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.

Artificial Intelligence↗

PubMedDate not supplied

Statistical models for prediction of arterial oxygen and carbon dioxide tensions during mechanical ventilation.

The possibility of constructing statistical models for prediction of alveolar oxygen and carbon dioxide tensions has been investigated in 20 mechanically ventilated patients in acute respiratory failure (ARF). Linear multiple regression analysis using PaCO2 and PaO2 as dependent variables was used to construct (a) models for individual patients, (b) models for specific diagnostic groups and (c) general models (all patients). The coefficient of determination (R2) was highest for the individual patient models (0.38-0.99) and lowest for the general models (0.28-0.49). In order to achieve a high predictive accuracy, models matching individual patients should be constructed on the basis of initial invasive blood gas measurement. Statistically derived models may bring better understanding of the behaviour of factors influencing arterial gas tensions in ARF and may be of value in the management of patients on mechanical ventilation.

Adult↗

PubMedDate not supplied

Epidemiology of myasthenia gravis: a population-based study in Stockholm, Sweden.

A regional database of myasthenia gravis (MG) patients was used to estimate the prevalence and selected characteristics of the disease in the county of Stockholm, Sweden. The prevalence of MG was 14.1/100,000 (17.1 for women and 10.8 for men). The mean age at onset for women and men was 34.9 and 48.5 years, respectively. About 60% of patients were diagnosed within the first year after initial symptoms. Generalized MG was found in 79% of patients, and 10% had severe symptoms. Almost two thirds of the patients had undergone thymectomy, and 30% needed immunosuppressive treatment. The increase in the prevalence of MG since the 1960s probably reflects an improvement in prognosis and higher detection rates of patients with milder symptoms. A delay in diagnosis indicates that early signs and symptoms of MG are still not well known by all doctors.

Adolescent↗

PubMedDate not supplied

Assessment of prognosis in acute myocardial infarction by computer analysis of cardio-respiratory variables.

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Acute Disease↗