Evaluation of a knowledge-based decision-support system for ventilator therapy management.

Evaluation of knowledge-based systems differs from that of conventional systems in terms of verification and validation techniques. Furthermore, evaluating medical decision-support systems is difficult because the field is thus far comparatively unexplored. This paper presents an evaluation of a medical knowledge-based system called VentEx that supports decision-making in the management of ventilator therapy. Real patient data from 1300 hours of patient care involving 12 patients with 6 diagnoses are used to validate the knowledge base. The results range from 4.5% to 15.6% disagreement between the setting recommendations produced by VentEx and a gold standard, and 22.2% disagreement for recommendations for weaning. A comparison between the standard and two physicians showed that VentEx produced advice of the same quality as the physicians.

A decision support system for diabetes care: ethical aspects.

In this paper the design and implementation of a decision support system for diabetes care is examined from an ethical perspective. It is noted that the system creates potential for enhancing the realization of the principle of autonomy through improved information to patients and participation by patients. However, there is also potential for using the system in a way that is contrary to good health care. It may provide patients with information they are unable to interpret and handle, and it may be used by healthcare authorities for controlling their personnel in ways contrary to good quality working conditions. In order for a decision support system to function as a well-integrated element in ethically based health care, different ethical aspects have to be taken into account during the design of the system. The ethical aspects also constitute one perspective of a systematic re-evaluation and re-design process.

Database and knowledge base integration--a data mapping method for Arden Syntax knowledge modules.

One of the most important categories of decision-support systems in medicine are data driven systems where the inference engine is linked to a database. It is, therefore, important to find methods that facilitate the implementation of database queries referred to in the knowledge modules. A method is described for linking clinical databases to a knowledge base with Arden Syntax modules. The method is based on a query meta-database including templates for SQL queries which is maintained by a database administrator. During knowledge module authoring the medical expert refers only to a code in the query meta-database; no knowledge is needed about the database model or the naming of attributes and relations. The method uses standard tools, such as C+2 and ODBC, which makes it possible to implement the method at many platforms and to link to different clinical databases in a standardized way.

Extended telemedical consultation using Arden Syntax based decision support, hypertext and WWW technique.

There is an obvious need for geographic distribution of expert knowledge among several health care units without increasing the cost of on-site expertise in locations where health care is provided. This paper describes the design of a knowledge-based decision-support system for extended consultation in clinical medicine. The system is based on Arden Syntax for Medical Logic Modules and hypertext using World Wide Web technology. It provides advice and explanations regarding the given advice. The explanations are presented in a hypertext format allowing the user to browse related information and to verify the relevance of the given advice. The system is intended to be used in a closed local network. With special precautions regarding issues of safety and patient security, the system can be used over wider areas such as in rural medicine. A prototype has been developed in the field of clinical microbiology and infectious diseases regarding infective endocarditis.

Kave: a tool for knowledge acquisition to support artificial ventilation.

A decision support system for artificial ventilation is being developed. One of the fundamental goals for this system is the application of the system when a domain expert is not present. Such a system requires a rich knowledge base. The knowledge acquisition process is often considered to be the bottleneck in acquiring such a complete knowledge base. Since no single available method, for example interviewing domain experts, is sufficient for removing this bottleneck, we have chosen a combination of different methods. The different backgrounds of knowledge engineers and domain experts could cause communication restrictions and difficulties between them, e.g. they might not understand each others knowledge domain and this will affect formulation of the knowledge. To solve this problem we needed a tool which supports both the knowledge engineer and the domain expert already from the initial phase of developing the knowledge base. We have developed a knowledge acquisition system called KAVE to elicit knowledge from domain experts and storing it in the knowledge base. KAVE is based on a domain specific conceptual model which is a result of cooperation between knowledge engineers and domain experts during identification, design and structuring of knowledge for this domain. KAVE includes a patient simulator to help validate knowledge in the knowledge base and a knowledge editor to facilitate refinement and maintenance of the knowledge base.

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.

Pre-compiling medical logic modules into C++ in building medical decision support systems.

Development of medical knowledge bases is a time-consuming process, and no single medical institution can develop medical knowledge bases covering all areas of medicine. The use of medical knowledge representation standards such as the Arden Syntax is an attempt to enhance the writability and readability of computer-stored knowledge and facilitate transfer and sharing among institutions. A method for the realisation of decision support systems based on knowledge formulated according to the Arden Syntax is presented. An essential tool in this process is a medical logic module (MLM) pre-compiler, translating MLMs into an object-oriented programming language, C++. Advantages of the C++ approach compared with other alternatives are discussed.

VentEx: an on-line knowledge-based system to support ventilator management.

