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S W Tu

Publications and source records attributed to S W Tu.

16 recordsLinked to original sources
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↗
PubMed1996

The EON model of intervention protocols and guidelines.

We present a computational model of treatment protocols abstracted from implemented systems that we have developed previously. In our framework, a protocol is modeled as a hierarchical plan where high-level protocol steps are decomposed into descriptions of more specific actions. The clinical algorithms embodied in a protocol are represented by procedures that encode the sequencing, looping, and synchronization of protocol steps. The representation allows concurrent and optional protocol steps. We define the semantics of a procedure in terms of an execution model that specifies how the procedure should be interpreted. We show that the model can be applied to an asthma guideline different from the protocols for which the model was originally constructed.

Adult↗
PubMed1995

Computer-based screening of patients with HIV/AIDS for clinical-trial eligibility.

OBJECTIVE: To assess the potential effect of a computer-based system on accrual to clinical trials, we have developed methodology to identify retrospectively and prospectively patients who are eligible or potentially eligible for protocols. DESIGN: Retrospective chart abstraction with computer screening of data for potential protocol eligibility. SETTING: A county-operated clinic serving human immunodeficiency virus (HIV) positive patients with or without acquired immune deficiency syndrome (AIDS). PATIENTS: A randomly selected group of 60 patients who were HIV-infected, 30 of whom had an AIDS-defining diagnosis. DESIGN: Using a computer-based eligibility screening system, for each clinic visit and hospitalization, patients were categorized as eligible, potentially eligible, or ineligible for each of the 17 protocols active during the 7-month study period. Reasons for ineligibility were categorized. RESULTS: None of the patients was enrolled on a clinical trial during the 7-month period. Thirteen patients were identified as eligible for protocol; three patients were eligible for two different protocols; and one patient was eligible for the same protocol during two different time intervals. Fifty-four patients were identified as potentially eligible for a total of 165 accrual opportunities, but important information, such as the result of a required laboratory test, was missing, so that eligibility could not be determined unequivocally. Ineligibility for protocol was determined in 414 (35%) potential opportunities based only on conditions that were amenable to modification, such as the use of concurrent medications; 194 (17%) failed only laboratory tests or subjective determinations not routinely performed; and 346 (29%) failed only routine laboratory tests. CONCLUSIONS: There are substantial numbers of eligible and potentially eligible patients who are not enrolled or evaluated for enrollment in prospective clinical trials. Computer-based eligibility screening when coupled with a computer-based medical record offers the potential to identify patients eligible or potentially eligible for clinical trial, to assist in the selection of protocol eligibility criteria, and to make accrual estimates.

Acquired Immunodeficiency Syndrome↗
PubMed1995

Ontology-based configuration of problem-solving methods and generation of knowledge-acquisition tools: application of PROTEGE-II to protocol-based decision support.

PROTEGE-II is a suite of tools and a methodology for building knowledge-based systems and domain-specific knowledge-acquisition tools. In this paper, we show how PROTEGE-II can be applied to the task of providing protocol-based decision support in the domain of treating HIV-infected patients. To apply PROTEGE-II, (1) we construct a decomposable problem-solving method called episodic skeletal-plan refinement, (2) we build an application ontology that consists of the terms and relations in the domain, and of method-specific distinctions not already captured in the domain terms, and (3) we specify mapping relations that link terms from the application ontology to the domain-independent terms used in the problem-solving method. From the application ontology, we automatically generate a domain-specific knowledge-acquisition tool that is custom-tailored for the application. The knowledge-acquisition tool is used for the creation and maintenance of domain knowledge used by the problem-solving method. The general goal of the PROTEGE-II approach is to produce systems and components that are reusable and easily maintained. This is the rationale for constructing ontologies and problem-solving methods that can be composed from a set of smaller-grained methods and mechanisms. This is also why we tightly couple the knowledge-acquisition tools to the application ontology that specifies the domain terms used in the problem-solving systems. Although our evaluation is still preliminary, for the application task of providing protocol-based decision support, we show that these goals of reusability and easy maintenance can be achieved. We discuss design decisions and the tradeoffs that have to be made in the development of the system.

