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At least 163 records · Page 9Linked to original sources
PubMed1995

Development of a controlled medical terminology: knowledge acquisition and knowledge representation.

The creation of controlled medical terminologies is a central challenge in the development of electronic patient records. In the T-Helper patient-record system, designed for the care of patients with HIV disease, the IVORY module allows health-care workers to compose textual progress notes by making selections from menus generated automatically from a controlled medical terminology. Construction of this IVORY terminology required extensive design sessions with a team of computer scientists and an expert physician. Refinement of the terminology was only possible when the design team could envision how the completed T-Helper system would be used in the context of clinical practice. Development of controlled medical terminologies is a significant problem in knowledge acquisition. Techniques used to acquire and represent clinical concepts for the purpose of building decision-support systems also are appropriate for the construction of controlled terminologies such as the one in T-Helper.

Artificial Intelligence↗
PubMed2005

Clinical decision-support for diagnosing stress-related disorders by applying psychophysiological medical knowledge to an instance-based learning system.

OBJECTIVE: An important procedure in diagnosing stress-related disorders caused by dysfunction in the interaction of the heart with breathing, i.e., respiratory sinus arrhythmia (RSA), is to analyse the breathing first and then the heart rate. Analysing these measurements is a time-consuming task for the diagnosing clinician. A decision-support system in this area would reduce the analysis task of the clinician and enable him/her to give more attention to the patient. We have created a decision-support system which contains a signal classifier and a pattern identifier. The system performs an analysis of the physiological time series concerned which would otherwise be performed manually by the clinician. METHODS: The signal-classifier, HR3Modul, classifies heart-rate patterns by analysing both cardio- and pulmonary signals, i.e., physiological time series. HR3Modul uses case-based reasoning (CBR), using a wavelet-based method for retrieving features from the signals. The system searches for familiar shapes in the signals by comparing them with shapes already stored. We have applied a best fit scheme for handling signals of different lengths, as the length of a breath is highly dynamic. We also apply automatic weighting to the features to obtain a more autonomous system. The classified heart signals indicate if a patient may be suffering from a stress-related disorder and the nature of the disorder. These classified signals are thereafter sent to the second subsystem, the pattern-identifier. The pattern-identifier analyses the classified signals and searches for familiar patterns by identifying sequences in the classified signals. The identified sequences give clinicians a more complete analysis of the measurements, providing them with a better basis for diagnosis. RESULTS AND CONCLUSION: We have shown that a case-based classifier with a wavelet feature extractor and automatic weighting is a viable option for building a decision-support system for the psychophysiological domain, as it is at par, or even outperforms other retrieval techniques and is less complex.

Algorithms↗
PubMedDate not supplied

Computer-assisted management of primary open-angle glaucoma. Knowledge acquisition and prototype testing.

Primary open-angle glaucoma is a common disease afflicting 1% to 2% of people older than 50 years of age. The care of patients with glaucoma is a subject of debate because the disease is incompletely understood. The diagnosis relies on a number of examinations, many of them performed by ophthalmic nurses, and the care of patients with glaucoma has become one of the main tasks for ophthalmic nurses in Sweden. This study describes a knowledge-based system for decision support in glaucoma management, which uses seven data elements about the patient to arrive at one of 25 different recommendations for appropriate action. In 267 patient visits to five different eye clinics, the program recommendations were compared with the actual decisions made by the responsible physician. The concordance was 92% to 100% when policy differences among the clinics were taken into account. The program appears to provide substantial decision support in the management of primary open-angle glaucoma. The program's ability to support the ophthalmic nurses in the care of patients with open-angle glaucoma is being evaluated.

Aged↗
PubMed1990

Using hindsight in medical decision making.

As the clinical picture of a patient evolves over time, more information becomes available. Certain procedure require time to perform, causing delay between the time when the tests are ordered and when the results are available. Furthermore, as the patient's condition changes over time, serial measurements can be made. The availability of more data allows a more accurate assessment of the patient. Uncertainties, guesses or errors that were made early in the clinical course of patient care can also be identified and resolved when more information is available. Reasoning with a stream of data that changes over time presents a challenge to the designers of expert systems. The use of hindsight in expert system requires that appropriate attention be paid to the temporal relations of the data and that care is exercised in revising decision. I present a data-dependency system, the Temporal Control Structure (TCS), designed to support reasoning with data changing over time and show how it can be used to implement reasoning by hindsight.

