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PubMed1994

Towards productive Knowledge-Based Systems in clinical organizations: a methods perspective.

This paper discusses an approach towards Knowledge-Based Systems (KBS) development which emphazises fit in the clinical organization, utility and safety. KBS design is identified as a subset of Decision Support System (DSS) design, and experiences from use of development methods is made available from the more general field. Generic and specific issues related to making KBS development result in systems used in clinical practice are discussed, and an integration of the Logic Engineering KBS technique into the Action Design DSS requirements specification method is outlined. Group-based knowledge modeling is identified as the bridge between the methods. It is concluded that while Action Design provides organizational validation (Are we building the right system?), Logic Engineering adds on KBS design verification (Are we going to build the system right?).

Artificial Intelligence↗
PubMed1994

Data driven medical decision support based on Arden Syntax within the HELIOS environment.

Methods and tools for development of data driven decision support systems (DSSs) is an integral part of the HELIOS software engineering environment. The DSS development environment includes tools for knowledge acquisition and knowledge base construction, provides trigger mechanisms for evocation of the knowledge base and an inference engine for execution control of appropriate parts of the knowledge base during run-time. The tools are part of the HELIOS service components and are integrated within the HELIOS communication framework. The work is based on the Arden Syntax, a standard for knowledge representation, allowing medical logic modules (MLMs) to be shared and transferred between institutions.

Artificial Intelligence↗
PubMed2000

An ontological approach for the development of shareable guidelines.

Computer-based clinical guidelines and protocols are being increasingly applied in diverse areas. Although there is still little standardization to facilitate sharing, various parties are engaged in the development of shareable guideline representation formalisms and corresponding decision support systems. This paper mentions some of these developed representations, discusses their pros en cons, and demonstrates and discusses a new approach, which combines common elements from earlier-developed formalisms with new ones to improve the reusability and shareability of developed guidelines. An ontological representation is presented that formalizes guidelines in terms of domain-specific knowledge and employed generic strategies that use this domain-specific knowledge in order to solve particular guideline tasks. Furthermore, a framework is described that supports this representation and three examples are shown of guidelines of various granularity and complexity that were developed by means of this approach.

Algorithms↗
PubMed2005

Identifying reasoning strategies in medical decision making: a methodological guide.

Reasoning strategies are a key component in many medical tasks, including decision making, clinical problem solving, and understanding of medical texts. Identification of reasoning strategies used by clinicians may prove critical to the optimal design of decision support systems. This paper presents a formal method of cognitive-semantic analysis for the identification and characterization of reasoning strategies deployed in medical tasks and demonstrates its use through specific examples. Although semantic analysis was originally developed in the investigation of knowledge structures, it can also be applied to identify the reasoning and decision processes used by physicians and medical trainees in clinical tasks. Assumptions underlying the methods, as well as illustrations of their use in diagnostic explanation tasks, are presented. We discuss semantic analysis in the context of the current interests in developing medical ontologies and argue that a frame-based propositional analytic methodology can provide a systematic way of addressing the construction of such ontologies. Although the application of propositional analysis methods has some limitations, we show how such limitations are being addressed and present some examples of information tools that have been developed to ease, and make more systematic, the process of analysis.

Artificial Intelligence↗
PubMed2004

Fuzzy utility and equilibria.

A decision maker is frequently confronted with fuzzy constraints, fuzzy utility maximization, and fuzziness about the state of competitors. In this paper we present a framework for fuzzy decision-making, using techniques from fuzzy logic, game theory, and micro-economics. In the first part, we study the rationality of fuzzy choice. We introduce fuzzy constraints, and show that this can easily be combined with maximizing a fuzzy utility. The second part of the paper analyzes games with uncertainty about the state of the competitors. We implement fuzzy Cournot adjustment, define equilibria, and study their stability. Finally, we show how a play progresses where the players have uncertainty about the state of the other players, and about their utility. For a likely procedure of utility maximization, the equilibria are the same as for the game without utility maximization.

Algorithms↗
PubMed2001

Sharable computer-based clinical practice guidelines: rationale, obstacles, approaches, and prospects.

