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Biomedical subjects

S Andreassen

Publications and source records attributed to S Andreassen.

At least 37 records · Page 2Linked to original sources
PubMed1998

Preliminary experience of the DIAS computer model in providing insulin dose advice to patients with insulin dependent diabetes.

The Diabetes Advisory System (DIAS) is a model of human glucose metabolism which predicts hourly blood glucose concentrations and provides advice on insulin dose. Its ability to provide appropriate advice was assessed in 20 well-controlled IDDM patients (mean (SD) age 38 (11), duration 17 (9) years; HbA1 8.8 (0.9)%, reference range 5.4-7.6%). Patients recorded blood glucose measurements, insulin dose and food intake for 4 days. These data were used to generate insulin dose advice by both DIAS and a diabetes specialist nurse. Patients were then allocated to follow either DIAS or nurse advice for a further 4 days. There was no significant difference in mean recorded blood glucose values or frequency of reported hypoglycaemia between the DIAS and nurse groups either before or after insulin dose adjustment. The DIAS model, however, generated significantly lower insulin dose advice than the nurse (median (range)% change in insulin dose: DIAS group -13.3% (-25.0 to +11.6) versus nurse group 0% (-8.7 to +2.5), P < 0.05). We conclude that, in the patients studied, DIAS provided insulin dose advice which maintained good short term control of diabetes, despite significant reductions in dose in some cases.

Adult↗
PubMed1998

Using a double blind controlled clinical trial to evaluate the function of a Diabetes Advisory System: a feasible approach?

This paper assesses the feasibility of using a double blind controlled clinical trial to evaluate the function of a decision support system by applying such a design to the evaluation of a Diabetes Advisory System (DIAS). DIAS is based on a model of the human carbohydrate metabolism and is designed an interactive clinical tool, which can be used to predict the effects of changes in insulin dose or food intake on the blood glucose concentration in patients with insulin dependent diabetes. It can also be used to identify risk periods for hypoglycaemia. and to provide advice on insulin dose. The latter feature was evaluated in the present study. We believe double blind controlled clinical trials are prerequisites for clinical application of many decision support systems, and conclude that the present double blind controlled clinical trial is a suitable evaluation method for the function of DIAS.

Adolescent↗
PubMed1998

DIAS-NIDDM--a model-based decision support system for insulin dose adjustment in insulin-treated subjects with NIDDM.

A decision support system has been developed, Diabetes Insulin Advisory System for patients with non-insulin dependent diabetes mellitus (DIAS-NIDDM), assisting in the adjustment of insulin doses in insulin-treated subjects. DIAS-NIDDM uses a causal probabilistic network (CPN) model of carbohydrate metabolism to make stochastic predictions of blood glucose (BG) excursions. The CPN model is an extension of an existing model with an added component representing endogenous insulin secretion. A linear relationship between BG and insulin concentration due to BG stimulated insulin secretion is assumed. Model parameters (pancreatic sensitivity, insulin sensitivity, and time-to-peak of NPH insulin) are estimated by Bayesian probability updating from patient's specific data (food intake, insulin doses, BG measurements) recorded over a period of 4 days. The estimated parameters allow the system to be potentially used as a diagnostic tool to identify abnormalities of carbohydrate metabolism: impaired insulin secretion, insulin resistance and the severity of the impairments. DIAS-NIDDM was used to predict patient-specific BG profiles and advise on insulin doses during a pilot study in eight patients with NIDDM of whom five were treated with insulin. Compared to the administered insulin amount, daily insulin amount advised by DIAS-NIDDM was similar (within 4 U) in three patients, higher by 20% (19 U) in one patient and lower by 40% (18 U) and 50% (11 U) in two patients, respectively. The inter-day coefficient of variation of the daily insulin advice suggests that, at least according to DIAS-NIDDM criteria, day-to-day adjustment of insulin doses is necessary to maintain optimum control.

Computer Simulation↗
PubMed1998

[Assessment of patients' health status--review of the literature and assessment methods].

The question often arises of how health status is to be measured, or to what end. A number of instruments have been developed for the measurement of patient health, and the article consists in a review of the methodology and specific applications of some of those most commonly used, as outlined in published reports. The review showed the defined goal (s) of investigation to constitute a crucial determinant of the choice of methods, as most of the available instruments are designed for specific situations or categories of patients. Thus, an unfortunate choice may result in sufficient or erroneous data for the purposes of decision making.