Artificial Intelligence↗
PubMed2002

Representation primitives, process models and patient data in computer-interpretable clinical practice guidelines: a literature review of guideline representation models.

Representation of clinical practice guidelines in a computer-interpretable format is a critical issue for guideline development, implementation, and evaluation. We studied 11 types of guideline representation models that can be used to encode guidelines in computer-interpretable formats. We have consistently found in all reviewed models that primitives for representation of actions and decisions are necessary components of a guideline representation model. Patient states and execution states are important concepts that closely relate to each other. Scheduling constraints on representation primitives can be modeled as sequences, concurrences, alternatives, and loops in a guideline's application process. Nesting of guidelines provides multiple views to a guideline with different granularities. Integration of guidelines with electronic medical records can be facilitated by the introduction of a formal model for patient data. Data collection, decision, patient state, and intervention constitute four basic types of primitives in a guideline's logic flow. Decisions clarify our understanding on a patient's clinical state, while interventions lead to the change from one patient state to another.

Artificial Intelligence↗
PubMed1998

Health care workers and their needs: the forgotten shadow of AIM research.

The field of AI in Medicine (AIM) seems to have accepted that decision support is, and will be, needed within most medical domains. As society calls for cost-effectiveness, and human expertise or expert guidance are not always available, decision support systems (DSSs) are proposed as the solutions. These solutions, however, do not necessarily correspond with the basic needs of their targeted users. We will show this through a review of the literature related to health care workers and the various factors that have an influence on their performances. Furthermore, we will use these empirical findings to argue that the AIM community must go beyond its decision support philosophy, whereby the gaps in human expertise are filled in by the computer. In the future, joint emphasis must be placed on decision support and the promotion towards independent and self-sufficient problem solving. In order to implement this paradigm change, the AIM community will have to incorporate findings from the research discipline of AI in Education.

Artificial Intelligence↗
PubMed2006

Prediction of signal peptides using bio-basis function neural networks and decision trees.

Signal peptide identification is of immense importance in drug design. Accurate identification of signal peptides is the first critical step to be able to change the direction of the targeting proteins and use the designed drug to target a specific organelle to correct a defect. Because experimental identification is the most accurate method, but is expensive and time-consuming, an efficient and affordable automated system is of great interest. In this article, we propose using an adapted neural network, called a bio-basis function neural network, and decision trees for predicting signal peptides. The bio-basis function neural network model and decision trees achieved 97.16% and 97.63% accuracy respectively, demonstrating that the methods work well for the prediction of signal peptides. Moreover, decision trees revealed that position P(1'), which is important in forming signal peptides, most commonly comprises either leucine or alanine. This concurs with the (P(3)-P(1)-P(1')) coupling model.

Algorithms↗
PubMed2001

Integration of textual guideline documents with formal guideline knowledge bases.

Numerous approaches have been proposed to integrate the text of guideline documents with guideline-based care systems. Current approaches range from serving marked up guideline text documents to generating advisories using complex guideline knowledge bases. These approaches have integration problems mainly because they tend to rigidly link the knowledge base with text. We are developing a bridge approach that uses an information retrieval technology. The new approach facilitates a versatile decision-support system by using flexible links between the formal structures of the knowledge base and the natural language style of the guideline text.

Artificial Intelligence↗
PubMed1997

A decision-driven design of a decision support system in anesthesia.

We present a new approach to the design of a decision support system (DSS) in anesthesia which converts the available data to relevant information. Instead of a patient-driven design (patient modelling), we use a decision-driven design (anesthetist modelling). This approach results in a system consisting of three stages. First, the incoming data is validated to ensure reliable further processing for both DSS and anesthetist. Second, the validated data is analyzed to detect patterns that could trigger the anesthetist's response. Finally, from these patterns a strategic selection is made and offered to the anesthetist as information relevant for the decision that has to be made in the current context. Results show that this approach is feasible, but further evaluation is necessary before practical application is possible.

