| 1. |
- Falkman, Göran, 1968-
(författare)
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Information visualisation in clinical odontology : Multidimensional analysis and interactive data exploration
- 2001
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Ingår i: Artificial Intelligence in Medicine. - : Elsevier. - 0933-3657 .- 1873-2860. ; 22:2, s. 133-158
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Tidskriftsartikel (refereegranskat)abstract
- In 1995, the MedView project, based on a co-operation between computing science and clinical medicine was initiated. The overall goal of the project was to develop models, methods and tools to support clinicians in their daily diagnostic work. As part of MedView, two information visualisation tools were developed and tested as solutions to the problem of visualising clinical experience derived from large amounts of clinical data. The first tool (The Cube) was based on the idea of dynamic three-dimensional (3D) parallel diagrams, an idea similar to the notion of 3D parallel co-ordinates. The Cube was developed to enhance the clinician's ability to intelligibly analyse existing patient material and to allow for pattern recognition and statistical analysis. The second tool (SimVis) was based on a similarity assessment-based interaction model for exploring data, and was designed to help clinicians to classify and cluster clinical examination data. User interaction was supported by 3D visualisation of clusters and similarity measures. Both tools were tested on a knowledge base containing about 1500 examinations obtained from different clinics. Clinical practice indicated that the basic ideas are conceptually appealing to the involved clinicians as the tools can be used for generating and testing of hypotheses.
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| 2. |
- Forsström, Jari, et al.
(författare)
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DIAGAID : a connectionists approach to determine the information value of clinical data
- 1991
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Ingår i: Artificial Intelligence in Medicine. - : Elsevier. - 0933-3657 .- 1873-2860. ; 3:4, s. 193-201
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Tidskriftsartikel (refereegranskat)abstract
- In clinical medicine the diagnosis is usually based on several signs and symptoms and some laboratory test results. It would be of great benefit if we could learn the diagnostic criteria from example cases and represent systematically the most important findings supporting or rejecting the diagnosis. In rare diseases where the physician may not be familiar with the ailment the scoring of symptoms would be very useful. In this paper we describe a connectionist approach based on Minsky-Papert's perceptrons for evaluation of the information value of clinical data in diagnosing the Nephropathia epidemica. The scores are compared with those from the Bayesian approach and from the evaluation by an experienced clinician.
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| 3. |
- Jones, Richard W., et al.
(författare)
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Computerised anaesthesia monitoring using fuzzy trend templates
- 2001
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Ingår i: Artificial Intelligence in Medicine. - 0933-3657 .- 1873-2860. ; 21:1-3, s. 247-251
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Tidskriftsartikel (refereegranskat)abstract
- The task of administering anaesthesia requires the clinician to be vigilant for long periods of time to detect the onset of adverse conditions. Large amounts of data must be analysed in real-time and, if a problem is detected, it must be diagnosed as a matter of urgency, this being done while other management protocols are being carried out. For these reasons it would be of benefit if automated decision support could be provided for anaesthesia monitoring, to lighten the cognitive load on the anaesthetist. The Sentinel anaesthesia monitor has been developed with this objective in mind. It uses a fuzzy time-domain pattern matching technique, termed fuzzy trend templates, to detect vaguely specified patterns in multiple physiological data streams. These patterns are representative of symptoms associated with undesirable patient states. The system is capable of detecting trends and states such as 'significant rise' and 'high', and associating vague duration and temporal intervals with individual trends. Fuzzy trend templates have proven to be quite intuitive to specify, given linguistic (anaesthetists') knowledge about the problem domain. Sentinel's implementation of fuzzy trend templates also uses an extension to fuzzy logic based on the theory of evidence, to handle situations where desired information is not available, for example, when sensors are not being used. In off-line testing, Sentinel has achieved sensitivity and specificity of above 90% in the diagnosis of seven common or serious conditions that can arise during anaesthesia
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| 4. |
- Lowe, Andrew, et al.
(författare)
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The graphical presentation of decision support information in an intelligent anaesthesia monitor
- 2001
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Ingår i: Artificial Intelligence in Medicine. - 0933-3657 .- 1873-2860. ; 22:2, s. 173-191
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Tidskriftsartikel (refereegranskat)abstract
- This contribution examines the graphical presentation of decision support information generated by an intelligent monitor, named SENTINEL, developed for use during anaesthesia. Clinicians make diagnoses in real-time during operations by examining clinically significant trends in multiple signals. SENTINEL attempts to mimic this decision process by using a system of fuzzy trend templates. SENTINEL's implementation of fuzzy trend templates is capable of providing the dual fuzzy measures of belief and plausibility, which are derived from the theory of evidence. It is thus capable of generating fairly rich diagnostic decision support information. However, for SENTINEL to be effective, the visual presentation of this information must be intuitive to the anaesthetist, who may not be familiar with the theory of evidence.This paper discusses techniques that are being evaluated to meet the requirements of the SENTINEL anaesthesia monitor. Specifically, the paper presents methods for highlighting clinically significant trends in physiological (or derived) signals by superimposing a coloured band on the signal that reflects fuzzy output from the intelligent monitor. This paper also discusses the intuitive graphical presentation of binary diagnostic fuzzy measures, including their further interpretation and presentation as crisp 'alarm' and 'warning' conditions
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| 5. |
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| 6. |
- Strömberg, Jan-Erik, et al.
