| 1. |
- Ahmed, Mobyen Uddin, et al.
(författare)
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A Computer Aided System for Post-operative Pain Treatment Combining Knowledge Discovery and Case-Based Reasoning
- 2012
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Ingår i: Lecture Notes in Computer Science, vol. 7466. - Berlin, Heidelberg : Springer. - 9783642329852 ; , s. 3-16
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Bokkapitel (refereegranskat)abstract
- The quality improvement for individual postoperative-pain treatment is an important issue. This paper presents a computer aided system for physicians in their decision making tasks in post-operative pain treatment. Here, the system combines a Case-Based Reasoning (CBR) approach with knowledge discovery. Knowledge discovery is applied in terms of clustering in order to identify the unusual cases. We applied a two layered case structure for case solutions i.e. the treatment is in the first layer and outcome after treatment (i.e. recovery of the patient) is in the second layer. Moreover, a 2nd order retrieval approach is applied in the CBR retrieval step in order to retrieve the most similar cases. The system enables physicians to make more informed decisions since they are able to explore similar both regular and rare cases of post-operative patients. The two layered case structure is moving the focus from diagnosis to outcome i.e. the recovery of the patient, something a physician is especially interested in, including the risk of complications and side effects.
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| 2. |
- Ahmed, Mobyen Uddin, Dr, 1976-, et al.
(författare)
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Artificial intelligence, machine learning and reasoning in health informatics—an overview
- 2021
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Ingår i: Intelligent Systems Reference Library, Vol. 192. - Cham : Springer Science and Business Media Deutschland GmbH. ; , s. 171-192
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Bokkapitel (refereegranskat)abstract
- As humans are intelligent, to mimic or models of human certain intelligent behavior to a computer or a machine is called Artificial Intelligence (AI). Learning is one of the activities by a human that helps to gain knowledge or skills by studying, practising, being taught, or experiencing something. Machine Learning (ML) is a field of AI that mimics human learning behavior by constructing a set of algorithms that can learn from data, i.e. it is a field of study that gives computers the ability to learn without being explicitly programmed. The reasoning is a set of processes that enable humans to provide a basis for judgment, making decisions, and prediction. Machine Reasoning (MR), is a part of AI evolution towards human-level intelligence or the ability to apply prior knowledge to new situations with adaptation and changes. This book chapter presents some AI, ML and MR techniques and approached those are widely used in health informatics domains. Here, the overview of each technique is discussed to show how they can be applied in the development of a decision support system.
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| 3. |
- Ahmed, Mobyen Uddin, Dr, 1976-, et al.
(författare)
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Artificial intelligence, machine learning and reasoning in health informatics—case studies
- 2021
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Ingår i: Intelligent Systems Reference Library, Vol 192. - Cham : Springer Science and Business Media Deutschland GmbH. ; , s. 261-291
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Bokkapitel (refereegranskat)abstract
- To apply Artificial Intelligence (AI), Machine Learning (ML) and Machine Reasoning (MR) in health informatics are often challenging as they comprise with multivariate information coming from heterogeneous sources e.g. sensor signals, text, etc. This book chapter presents the research development of AI, ML and MR as applications in health informatics. Five case studies on health informatics have been discussed and presented as (1) advanced Parkinson’s disease, (2) stress management, (3) postoperative pain treatment, (4) driver monitoring, and (5) remote health monitoring. Here, the challenges, solutions, models, results, limitations are discussed with future wishes.
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| 4. |
- Ahmed, Mobyen Uddin, et al.
(författare)
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Case-Based Reasoning for Medical and Industrial Decision Support Systems
- 2010
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Ingår i: Successful Case-based Reasoning Applications. - Berlin, Heidelberg : Springer. - 9783642140778 ; , s. 7-52
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- The amount of medical and industrial experience and knowledge is rapidly growing and it is almost impossible to be up to date with everything. The demand of decision support system (DSS) is especially important in domains where experience and knowledge grow rapidly. However, traditional approaches to DSS are not always easy to adapt to a flow of new experience and knowledge and may also show a limitation in areas with a weak domain theory. This chapter explores the functionalities of Case-Based Reasoning (CBR) to facilitate experience reuse both in clinical and in industrial decision making tasks. Examples from the field of stress medicine and condition monitoring in industrial robots are presented here to demonstrate that the same approach assists both for clinical applications as well as for decision support for engineers. In the both domains, DSS deals with sensor signal data and integrate other artificial intelligence techniques into the CBR system to enhance the performance in a number of different aspects. Textual information retrieval, Rule-based Reasoning (RBR), and fuzzy logic are combined together with CBR to offer decision support to clinicians for a more reliable and efficient management of stress. Agent technology and wavelet transformations are applied with CBR to diagnose audible faults on industrial robots and to package such a system. The performance of the CBR systems have been validated and have shown to be useful in solving such problems in both of these domains.