Algorithms↗
PubMed1994

Rationale for the Arden Syntax.

The Arden Syntax, a language designed for writing and sharing task-specific knowledge for Medical Logic Modules (MLMs), has been recently accepted as a standard by the ASTM. The syntax is concerned with the critical task of sharing medical knowledge bases across many institutions. Because of the relative lack of agreement on vocabularies and data standards and because of the many other obstacles, the developers of the Arden Syntax took a pragmatic, straightforward approach that has borne fruit in a very short period of time. The syntax provides a vehicle for the health care community to begin sharing, so that we can see what works and what does not work, and we can begin to address the critical obstacles. In designing a language like the Arden Syntax, the authors make many decisions--but the final document gives only the result of these decisions without any explanation. By writing down the rationale behind the design of the syntax, we hope to aid users of the language, implementors of the language, and future designers of new languages.

Artificial Intelligence↗
PubMed1990

Decision rules for the ECG diagnosis of inferior myocardial infarction.

ECG measurements from 341 patients with inferior myocardial infarction (IMI) and 327 normal subjects were used to develop and test decision rules for the ECG diagnosis of IMI. Recursive partitioning provided a simple decision rule with 75% sensitivity and 97% specificity, using Q amplitude and Q duration in a VF, Q duration in III, and T-wave axis in the frontal plane as decision variables. Dropping T-wave axis from the decision rule led to a 10% decrease in sensitivity. Multiple logistic regression provided sensitivities and specificities which were similar to those for recursive partitioning. Both methods outperformed traditional noncontour criteria for IMI.

Adult↗
PubMed2004

Rough sets and genetic algorithms in learning cellular neural networks cloning template for decision making system.

We purpose to find a new beneficial method for accelerating the Decision-Making and classifier support applied on imprecise data. This acceleration can be done by integration between Rough Sets theory, which gives us the minimal set of decision rules, and the Cellular Neural Networks. Our method depends on Genetic Algorithms for designing the cloning template for more accuracy. Some illustrative examples are given to demonstrate the effectiveness of the proposed method, whose advantages and limitations are also discussed.

Algorithms↗
PubMed1986

Computer-aided diagnostic strategy selection.

Determination of the optimal diagnostic work-up strategy for the patient is becoming a major concern for the practicing physician. Overlap of the indications for various diagnostic procedures, differences in their invasiveness or risk, and high costs have made physicians aware of the need to consider the choice of procedure carefully, as well as its relation to management actions available. In this article, the author discusses research approaches that aim toward development of formal decision analytic methods to allow the physician to determine optimal strategy; clinical algorithms or rules as guides to physician decisions; improved measures for characterizing the performance of diagnostic tests; educational tools for increasing the familiarity of physicians with the concepts underlying these measures and analytic procedures; and computer-based aids for facilitating the employment of these resources in actual clinical practice.

Artificial Intelligence↗
PubMed2004

Approaches for creating computer-interpretable guidelines that facilitate decision support.

During the last decade, studies have shown the benefits of using clinical guidelines in the practice of medicine. Although the importance of these guidelines is widely recognized, health care organizations typically pay more attention to guideline development than to guideline implementation for routine use in daily care. However, studies have shown that clinicians are often not familiar with written guidelines and do not apply them appropriately during the actual care process. Implementing guidelines in computer-based decision support systems promises to improve the acceptance and application of guidelines in daily practice because the actions and observations of health care workers are monitored and advice is generated whenever a guideline is not followed. Such implementations are increasingly applied in diverse areas such as policy development, utilization management, education, clinical trials, and workflow facilitation. Many parties are developing computer-based guidelines as well as decision support systems that incorporate these guidelines. This paper reviews generic approaches for developing and implementing computer-based guidelines that facilitate decision support. It addresses guideline representation, acquisition, verification and execution aspects. The paper describes five approaches (the Arden Syntax, GuideLine Interchange Format (GLIF), PROforma, Asbru and EON), after the approaches are compared and discussed.

Artificial Intelligence↗
PubMed2000

A knowledge-based patient assessment system: conceptual and technical design.