Artificial Intelligence↗
PubMed1995

PROTEGE-II: computer support for development of intelligent systems from libraries of components.

PROTEGE-II is a suite of tools that facilitates the development of intelligent systems. A tool called MAiTRE allows system builders to create and refine abstract models (ontologies) of application domains. A tool called DASH takes as input a modified domain ontology and generates automatically a knowledge-acquisition tool that application specialists can use to enter the detailed content knowledge required to define particular applications. The domain-dependent knowledge entered into the knowledge-acquisition tool is used by assemblies of domain-independent problem-solving methods that provide the computational strategies required to solve particular application tasks. The result is an architecture that offers a divide-and-conquer approach that separates system-building tasks that require skill in domain analysis and modeling from those that require simple entry of content knowledge. At the same time, applications can be constructed from libraries of component--of both domain ontologies and domain-independent problem-solving methods--allowing the reuse of knowledge and facilitating ongoing system maintenance. We have used PROTEGE-II to construct a number of knowledge-based systems, including the reasoning components of T-Helper, which assists physicians in the protocol-based care of patients who have HIV infection.

Clinical Trials as Topic↗
PubMed1995

Knowledge-based temporal abstraction in diabetes therapy.

We suggest a general framework for solving the task of creating abstract, interval-based concepts from time-stamped clinical data. We refer to this problem-solving framework as the knowledge-based temporal-abstraction (KBTA) method. The KBTA method emphasizes explicit representation, acquisition, maintenance, reuse, and the sharing of knowledge required for abstraction of time-oriented clinical data. We describe the subtasks into which the KBTA method decomposes its task, the problem-solving mechanisms that solve these subtasks, and the knowledge necessary for instantiating these mechanisms in a particular clinical domain. We have implemented the KBTA method in the RESUME system and have applied it to the task of monitoring the care of insulin-dependent diabetics.

Artificial Intelligence↗
PubMed1994

A temporal-abstraction mediator for protocol-based decision-support systems.

The inability of many clinical decision-support applications to integrate with existing databases limits the wide-scale deployment of such systems. To overcome this obstacle, we have designed a data-interpretation module that can be embedded in a general architecture for protocol-based reasoning and that can support the fundamental task of detecting temporal abstractions. We have developed this software module by coupling two existing systems--RESUME and Chronus--that provide complementary temporal-abstraction techniques at the application and the database levels, respectively. Their encapsulation into a single module thus can resolve the temporal queries of protocol planners with the domain-specific knowledge needed for the temporal-abstraction task and with primary time-stamped data stored in autonomous clinical databases. We show that other computer methods for the detection of temporal abstractions do not scale up to the data- and knowledge-intensive environments of protocol-based decision-support systems.

Artificial Intelligence↗
PubMed1994

Knowledge-based temporal abstraction for diabetic monitoring.

We have developed a general method that solves the task of creating abstract, interval-based concepts from time-stamped clinical data. We refer to this method as knowledge-based temporal-abstraction (KBTA). In this paper, we focus on the knowledge representation, acquisition, maintenance, reuse and sharing aspects of the KBTA method. We describe five problem-solving mechanisms that solve the five subtasks into which the KBTA method decomposes its task, and four types of knowledge necessary for instantiating these mechanisms in a particular domain. We present an example of instantiating the KBTA method in the clinical area of monitoring insulin-dependent-diabetes patients.

Artificial Intelligence↗
PubMed1993

AIDS2: a decision-support tool for decreasing physicians' uncertainty regarding patient eligibility for HIV treatment protocols.

