this manner revealed similarities among the eligibility criteria and permitted some standardization of criteria based on clinical state. We have developed an eligibility criteria authoring tool which uses a standard set of eligibility criteria and a diagram of the clinical states to present the relevant eligibility criteria to the protocol author. We demonstrate our ideas with phase-3 protocols from breast cancer, prostate cancer, and non-small cell lung cancer. Based on measurements of redundancy and percentage coverage of criteria included in our tool, we conclude that our model reduces redundancy in the number of criteria needed to author multiple protocols, and it allows some eligibility criteria to be authored automatically based on the clinical state of interest for a protocol.

Breast Neoplasms↗
PubMed1999

Justification of automated decision-making: medical explanations as medical arguments.

People use arguments to justify their claims. Computer systems use explanations to justify their conclusions. We are developing WOZ, an explanation framework that justifies the conclusions of a clinical decision-support system. WOZ's central component is the explanation strategy that decides what information justifies a claim. The strategy uses Toulmin's argument structure to define pieces of information and to orchestrate their presentation. WOZ uses explicit models that abstract the core aspects of the framework such as the explanation strategy. In this paper, we present the use of arguments, the modeling of explanations, and the explanation process used in WOZ. WOZ exploits the wealth of naturally occurring arguments, and thus can generate convincing medical explanations.

Artificial Intelligence↗
PubMed1999

A flexible approach to guideline modeling.

We describe a task-oriented approach to guideline modeling that we have been developing in the EON project. We argue that guidelines seek to change behaviors by making statements involving some or all of the following tasks: (1) setting of goals or constraints, (2) making decisions among alternatives, (3) sequencing and synchronization of actions, and (4) interpreting data. Statements about these tasks make assumptions about models of time and of data abstractions, and about degree of uncertainty, points of view, and exception handling. Because of this variability in guideline tasks and assumptions, monolithic models cannot be custom tailored to the requirements of different classes of guidelines. Instead, we have created a core model that defines a set of basic concepts and relations and that uses different submodels to account for differing knowledge requirements. We describe the conceptualization of the guideline domain that underlies our approach, discuss components of the core model and possible submodels, and give three examples of specialized guideline models to illustrate how task-specific guideline models can be specialized and assembled to better match modeling requirements of different guidelines.

Asthma↗
PubMed1999

The low availability of metadata elements for evaluating the quality of medical information on the World Wide Web.

A great barrier to the use of Internet resources for patient education is the concern over the quality of information available. We conducted a study to determine what information was available in Web pages, both within text and metadata source code, that could be used in the assessment of information quality. Analysis of pages retrieved from 97 unique sites using a simple keyword search for "breast cancer treatment" on a generic and a health-specific search engine revealed that basic publishing elements were present in low frequency: authorship (20%), attribution/references (32%), disclosure (41%), and currency (35%). Only one page retrieved contained all four elements. Automated extraction of metadata elements from the source code of 822 pages retrieved from five popular generic search engines revealed even less information. We discuss the design of a metadata-based system for the evaluation of quality of medical content on the World Wide Web that addresses current limitations in ensuring quality.

Breast Neoplasms↗
PubMed1999

Scalable software architectures for decision support.

Interest in decision-support programs for clinical medicine soared in the 1970s. Since that time, workers in medical informatics have been particularly attracted to rule-based systems as a means of providing clinical decision support. Although developers have built many successful applications using production rules, they also have discovered that creation and maintenance of large rule bases is quite problematic. In the 1980s, several groups of investigators began to explore alternative programming abstractions that can be used to build decision-support systems. As a result, the notions of "generic tasks" and of reusable problem-solving methods became extremely influential. By the 1990s, academic centers were experimenting with architectures for intelligent systems based on two classes of reusable components: (1) problem-solving methods--domain-independent algorithms for automating stereotypical tasks--and (2) domain ontologies that captured the essential concepts (and relationships among those concepts) in particular application areas. This paper highlights how developers can construct large, maintainable decision-support systems using these kinds of building blocks. The creation of domain ontologies and problem-solving methods is the fundamental end product of basic research in medical informatics. Consequently, these concepts need more attention by our scientific community.

Decision Support Systems, Clinical↗
PubMed1998

VM-in-Protégé: a study of software reuse.