Health Status↗
PubMed1997

DIAS--the diabetes advisory system: an outline of the system and the evaluation results obtained so far.

The present paper gives a description of the Diabetes Advisory System (DIAS), and the evaluation results obtained so far. DIAS is a decision support system for the management of insulin dependent diabetes. The core of the system is a compartment model of the human carbohydrate metabolism implemented as a causal probabilistic network (CPN or Bayesian network), which gives it the ability to handle the uncertainty, for example, in blood glucose measurements or physiological variations in glucose metabolism. The evaluation results suggest that, at least in our hands, DIAS can generate advice that is safe and of a quality that is at least comparable to what is available from experienced clinicians.

Bayes Theorem↗
PubMed1997

Causal probabilistic network and power spectral estimation used in sleep stage classification.

A new method for sleep-stage classification using a causal probabilistic network as automatic classifier has been implemented and validated. The system uses features from the primary sleep signals from the brain (EEG) and the eyes (AOG) as input. From the EEG, features are derived containing spectral information which is used to classify power in the classical spectral bands, sleep spindles and K-complexes. From AOG, information on rapid eye movements is derived. Features are extracted every 2 seconds. The CPN-based sleep classifier was implemented using the HUGIN system, an application tool to handle causal probabilistic networks. The results obtained using different training approaches show agreements ranging from 68.7 to 70.7% between the system and the two experts when a pooled agreement is computed over the six subjects. As a comparison, the interrater agreement between the two experts was found to be 71.4%, measured also over the six subjects.

Adult↗
PubMed1996

Analysing the hypoglycaemic counter-regulation: a clinically relevant phenomenon?

This paper describes an analysis of the temporal relation between episodes of low blood glucose (hypoglycaemia) and counter-regulations, i.e., episodes of elevated blood glucose (hyperglycaemia), in patients with insulin dependent diabetes. The relation was assessed by statistical methods based on a metabolic computer model of the human glucose metabolism. The study material was standard collected clinical data on meals, insulin injections, and measured blood glucose from hospitalised patients. We have found that a typical hypoglycaemic counter-regulation begins 6-8 h after the hypoglycaemia, that it lasts 16-18 h, giving a total duration of 24 h, and that it elevates the blood glucose by 4-10 mmol/l. The phenomenon was demonstrated in the data from more than half of the patients with hypoglycaemic episodes.

Blood Glucose↗
PubMed1996

Use of the DIAS model to predict unrecognised hypoglycaemia in patients with insulin-dependent diabetes.

The Diabetes Advisory System (DIAS) is a model of human glucose metabolism implemented in a causal probabilistic network. It handles data on insulin dose, carbohydrate intake and blood glucose concentration to predict hourly blood glucose concentrations and thus provide an indication of blood glucose values between home blood tests. DIAS was used to predict blood glucose profiles in eight patients with well-controlled insulin-dependent diabetes, who are at increased risk of hypoglycaemia (abnormally low blood glucose levels). DIAS predicted nocturnal hypoglycaemia in six patients and daytime hypoglycaemia in one patient. The occurrence of nocturnal hypoglycaemia was not recognised by the patient or suspected by their doctor but was subsequently confirmed by blood testing in five patients. It is known that unrecognised nocturnal hypoglycaemia is common in patients with well-controlled diabetes. The ability of DIAS to identify such periods of hypoglycaemia with reasonable accuracy illustrates how the advanced technology it employs may provide reliable decision support to clinicians.

Adolescent↗
PubMed1996

Estimation of pulmonary diffusion resistance and shunt in an oxygen status model.

A compartment model of the transport of oxygen from the alveoli to the tissues is described. In patients with both pulmonary shunt and alveolar resistance to diffusion of oxygen, the model is used to simulate their response to variations in the inspired oxygen fraction. These simulation results are compared to the responses from a patient with respiratory malfunction, indicating that the method can identify patients where not only a pulmonary shunt but also a high alveolar resistance to diffusion of oxygen is clinically significant. Estimation of pulmonary shunt and oxygen diffusion resistance can be done in two different implementations of the model. In the first implementation the estimates are generated by numerical solution of the equations of the compartment model. In the second implementation the equations have been used to construct a causal probabilistic net where biological uncertainties and uncertainties in the measurements can be represented.