We have developed a decision-support tool, the AIDS Intervention Decision-Support System (AIDS2), to assist in the task of matching patients to therapy-related research protocols. The purposes of AIDS2 are to determine the initial eligibility status of HIV-infected patients for therapy-related research protocols, and to suggest additional data-gathering activities that will decrease uncertainty related to the eligibility status. AIDS2 operates in either a patient-driven or protocol-driven mode. We represent the system knowledge in three combined levels: a classification level, where deterministic knowledge is represented; a belief-network level, where probabilistic knowledge is represented; and a control level, where knowledge about the system's operation is stored. To determine whether the design specifications were met, we presented a series of 10 clinical cases based on actual patients to the system. AIDS2 provided meaningful advice in all cases.

Acquired Immunodeficiency Syndrome↗
PubMed1993

A methodology for determining patients' eligibility for clinical trials.

The task of determining patients' eligibility for clinical trials is knowledge and data intensive. In this paper, we present a model for the task of eligibility determination, and describe how a computer system can assist clinical researchers in performing that task. Qualitative and probabilistic approaches to computing and summarizing the eligibility status of potentially eligible patients are described. The two approaches are compared, and a synthesis that draws on the strengths of each approach is proposed. The result of applying these techniques to a database of HIV-positive patient cases suggests that computer programs such as the one described can increase the accrual rate of eligible patients into clinical trials. These methods may also be applied to the task of determining from electronic patient records whether practice guidelines apply in particular clinical situations.

Clinical Trials as Topic↗
PubMed1992

An extended SQL for temporal data management in clinical decision-support systems.

We are developing a database implementation to support temporal data management for the T-HELPER physician workstation, an advice system for protocol-based care of patients who have HIV disease. To understand the requirements for the temporal database, we have analyzed the types of temporal predicates found in clinical-trial protocols. We extend the standard relational data model in three ways to support these querying requirements. First, we incorporate timestamps into the two-dimensional relational table to store the temporal dimension of both instant- and interval-based data. Second, we develop a set of operations on timepoints and intervals to manipulate timestamped data. Third, we modify the relational query language SQL so that its underlying algebra supports the specified operations on timestamps in relational tables. We show that our temporal extension to SQL meets the temporal data-management needs of protocol-directed decision support.

Acquired Immunodeficiency Syndrome↗
PubMed1991

Temporal-abstraction mechanisms in management of clinical protocols.

We have identified several general temporal-abstraction mechanisms needed for reasoning about time-stamped data, such as are needed in management of patients being treated on clinical protocols: simple temporal abstraction (a mechanism for abstracting several parameter values into one class), temporal inference (a mechanism for inferring sound logical conclusions over a single interval or two meeting intervals), and temporal interpolation (a mechanism for bridging non-meeting temporal intervals). Making explicit the knowledge required for temporal abstractions supports the acquisition of planning knowledge, the identification of clinical problems, and the formulation of clinical-management-plan revisions.

Clinical Protocols↗
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↗
PubMedDate not supplied

EON: a component-based approach to automation of protocol-directed therapy.

Provision of automated support for planning protocol-directed therapy requires a computer program to take as input clinical data stored in an electronic patient-record system and to generate as output recommendations for therapeutic interventions and laboratory testing that are defined by applicable protocols. This paper presents a synthesis of research carried out at Stanford University to model the therapy-planning task and to demonstrate a component-based architecture for building protocol-based decision-support systems. We have constructed general-purpose software components that (1) interpret abstract protocol specifications to construct appropriate patient-specific treatment plans; (2) infer from time-stamped patient data higher-level, interval-based, abstract concepts; (3) perform time-oriented queries on a time-oriented patient database; and (4) allow acquisition and maintenance of protocol knowledge in a manner that facilitates efficient processing both by humans and by computers. We have implemented these components in a computer system known as EON. Each of the components has been developed, evaluated, and reported independently. We have evaluated the integration of the components as a composite architecture by implementing T-HELPER, a computer-based patient-record system that uses EON to offer advice regarding the management of patients who are following clinical trial protocols for AIDS or HIV infection. A test of the reuse of the software components in a different clinical domain demonstrated rapid development of a prototype application to support protocol-based care of patients who have breast cancer.

Algorithms↗