Protégé is a system that encompasses a suite of graphical tools and a methodology for applying them to the task of creating and maintaining knowledge-based systems. One of our key goals for Protégé is to facilitate reuse on new problems of components of previously developed solutions. We investigated this reusability by applying preexisting library components in the Protégé system to a reconstruction of VM, a well-known rule-based system for ventilator management. The formal steps of the Protégé methodology-ontology creation, problem-solving method selection, knowledge engineering, and mapping-relation instantiation-were followed, and a working system with much of the reasoning capability of the original VM was created. The work illuminated important lessons regarding aspects of component reusability.

Algorithms↗
PubMed1998

Domain ontologies in software engineering: use of Protégé with the EON architecture.

Domain ontologies are formal descriptions of the classes of concepts and the relationships among those concepts that describe an application area. The Protégé software-engineering methodology provides a clear division between domain ontologies and domain-independent problem-solvers that, when mapped to domain ontologies, can solve application tasks. The Protégé approach allows domain ontologies to inform the total software-engineering process, and for ontologies to be shared among a variety of problem-solving components. We illustrate the approach by describing the development of EON, a set of middleware components that automate various aspects of protocol-directed therapy. Our work illustrates the organizing effect that domain ontologies can have on the software-development process. Ontologies, like all formal representations, have limitations in their ability to capture the semantics of application areas. Nevertheless, the capability of ontologies to encode clinical distinctions not usually captured by controlled medical terminologies provides significant advantages for developers and maintainers of clinical software applications.

Computer Systems↗
PubMed1998

Modern architectures for intelligent systems: reusable ontologies and problem-solving methods.

When interest in intelligent systems for clinical medicine soared in the 1970s, workers in medical informatics became particularly attracted to rule-based systems. Although many successful rule-based applications were constructed, development and maintenance of large rule bases remained quite problematic. In the 1980s, an entire industry dedicated to the marketing of tools for creating rule-based systems rose and fell, as workers in medical informatics began to appreciate deeply why knowledge acquisition and maintenance for such systems are difficult problems. During this time period, investigators began to explore alternative programming abstractions that could be used to develop intelligent systems. The notions of "generic tasks" and of reusable problem-solving methods became extremely influential. By the 1990s, academic centers were experimenting with architectures for intelligent systems based on two classes of reusable components: (1) domain-independent problem-solving methods-standard algorithms for automating stereotypical tasks--and (2) domain ontologies that captured the essential concepts (and relationships among those concepts) in particular application areas. This paper will highlight how intelligent systems for diverse tasks can be efficiently automated using these kinds of building blocks. The creation of domain ontologies and problem-solving methods is the fundamental end product of basic research in medical informatics. Consequently, these concepts need more attention by our scientific community.

Algorithms↗
PubMed1998

A declarative explanation framework that uses a collection of visualization agents.

User acceptance of a knowledge-based system depends partly on how effective the system is in explaining its reasoning and justifying its conclusions. The WOZ framework provides effective explanations for component-based decision-support systems. It represents explanation using explicit models, and employs a collection of visualization agents. It blends the strong features of existing explanation strategies, component-based systems, graphical visualizations, and explicit models. We illustrate the features of WOZ with the help of a component-based medical therapy system. We describe the explanation strategy, the roles of the visualization agents and components, and the communication structure. The integration of existing and new visualization applications, the domain-independent framework, and the incorporation of varied knowledge sources for explanation can result in a flexible explanation facility.

Artificial Intelligence↗
PubMed1998

Therapy planning as constraint satisfaction: a computer-based antiretroviral therapy advisor for the management of HIV.

We applied the Protégé methodology for building knowledge-based systems to the domain of antiretroviral therapy. We modeled the task of prescribing drug therapy for HIV, abstracting the essential characteristics of the problem solving. We mapped our model of the antiretroviral-therapy domain to the class of constraint-satisfaction problems, and reused the propose-and-revise problem-solving method, from the Protégé library of methods, to build an antiretroviral therapy advisor, ART Critic. Careful modeling and using Protégé allowed us to build a useful and extensible knowledge-based application rapidly.

Anti-HIV Agents↗
PubMed1997

Sequential versus standard neural networks for pattern recognition: an example using the domain of coronary heart disease.