Clinical practice guideline automation at the point of care is of growing interest, yet most guidelines are authored in unstructured narrative form. Computer-based execution depends on a formal structured representation, and also faces a number of other challenges at all stages of the guideline lifecycle: modeling, authoring, dissemination, implementation, and update. This is because of the multiplicity of conceptual models, authoring tools, authoring approaches, intended applications, implementation platforms, and local interface requirements and operational constraints. Complexity and time required for development and structure are also huge obstacles. These factors argue for convergence on a common shared model for representation that can be the basis of dissemination. A common model would facilitate direct interpretation or mapping to multiple implementation environments. GLIF (GuideLine Interchange Format) is a formal representation model for guidelines, created by the InterMed Collaboratory as a proposed basis for a shared representation. GLIF currently addresses the process of authoring and dissemination; the InterMed team's major focus now is on tools to facilitate these tasks and the mapping to clinical information system environments. Because of limitations in what can be done by a single team with finite resources, however, and the variety of additional perspectives that need to be accommodated, the InterMed team has determined that further development of a shared representation would be best served as an open process in which the world community is engaged. Under the auspices of the HL7 Decision Support Technical Committee, a GLIF Special Interest Group has been established, which is intended to be a forum for collaborative refinement and extension of a standard representation that can support the needs of the guideline lifecycle. Significant areas for future work will need to include demonstrations of effective means for incorporating guide-lines at point of care, reconciliation of functional requirements of different models and identification of those most important for supporting practical implementation, im-proved means for authoring and management of complexity, and methods for automatically analyzing and validating syntax, semantics, and logical consistency of guidelines.

Artificial Intelligence↗
PubMed2001

Health informatics.

Health informatics is the development and assessment of methods and systems for the acquisition, processing and interpretation of patient data with the help of knowledge from scientific research. This definition implies that health informatics is not tied to the application of computers but more generally to the entire management of information in healthcare. The focus is the patient and the process of care. The apparent information overload and the imperfection of medical decision making motivate the use of information systems for medical decision support. Health informatics provides tools to control processes in healthcare, acquire medical knowledge and communicate information between all people and organisations involved with healthcare. Although the development of medical information systems may often lag behind the available possibilities, the technological state of the current medical information systems is better than it is generally held to be. Health informatics should help healthcare professionals to provide better and more cost-effective care and enable healthcare systems to be more efficient and to adapt better to our patients' needs. Health informatics may reshape the way we deliver care to meet the demands of the future.

Artificial Intelligence↗
PubMed1998

Knowledge discovery and data mining to assist natural language understanding.

As natural language processing systems become more frequent in clinical use, methods for interpreting the output of these programs become increasingly important. These methods require the effort of a domain expert, who must build specific queries and rules for interpreting the processor output. Knowledge discovery and data mining tools can be used instead of a domain expert to automatically generate these queries and rules. C5.0, a decision tree generator, was used to create a rule base for a natural language understanding system. A general-purpose natural language processor using this rule base was tested on a set of 200 chest radiograph reports. When a small set of reports, classified by physicians, was used as the training set, the generated rule base performed as well as lay persons, but worse than physicians. When a larger set of reports, using ICD9 coding to classify the set, was used for training the system, the rule base performed worse than the physicians and lay persons. It appears that a larger, more accurate training set is needed to increase performance of the method.

Artificial Intelligence↗
PubMedDate not supplied

Decision support for drug prescription integrated with computer-based patient records in primary care.

A conceptual model of an information system that integrates a controlled vocabulary, a patient database, and a knowledge base is described. Methods, design and components for the implementation of the system are discussed. It is argued that the key issue for the successful introduction of computer-based decision support in primary care today is integration with a computer-based patient record. Also important is that the knowledge acquisition process is based on the general practitioner's real needs. This has been achieved by, first, providing general practitioners with real patient data from a series of retrospective database studies; and second, letting a panel of general practitioners select, discuss and decide which computer reminders to implement. A hybrid representation scheme was chosen for the knowledge base. The combination of a standard procedural representation (the so-called Arden syntax) for the reminder knowledge with a semantic net representation for the medical factual knowledge facilitates knowledge sharing with other systems and knowledge reuse within the system.