(författare)
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Extraction of Diagnostic Rules using Recursive Partitioning Systems : A Comparision of Two Approaches
- 1992
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Ingår i: Artificial Intelligence in Medicine. - 0933-3657 .- 1873-2860. ; 4:5, s. 373-387
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Tidskriftsartikel (refereegranskat)abstract
- There are several empirical systems based on principles of learning from examples that can be used as a tool for decision support by medical experts in medicine. We are comparing two systems of this kind, one based on Quinlan's ID3 algorithm, and the other based on Breiman's CART (Classification And Regression Trees) algorithm. Both of these methods represent the extracted knowledge in form of binary tree structured diagnostic rules. In this paper we present the most important features of the two systems and discuss important differences between the two; all this in a uniform framework. We then study the implications these differences and similarities make when applied to clinical data. The empirical study includes two medical data sets: the first one concerning patients with highly selective vagotomy (HSV) for duodenal ulcer surgery, and the second one concerning patients with non-specified liver disease.
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| 7. |
- Timpka, Toomas
(författare)
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Proactive health computing
- 2001
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Ingår i: Artificial Intelligence in Medicine. - 0933-3657 .- 1873-2860. ; 23:1, s. 13-24
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Tidskriftsartikel (refereegranskat)abstract
- In an analysis departing from the global health situation, the foundation for a change of paradigm in health informatics based on socially embedded information infrastructures and technologies is identified and discussed. It is shown how an increasing computing and data transmitting capacity can be employed for proactive health computing. As a foundation for ubiquituos health promotion and prevention of disease and injury, proactive health systems use data from multiple sources to supply individuals and communities evidence-based information on means to improve their state of health and avoid health risks. The systems are characterised by:being profusely connected to the world around them, using perceptual interfaces, sensors and actuators,responding to external stimuli at faster than human speeds,networked feed-back loops, andhumans remaining in control, while being left outside the primary computing loop.The extended scientific mission of this new partnership between computer science, electrical engineering and social medicine is suggested to be the investigation of how the dissemination of information and communication technology on democratic grounds can be made even more important for global health than sanitation and urban planning became a century ago. Copyright © 2001 Elsevier Science B.V.
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| 8. |
- Ahmed, Mobyen Uddin, et al.
(författare)
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A Multi-Module Case Based Biofeedback System for Stress Treatment
- 2011
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Ingår i: Artificial Intelligence in Medicine. - : Elsevier BV. - 0933-3657 .- 1873-2860. ; 51:2, s. 107-115
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Tidskriftsartikel (refereegranskat)abstract
- Biofeedback is today a recognized treatment method for a number of physical and psychological problems. Experienced clinicians often achieve good results in these areas and their success largely builds on many years of experience and often thousands of treated patients. Unfortunately many of the areas where biofeedback is used are very complex, e.g. diagnosis and treatment of stress. Less experienced clinicians may even have difficulties to initially classify the patient correctly. Often there are only a few experts available to assist less experienced clinicians. To reduce this problem we propose a computer assisted biofeedback system helping in classification, parameter setting and biofeedback training. By adopting a case based approach in a computer-based biofeedback system, decision support can be offered to less experienced clinicians and provide a second opinion to experts. We explore how such a system may be designed and validate the approach in the area of stress where the system assists in the classification, parameter setting and finally in the training. In a case study we show that the case based biofeedback system outperforms novice clinicians based on a case library of cases authorized by an expert.
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| 9. |
- Alabdallah, Abdallah, 1979-, et al.
(författare)
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The Concordance Index decomposition : A measure for a deeper understanding of survival prediction models
- 2024
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Ingår i: Artificial Intelligence in Medicine. - Amsterdam : Elsevier B.V.. - 0933-3657 .- 1873-2860. ; 148
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Tidskriftsartikel (refereegranskat)abstract
- The Concordance Index (C-index) is a commonly used metric in Survival Analysis for evaluating the performance of a prediction model. In this paper, we propose a decomposition of the C-index into a weighted harmonic mean of two quantities: one for ranking observed events versus other observed events, and the other for ranking observed events versus censored cases. This decomposition enables a finer-grained analysis of the relative strengths and weaknesses between different survival prediction methods. The usefulness of this decomposition is demonstrated through benchmark comparisons against classical models and state-of-the-art methods, together with the new variational generative neural-network-based method (SurVED) proposed in this paper. The performance of the models is assessed using four publicly available datasets with varying levels of censoring. Using the C-index decomposition and synthetic censoring, the analysis shows that deep learning models utilize the observed events more effectively than other models. This allows them to keep a stable C-index in different censoring levels. In contrast to such deep learning methods, classical machine learning models deteriorate when the censoring level decreases due to their inability to improve on ranking the events versus other events.
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| 10. |
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