This paper describes the design of an inpatient patient assessment application that captures nursing assessment data using a wireless laptop computer. The primary aim of this system is to capture structured information for facilitating decision support and quality monitoring. The system also aims to improve efficiency of recording patient assessments, reduce costs, and improve discharge planning and early identification of patient learning needs. Object-oriented methods were used to elicit functional requirements and to model the proposed system. A tools-based development approach is being used to facilitate rapid development and easy modification of assessment items and rules for decision support. Criteria for evaluation include perceived utility by clinician users, validity of decision support rules, time spent recording assessments, and perceived utility of aggregate reports for quality monitoring.

Artificial Intelligence↗
PubMed1998

Patient Dependency Knowledge-Based Systems.

The ability of Patient Dependency Systems to provide information for staffing decisions and budgetary development has been demonstrated. In addition, they have become powerful tools in modern hospital management. This growing interest in Patient Dependency Systems has renewed calls for their automation. As advances in Information Technology and in particular Knowledge-Based Engineering reach new heights, hospitals can no longer afford to ignore the potential benefits obtainable from developing and implementing Patient Dependency Knowledge-Based Systems. Experience has shown that the vast majority of decisions and rules used in the Patient Dependency method are too complex to capture in the form of a traditional programming language. Furthermore, the conventional Patient Dependency Information System automates the simple and rigid bookkeeping functions. On the other hand Knowledge-Based Systems automate complex decision making and judgmental processes and therefore are the appropriate technology for automating the Patient Dependency method. In this paper a new technique to automate Patient Dependency Systems using knowledge processing is presented. In this approach all Patient Dependency factors have been translated into a set of Decision Rules suitable for use in a Knowledge-Based System. The system is capable of providing the decision-maker with a number of scenarios and their possible outcomes. This paper also presents the development of Patient Dependency Knowledge-Based Systems, which can be used in allocating and evaluating resources and nursing staff in hospitals on the basis of patients' needs.

Accounting↗
PubMed1995

Informatics for care protocols and guidelines: towards a European knowledge model.

The DILEMMA Generic Protocol Model (DGPM) is a trans-national ontology of clinical protocols designed using a blend of logic engineering and business modelling techniques, and developed as part of the AIM programme's DILEMMA project. It allows the declarative representation of clinical activities and the knowledge associated with them. It is being used to represent protocols of all sorts-including standards and guidelines-for use in acute, primary, home and shared care. Central to the model are the states that protocol-derived actions can assume, and the statements that must be true before transitions between those states can be proposed. Proposed transitions may then be scrutinised by clinicians, patients and carers, and approved or rejected. This approach enables the model to handle anticipated exception situations, as well as more normal protocol selection and application. Links to multi-media material are being investigated, to enable users to examine the evidence upon which protocols are based, and to provide decision support where deterministic reasoning is not readily available. The model is being discussed with other AIM projects, with a view to developing a European consensus, and generating a version of the model for submission to CEN TC251 as a European pre-normative standard.

Artificial Intelligence↗
PubMed1995

Using intermediate states to improve the ability of the Arden Syntax to implement care plans and reuse knowledge.

The Arden Syntax is one of a few knowledge representation languages currently in use for clinical decision support. While some of these languages are being used in active patient care settings, none have gained widespread acceptance as a clinical tool. Prior attempts to represent temporally complex care plans in the Arden Syntax have revealed difficulties in representing and tracking series of consecutive time-oriented events and recommendations, in sharing and reusing knowledge and in dealing with unobtainable data. In an attempt to improve Arden's ability to deal with these problems and demonstrate the importance of these factors, the clinical event monitor has been adapted to store coded data representing Intermediate States in the Columbia Presbyterian Medical Center (CPMC) central data repository. The Intermediate States define the current state of the patient as laid out in the care plan. Four care plans were constructed. The findings include an improved ability to track complex series of events and recommendations over long periods of time. The knowledge generated by the electronic care plans was able to be reused by the care plan that generated it, by other elements of the knowledge base and by non-decision support applications. Modular development, facilitated by the changes, simplified dealing with data not available to the central data repository by aiding the implementation of those parts of the care plan for which sufficient data is available.

Artificial Intelligence↗