This paper will demonstrate the clinical application of a knowledge-based decision-support system called VentEx for ventilator management. VentEx has been implemented using a knowledge-based development tool on a PC under the Microsoft Windows multitasking environment. It is integrated into a computer aided ventilator system including the Siemens Elema Servo Ventilator 900 C equipped with a Servo Computer Module 990 and the CO2 analyser 930. The system provides advanced ventilator monitoring with expert advice concerning ventilator strategy and settings based on data from on-line monitoring. The knowledge base has been primarily validated and the system has been clinically tested by the intensive care unit staff. Different approaches such as knowledge acquisition, representation and system integration have been outlined and discussed.

Database and knowledge base integration in decision support systems.

Since decision support systems (DSS) in medicine often are linked to clinical databases it is important to find methods that facilitate the work for DSS developers to implement database queries in the knowledge base (KB). This paper presents a method for linking clinical databases to a KB with Arden Syntax modules. The method is based on a query meta database including templates for SQL queries. During knowledge module authoring the medical expert only refers to a code in the query meta database. Our method uses standard tools so it can be implemented on different platforms and linked to different clinical databases.

Medical logic module (MLM) representation of knowledge in a ventilator treatment advisory system.

In any medical expert system it is the inherent knowledge that is the power of the system and not the particulars of its implementation. Therefore it would be valuable to use a representation that would allow: knowledge transfer between different systems, users, experts and 'importers' to be able to evaluate the logic, experts to easily input their knowledge and be guided how to use the syntax. Adren Syntax of Medical Logic Module is a proposed knowledge representation, fulfilling these criteria. The Arden Syntax has been used to represent rules and logic in a decision support system for ventilator therapy in patients with acute respiratory failure (ARF) that is under development. The medical experts involved in the project have used the Arden Syntax as a convenient way for transfer and storage of medical knowledge. The syntax is easy to learn and may be used with a minimum of training. In the present system, Medical Logical Modules have been used to represent knowledge pertinent to the initiation and maintenance phases of ventilator therapy.

Knowledge base design for decision support in respirator therapy.

A knowledge base is built for decision support applied to respirator therapy (the KUSIVAR project). The knowledge representation is object-oriented using frames to store multiple forms of knowledge: variable descriptions, transformation tables, rules and mathematical models. The system is data-driven, generating and displaying advice automatically triggered by changes in data from the respirator and the patient. The inferenceing mechanism is forward-chaining i.e. a rule is evaluated as soon as it's condition is satisfied. Temporal aspects of the reasoning are represented by a number of mechanisms, among others limited validity times for data, trend analysis and mathematical models. The knowledge base is organized according to disease groups and decision situation which simplifies knowledge acquisition and improves response times since it enables the system to focus on a limited set of rules in each situation. To test the feasibility of the system design a prototype has been built using Knowledge Engineering Environment (KEE) from Intellicorp on an Explorer workstation from Unisys. The production system, which is interfaced to a Siemens Elema Servo Ventilator 900C, is currently being implemented under the Microsoft Windows multitasking environment on a microcomputer based on an Intel 80386 processor.

Integrating knowledge-based technology into computer aided ventilation systems.

A knowledge-based decision support system for respirator treatment, the KUSIVAR system, has been designed in cooperation between hospital, university and industry. Changes in patient data from respirator and monitoring equipment trigger a computer program that generates advice to the staff concerning e.g. therapy modes and respirator settings using expert systems and process control technology. A prototype has been built on an advanced development workstation, the Unisys Explorer, using the software Knowledge Engineering Environment (KEE). The clinical version is implemented on an Intel 80396-based microcomputer connected on-line via a data-acquisition processor to the respirator. The decision support software is implemented as a module under the Microsoft Windows multitasking environment and communicates with modules for data acquisition, database handling and data presentation by means of message passing using the Windows Dynamic Data Exchange protocol. The modules present coherent user interfaces by conforming to Microsoft Windows standards. The knowledge base is being extensively validated by an expert group in the ICU and the system will be evaluated through animal experiments and clinical studies.

Integration of data driven decision support into the HELIOS environment.

The development of large-scale, clinically accepted decision support systems (DSS) calls for powerful and commonly available methods and tools for knowledge acquisition, system realisation, and knowledge base maintenance. The paper addresses problems associated with the integration of knowledge-based systems within the clinical setting with special reference to (i) data driven decision support, (ii) the Arden Syntax as a knowledge representation format and, (iii) the HELIOS software engineering environment. Architecture of a DSS based on Arden Syntax and its integration in the HELIOS environment are presented. Realisation of the DSS is discussed in relation to client-server architecture and object-oriented databases, which are essential concepts of the HELIOS environment. Sharability and reusability of the knowledge, together with commonality of used software tools are also discussed.