A decision support model for cost-effectiveness of radical prostatectomy in localized prostate cancer.

Objective: This study aimed to develop a probabilistic decision support model to calculate the lifetime incremental cost-effectiveness ratio (ICER) between radical prostatectomy and watchful waiting for different patient groups.

Material And Methods: A randomized trial (SPCG-4) provided most data for this study. Data on survival, costs and quality of life were inputs in a decision analysis, and a decision support model was developed.

The nature of unintended effects of health information systems concerning patient safety: a systematic review with thematic synthesis.

In order to understand the nature and causes through which Health Information Systems (HIS) can affect patient safety negatively, a systematic review with thematic synthesis of the qualitative studies was performed. 26 papers met our criteria and were included into content analysis. 40 error contributing factors in working with HIS were recognized.

Non-compliance with a postmastectomy radiotherapy guideline: decision tree and cause analysis.

Background: The guideline for postmastectomy radiotherapy (PMRT), which is prescribed to reduce recurrence of breast cancer in the chest wall and improve overall survival, is not always followed. Identifying and extracting important patterns of non-compliance are crucial in maintaining the quality of care in Oncology.

Methods: Analysis of 759 patients with malignant breast cancer using decision tree induction (DTI) found patterns of non-compliance with the guideline.

A data mining approach to analyze non-compliance with a guideline for the treatment of breast cancer.

Postmastectomy radiotherapy (PMRT) is prescribed in order to reduce the local recurrence of breast cancer and improve overall survival. A guideline supports the trade-off between benefits and adverse effects of PMRT. However, this guideline is not always followed in practice.

Predicting metastasis in breast cancer: comparing a decision tree with domain experts.

Breast malignancy is the second most common cause of cancer death among women in Western countries. Identifying high-risk patients is vital in order to provide them with specialized treatment. In some situations, such as when access to experienced oncologists is not possible, decision support methods can be helpful in predicting the recurrence of cancer.

An approach for generating fuzzy rules from decision trees.

Identifying high-risk breast cancer patients is vital both for clinicians and for patients. Some variables for identifying these patients such as tumor size are good candidates for fuzzification. In this study, Decision Tree Induction (DTI) has been applied to 3949 female breast cancer patients and crisp If-Then rules has been acquired from the resulting tree.

Canonical correlation analysis for data reduction in data mining applied to predictive models for breast cancer recurrence.

Data mining methods can be used for extracting specific medical knowledge such as important predictors for recurrence of breast cancer in pertinent data material. However, when there is a huge quantity of variables in the data material it is first necessary to identify and select important variables. In this study we present a preprocessing method for selecting important variables in a dataset prior to building a predictive model.

Exploring cancer register data to find risk factors for recurrence of breast cancer--application of Canonical Correlation Analysis.

Background: A common approach in exploring register data is to find relationships between outcomes and predictors by using multiple regression analysis (MRA). If there is more than one outcome variable, the analysis must then be repeated, and the results combined in some arbitrary fashion. In contrast, Canonical Correlation Analysis (CCA) has the ability to analyze multiple outcomes at the same time.

Requirements and prototyping of a home health care application based on emerging JAVA technology.

IT support for home health care is an expanding area within health care IT development. Home health care differs from other in- or outpatient care delivery forms in a number of ways, and thus, the introduction of home health care applications must be based on a rigorous analysis of necessary requirements to secure safe and reliable health care. This article reports early experiences from the development of a home health care application based on emerging JAVA technologies.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.