The goal of this study was to compare standard and sequential neural network models for recognition of patterns of disease progression. Medical researchers who perform prognostic modeling usually oversimplify the problem by choosing a single point in time to predict outcomes (e.g. death in 5 years). This approach not only fails to differentiate patterns of disease progression, but also wastes important information that is usually available in time-oriented research data bases. The adequate use of sequential neural networks can improve the performance of prognostic systems if the interdependencies among prognoses at different intervals of time are explicitly modeled. In such models, predictions for a certain interval of time (e.g. death within 1 year) are influenced by predictions made for other intervals, and prognostic survival curves that provide consistent estimates for several points in time can be produced. We developed a system of neural network models that makes use of time-oriented data to predict development of coronary heart disease (CHD), using a set of 2594 patients. The output of the neural network system was a prognostic curve representing survival without CHD, and the inputs were the values of demographic, clinical, and laboratory variables. The system of neural networks was trained by backpropagation and its results were evaluated in test sets of previously unseen cases. We showed that, by explicitly modeling time in the neural network architecture, the performance of the prognostic index, measured by the area under the receiver operating characteristic (ROC) curve, was significantly improved (p < 0.05).

Adult↗
PubMed1997

A foundational model of time for heterogeneous clinical databases.

Differences among the database representations of clinical data are a major barrier to the integration of databases and to the sharing of decision-support applications across databases. Prior research on resolving data heterogeneity has not addressed specifically the types of mismatches found in various timestamping approaches for clinical data. Such temporal mismatches, which include time-unit differences among timestamps, must be overcome before many applications can use these data to reason about diagnosis, therapy, or prognosis. In this paper, we present an analysis of the types of temporal mismatches that exist in databases. To formalize these various approaches to timestamping, we provide a foundational model of time. This model gives us the semantics necessary to encode the temporal dimensions of clinical data in legacy databases and to transform such heterogeneous data into a uniform temporal representation suitable for decision support. We have implemented this foundational model as an extension to our Chronus system, which provides clinical decision-support applications the ability to match temporal patterns in clinical databases. We discuss the uniqueness of our approach in comparison with other research on representing and querying clinical data with varying timestamp representations.

Databases as Topic↗
PubMed1997

A temporal database mediator for protocol-based decision support.

To meet the data-processing requirements for protocol-based decision support, a clinical data-management system must be capable of creating high-level summaries of time-oriented patient data, and of retrieving those summaries in a temporally meaningful fashion. We previously described a temporal-abstraction module (RESUME) and a temporal-querying module (Chronus) that can be used together to perform these tasks. These modules had to be coordinated by individual applications, however, to resolve the temporal queries of protocol planners. In this paper, we present a new module that integrates the previous two modules and that provides for their coordination automatically. The new module can be used as a standalone system for retrieving both primitive and abstracted time-oriented data, or can be embedded in a larger computational framework for protocol-based reasoning.

Artificial Intelligence↗
PubMed1996

Knowledge-based temporal abstraction in clinical domains.

We have defined a knowledge-based framework for the creation of abstract, interval-based concepts from time-stamped clinical data, the knowledge-based temporal-abstraction (KBTA) method. The KBTA method decomposes its task into five subtasks; for each subtask we propose a formal solving mechanism. Our framework emphasizes explicit representation of knowledge required for abstraction of time-oriented clinical data, and facilitates its acquisition, maintenance, reuse and sharing. The RESUME system implements the KBTA method. We tested RESUME in several clinical-monitoring domains, including the domain of monitoring patients who have insulin-dependent diabetes. We acquired from a diabetes-therapy expert diabetes-therapy temporal-abstraction knowledge. Two diabetes-therapy experts (including the first one) created temporal abstractions from about 800 points of diabetic-patients' data. RESUME generated about 80% of the abstractions agreed by both experts; about 97% of the generated abstractions were valid. We discuss the advantages and limitations of the current architecture.

Artificial Intelligence↗
PubMed1996

Toward reusable software components at the point of care.

An architecture built from five software components -a Router, Parser, Matcher, Mapper, and Server -fulfills key requirements common to several point-of-care information and knowledge processing tasks. The requirements include problem-list creation, exploiting the contents of the Electronic Medical Record for the patient at hand, knowledge access, and support for semantic visualization and software agents. The components use the National Library of Medicine Unified Medical Language System to create and exploit lexical closure-a state in which terms, text and reference models are represented explicitly and consistently. Preliminary versions of the components are in use in an oncology knowledge server.

Computer Systems↗