Artificial Intelligence↗
PubMed1995

Temporal reasoning abstractions in QMR.

A medical decision-support system (MDSS) employs either explicit or implicit temporal representation and reasoning (TRR). In this paper we first examine the factors that make explicit TRR necessary. We argue that for diagnostic MDSSs in large domains, such as internal medicine, implicit TRR is often sufficient for acceptable diagnostic performance. A necessary prerequisite for implementing implicit TRR is the identification of a set of proper TRR abstractions. We analyze the implicit TRR utilized in QMR, a MDSS operating in the domain of general internal medicine, and describe three classes of TRR abstractions. We discuss our findings in relation to work on temporal reasoning in medical informatics.

Animals↗
PubMed1996

Rule induction and instance-based learning applied in medical diagnosis.

Machine learning methods have been applied in a variety of medical domains in order to improve medical decision making. Improved medical diagnosis and prognosis can be achieved through automatic analysis of patient data stored in medical records, i.e., by learning from past experience. Given patient records with corresponding diagnoses, machine learning methods are able to classify new cases either through constructing explicit rules that generalize the training cases (e.g., rule induction) or by storing (some of) the training cases for reference (instance-based learning). This paper presents the methodologies of rule induction and instance-based learning and their application to medical diagnosis, in particular, the problem of early diagnosis of rheumatic diseases. It also discusses the possibility to use existing expert knowledge to support the learning process and the utility of such knowledge.

Algorithms↗
PubMed2002

Challenges in implementing a knowledge editor for the Arden Syntax: knowledge base maintenance and standardization of database linkages.

CONTEXT: Incorporation of research findings into clinical practice lags behind their dissemination in the medical literature. Arden Syntax is a standard that could be used to encode evidence in a clinical decision support system (CDSS). However, dissemination of knowledge is hampered by lack of standard linkages to clinical databases. OBJECTIVE: To create a knowledge editor that facilitates transfer of knowledge from the medical literature to clinical practice via a CDSS. METHODS: Using a Web browser-based application, we implemented linkages to MEDLINE to permit queries on demand and registration of queries to be executed periodically, with results copied into Arden Medical Logic Modules (MLMs). To facilitate standardization of MLMs, database linkages are encoded using emerging HL7 standards such as a data model (virtual medical record). CONCLUSIONS: A Web-based application can facilitate transfer of knowledge into clinical practice and knowledge base maintenance through periodic queries and deployment of standards for knowledge representation.

Artificial Intelligence↗
PubMed1991

Ranking radiotherapy treatment plans using decision-analytic and heuristic techniques.

Radiotherapy treatment optimization is done by generating a set of tentative treatment plans, evaluating them and selecting the plan closest to achieving a set of conflicting treatment objectives. The evaluation of potential plans involves making tradeoffs among competing possible outcomes. Multiattribute decision theory provides a framework for specifying such tradeoffs and using them to select optimal actions. Using these concepts, we have developed a plan-ranking model which ranks a set of tentative treatment plans from best to worst. Heuristics are used to refine this model so that it reflects the clinical condition of the patient being treated and the practice preferences of the physician prescribing the treatment. A figure of merit is computed for each tentative plan, and is used to rank the plans. The approach described is very general and can be used for other medical domains having similar characteristics. The figure of merit can also be used as an objective function by computer programs that attempt to automatically generate an optimal treatment plan.

Artificial Intelligence↗
PubMed2001

Patient safety in guideline-based decision support for hypertension management: ATHENA DSS.

The Institute of Medicine recently issued a landmark report on medical error.1 In the penumbra of this report, every aspect of health care is subject to new scrutiny regarding patient safety. Informatics technology can support patient safety by correcting problems inherent in older technology; however, new information technology can also contribute to new sources of error. We report here a categorization of possible errors that may arise in deploying a system designed to give guideline-based advice on prescribing drugs, an approach to anticipating these errors in an automated guideline system, and design features to minimize errors and thereby maximize patient safety. Our guideline implementation system, based on the EON architecture, provides a framework for a knowledge base that is sufficiently comprehensive to incorporate safety information, and that is easily reviewed and updated by clinician-experts.