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| 5. |
- Begum, Shahina, et al.
(författare)
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Classify and Diagnose Individual Stress Using Calibration and Fuzzy Case-Based Reasoning
- 2007
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Ingår i: Case-Based Reasoning Research and Development. - Berlin, Heidelberg : Springer. - 9783540741381 ; , s. 478-491
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Bokkapitel (refereegranskat)abstract
- Increased exposure to stress may cause health problems. An experi-enced clinician is able to diagnose a person's stress level based on sensor read-ings. Large individual variations and absence of general rules make it difficult to diagnose stress and the risk of stress-related health problems. A decision sup-port system providing clinicians with a second opinion would be valuable. We propose a novel solution combining case-based reasoning and fuzzy logic along with a calibration phase to diagnose individual stress. During calibration a num-ber of individual parameters are established. The system also considers the feedback from the patient on how well the test was performed. The system uses fuzzy logic to incorporating the imprecise characteristics of the domain. The cases are also used for the individual treatment process and transfer experience between clinicians. The validation of the approach is based on close collabora-tion with experts and measurements from 24 persons used as reference.
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| 6. |
- Begum, Shahina, et al.
(författare)
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Intelligent Signal Analysis Using Case-Based Reasoning for Decision Support in Stress Management
- 2010
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Ingår i: Computational Intelligence in Healthcare 4. - : Springer Berlin/Heidelberg. - 9783642144639 ; , s. 159-189
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- The complexity of modern lifestyle and society brings many advantages but also causes increased levels of stress for many people. It is known that increased exposure to stress may cause serious health problems if undiagnosed and untreated and a report from the Swedish government estimates that 1/3 of all long term sick leave is stress related. One of the physiological parameters for quantifying stress levels is the finger temperature that helps the clinician in diagnosis and treatment of stress. However, in practice, the complex and varying nature of signals makes it difficult and tedious to interpret and analyze the lengthy sequential measurements. A computer based system diagnosing stress would be valuable both for clinicians and for treatment. Since stress diagnosis has a week domain theory and there are large individual variations, Case-Based Reasoning is proposed as the main methodology. Feature extraction methods abstracting the original signals without losing key features are investigated. A fuzzy technique is also incorporated into the system to perform matching between the features derived from signals to better accommodate vagueness, uncertainty often present in clinical reasoning Validation of the approach is based on close collaboration with experts and measurements from twenty four persons. The system formulates a new problem case with 17 extracted features from the fifteen minutes (1800 samples) of biomedical sensor data. Thirty nine time series from twenty four persons have been used to evaluate the approach (matching algorithms) in which the system shows a level of performance close to an experienced expert. The system can be used as an expert for a less experienced clinician, as a second option for an experienced clinician or for treatment in home environment.
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| 7. |
- Begum, Shahina, et al.
(författare)
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Physiological Sensor Signals Analysis to Represent Cases in a Case-based Diagnostic System
- 2013
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Ingår i: Innovations in Knowledge-based Systems in Biomedicine, vol. 250. - Berlin, Heidelberg : Springer. - 9783642330148 ; , s. 1-25
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Today, medical knowledge is expanding so rapidly that clinicians cannot follow all progress any more. This is one reason for making knowledge- based systems desirable in medicine. Such systems can give a clinician a second opinion and give them access to new experience and knowledge. Recent advances in Artificial Intelligence (AI) offers methods and techniques with the potential of solving tasks previously difficult to solve with computer-based systems in medical domains. This chapter is especially concerned with diagnosis of stress-related dysfunctions using AI methods and techniques. Since there are large individual variations between people when looking at biological sensor signals to diagnose stress, this is a worthy challenge. Stress is an inevitable part of our human life. No one can live without stress. However, long-term exposure to stress may in the worst case cause severe mental and/or physical problems that are often related to different kind of psychosomatic disorders, coronary heart disease etc. So, diagnosis of stress is an important issue for health and well-being. Diagnosis of stress often involves acquisition of biological signals for example finger temperature, electrocardiogram (ECG), electromyography (EMG) signal, skin conductance (SC) signals etc. and is followed by a careful analysis by an expert. However, the number of experts to diagnose stress in psycho-physiological domain is limited. Again, responses to stress are different for different persons. So, interpreting a particular curve and diagnosing stress levels is difficult even for experts in the domain due to large individual variations. It is a highly complex and partly intuitive process which experienced clinicians use when manually inspecting biological sensor signals and classifying a patient. Clinical studies show that the pattern of variation within heart rate i.e., HRV signal and finger temperature can help to determine stress-related disorders. This chapter presents a signal pre-processing and feature extraction approach based on electrocardiogram (ECG) and finger temperature sensor signals. The extracted features are used to formulate cases in a case-based reasoning system to develop a personalized stress diagnosis system. The results obtained from the evaluation show a performance close to an expert in the domain in diagnosing stress.
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