Airway Resistance↗
PubMed1996

Acid-base chemistry of the blood--a general model.

This paper describes a general model of acid-base chemistry of the blood which can be used to simulate physiological perturbation of acid-base chemistry on addition or removal of any buffer acid or base. In particular, it is shown how this model can be used to estimate the concentrations of buffer acid or base. In particular, it is shown how this model can be used to estimate the concentrations of buffer acids and bases when blood is equilibrated to a new pCO2, when hydrogen ions H+ are added to the blood, or when two pools of blood with different concentrations of buffer acids and bases are mixed. The ability of the model to represent the addition or removal of any acid or base is a significant increase in functionality above the Siggaard-Andersen nomogram which is limited to simulating the effects of equilibrating the blood to a new pCO2. When used to represent the situation where blood is equilibrated at a new pCO2 the model enables calculation of the amount CO2 removed during equilibration, a further increase in functionality above the Siggaard-Andersen nomogram. In two experimental situations, equilibrating blood to a new pCO2 and addition of H+ ions, the model predictions are shown to be consistent with existing experimental data in the form of the Siggaard-Andersen nomogram.

Acid-Base Equilibrium↗
PubMed1996

Evaluation of the diagnostic performance of the expert EMG assistant MUNIN.

The diagnostic performance of the medical expert system MUNIN for diagnosis of neuromuscular disorders was evaluated on a set of 30 test cases. The cases were provided by 7 experienced electromyographers who were subsequently invited to participate in the evaluation. To reasonably cover the range of disorders, the electromyographers were asked to provide cases from patients with different types of muscular dystrophy, with neuromuscular transmission disorders, with motor neurone disease, and with different types of polyneuropathies. In addition, patients with a range of local neuropathies were provided. Out of the 30 cases, 11 cases were evaluated by an "almost peer review" method and the remaining 19 cases were evaluated by a "silver standard" method. The number of cases evaluated by "almost peer review" was limited to 11 due to time constraints on the evaluation procedure. During the "almost peer review," each electromyographer was asked to diagnose patients, using a vocabulary that closely resembled MUNIN's vocabulary. Subsequently, we attempted to provide a consensus diagnosis for the patients based on discussion among the participating electromyographers. The electromyographers were also asked to assess how well MUNIN had performed in each case. The remaining 19 cases were evaluated by a "silver standard" procedure, where MUNIN's diagnosis was compared to the diagnosis of the expert who provided the case. The results indicated that MUNIN performed well, and the electromyographers considered "that MUNIN performed at the same level as an experienced neurophysiologist." In particular, it was noted that MUNIN handled cases with conflicting findings well, and that it was able to diagnose patients with multiple diseases.

Diagnostic Services↗
PubMed1994

A probabilistic approach to glucose prediction and insulin dose adjustment: description of metabolic model and pilot evaluation study.

A model of carbohydrate metabolism has been implemented as a causal probabilistic network, allowing explicit representation of the uncertainties involved in the prediction of 24-h blood glucose profiles in insulin-dependent diabetic subjects. The parameters of the model were based on experimental data from the literature describing insulin and carbohydrate absorption, renal loss of glucose, insulin-independent glucose utilisation and insulin-dependent glucose utilisation and production. The model can be adapted to the observed glucose metabolism in the individual patient and can be used to generate predicted 24-h blood glucose profiles. A penalty is assigned to each level of blood glucose, to indicate that high and low blood glucose levels are undesirable. The system can be asked to find the insulin doses that result in the most desirable 24-h blood glucose profile. In a series of 12 patients, the system predicted blood glucose with a mean error of 3.3 mmol/l. The insulin doses suggested by the system seemed reasonable and in several cases seemed more appropriate than the doses actually administered to the patients.

Absorption↗
PubMed1994

Model-based biosignal interpretation.