Artificial Intelligence↗
PubMed2001

Towards integration of clinical decision support in commercial hospital information systems using distributed, reusable software and knowledge components.

PROBLEM: Clinicians' acceptance of clinical decision support depends on its workflow-oriented, context-sensitive accessibility and availability at the point of care, integrated into the Electronic Patient Record (EPR). Commercially available Hospital Information Systems (HIS) often focus on administrative tasks and mostly do not provide additional knowledge based functionality. Their traditionally monolithic and closed software architecture encumbers integration of and interaction with external software modules. Our aim was to develop methods and interfaces to integrate knowledge sources into two different commercial hospital information systems to provide the best decision support possible within the context of available patient data. METHODS: An existing, proven standalone scoring system for acute abdominal pain was supplemented by a communication interface. In both HIS we defined data entry forms and developed individual and reusable mechanisms for data exchange with external software modules. We designed an additional knowledge support frontend which controls data exchange between HIS and the knowledge modules. Finally, we added guidelines and algorithms to the knowledge library. RESULTS: Despite some major drawbacks which resulted mainly from the HIS' closed software architectures we showed exemplary, how external knowledge support can be integrated almost seamlessly into different commercial HIS. This paper describes the prototypical design and current implementation and discusses our experiences.

Abdominal Pain↗
PubMed2003

Development of a knowledge-based decision support system for identifying adequate wastewater treatment for small communities.

The identification of adequate treatment for small communities is a complex problem since it makes it necessary to combine aspects of the community and landscape, the receiving environment, and the available wastewater treatment technologies. This paper presents the development and implementation of a Knowledge-Based Decision Support System (KB-DSS) to tackle this problem. Different knowledge sources have been consulted in order to make up a comprehensive and accurate knowledge base. The core of the KB-DSS embraces two objectives. The first one is to assist in the selection of the treatment level adequate to fulfil the target quality standards for the receiving environment. The second one is to select the specific type of treatment. The KB-DSS is being applied to each one of the 3,482 different small communities comprised in the Small Communities Wastewater Treatment Plan of Catalonia, grouped according to river catchments. This paper also summarizes the different steps involved in the operation of the knowledge-based DSS when solving a real case study.

Artificial Intelligence↗
PubMed1991

The Columbia-Presbyterian Medical Center decision-support system as a model for implementing the Arden Syntax.

Columbia-Presbyterian Medical Center is implementing a decision-support system based on the Arden Syntax for Medical Logic Modules (MLM's). The system uses a compiler-interpreter pair. MLM's are first compiled into pseudo-codes, which are instructions for a virtual machine. The MLM's are then executed using an interpreter that emulates the virtual machine. This design has resulted in increased portability, easier debugging and verification, and more compact compiled MLM's. The time spent interpreting the MLM pseudo-codes has been found to be insignificant compared to database accesses. The compiler, which is written using the tools "lex" and "yacc," optimizes MLM's by minimizing the number of database accesses. The interpreter emulates a stack-oriented machine. A phased implementation of the syntax was used to speed the development of the system.

Artificial Intelligence↗
PubMed1994

A temporal analysis of QMR: abstracted temporal representation and reasoning and initial assessment of diagnostic performance trade-offs.

Explicit temporal representation and reasoning (TRR) in medical decision-support systems (MDSS) is generally considered to be a useful but often neglected aspect of system design and implementation. Given the great burden of explicit TRR both in knowledge acquisition and computational efficiency, developers of general-purpose large-scale systems typically utilize implicit (i.e., abstracted) forms of TRR. We are interested in understanding better the trade-offs of not incorporating explicit TRR in large general-purpose MDSS along the dimensions of system expressive power and diagnostic accuracy. In particular, we examine the types of abstracted TRR employed in QMR, a diagnostic system in the domain of general internal medicine, and the high-level effects of such an implicit treatment of time in the system's diagnostic performance. We present our findings and discuss implications for MDSS design and implementation practices.

Artificial Intelligence↗