Two relatively new approaches to model-based biosignal interpretation, qualitative simulation and modelling by causal probabilistic networks, are compared to modelling by differential equations. A major problem in applying a model to an individual patient is the estimation of the parameters. The available observations are unlikely to allow a proper estimation of the parameters, and even if they do, the task appears to have exponential computational complexity if the model is non-linear. Causal probabilistic networks have both differential equation models and qualitative simulation as special cases, and they can provide both Bayesian and maximum-likelihood parameter estimates, in most cases in much less than exponential time. In addition, they can calculate the probabilities required for a decision-theoretical approach to medical decision support. The practical applicability of causal probabilistic networks to real medical problems is illustrated by a model of glucose metabolism which is used to adjust insulin therapy in type I diabetic patients.

Bayes Theorem↗
PubMed1993

Non-reflex and reflex mediated ankle joint stiffness in multiple sclerosis patients with spasticity.

In this study, we have measured the passive, the intrinsic, and the reflex-mediated mechanical response to stretch of the ankle extensors and flexors in 13 spastic multiple sclerosis patients and 10 healthy control subjects. In the ankle flexors, the patients had no reflex-mediated stiffness. The passive stiffness was increased by 138% (95% confidence interval: 26-91%) and the intrinsic stiffness by 79% (41-158%) when compared with the healthy subjects. In the ankle extensors, the reflex-mediated stiffness and the intrinsic stiffness of the patients were equal to the reflex-mediated and the intrinsic stiffness in healthy subjects. The passive stiffness was increased by 152% (41-352%). We conclude that spastic muscles in multiple sclerosis patients have an increased non-reflex stiffness (passive plus intrinsic stiffness), and that the reflex-mediated stiffness in the extensors during a sustained voluntary contraction does not differ significantly from healthy subjects.

Adult↗
PubMed1993

Stretch responses to ankle rotation in multiple sclerosis patients with spasticity.

In 13 spastic patients with multiple sclerosis and 10 control subjects, electromyographic (EMG) and mechanical responses to stretch of the ankle extensors and ankle flexors during maintained contraction were measured. The reflex EMG responses in the extensors were divided into a phasic response (40-140 msec after onset of stretch) and a tonic response (200-400 msec after onset of stretch). In the control subjects, both the onset and peak latency of the phasic EMG response decreased with the contraction level (0.01 < P < 0.02 and P < 0.002 respectively) in the extensors. In the patients the latency of the phasic EMG response in the extensors was independent of the voluntary contraction level. This could be attributed to a disruption of the normal recruitment of the motor units according to the size principle. The phasic EMG response was larger in the patients than in the control subjects (P < 0.01). The tonic EMG response was of equal size in the two groups. The larger phasic EMG response in the patients was not followed by an increase in the reflex mediated mechanical stretch response. This shows that proposed changes in the muscle function in spastic patients based on changes in EMG stretch responses must be made with caution. In the ankle flexors all patients had reduced or absent EMG responses to stretch, consistent with earlier findings of an absent mechanical reflex mediated response.

Adult↗
PubMed1993

Specification of models in large expert systems based on causal probabilistic networks.

Problems involved in the specification of large expert systems are discussed. In the specification of causal probabilistic networks conditional probability tables for all nodes have to be provided. These conditional probability tables can often be described by models that specify the nature of interaction between nodes. Various types of models are described and a program that handles such models is presented. Large causal probabilistic networks often contain several copies of identical tables or structures. A header facility that provides common definitions of such repeated elements is proposed. This facility makes specifications much shorter and easier to construct and maintain.

Expert Systems↗
PubMed1992

Diagnostic function of the microhuman prototype of the expert system--MUNIN.

This paper describes the diagnostic function of a prototype expert system for electromyography (EMG). The prototype was restricted to a limited "Microhuman" anatomy with only 6 muscles and 8 nerves, and a corresponding limitation on the number of local nerve lesions. It attempted to give a detailed description of the most important groups of generalized nerve and muscle disorders, and the commonly used parameters from needle EMG and nerve conduction studies were included. The system can be used both for "diagnostic" and for "causal" reasoning. In diagnostic reasoning, the system's probabilistic inference engine is used to reason from test results through 14 different aspects of neuromuscular pathophysiology to disorders. In causal reasoning, the system reasons in the opposite direction from disorders through pathophysiology to expected test results. The diagnostic function of the system was illustrated by 3 cases: a normal subject, a patient with a bilateral carpal tunnel syndrome and a patient with both a diabetic polyneuropathy and a bilateral carpal tunnel syndrome.

Carpal Tunnel Syndrome↗