| 1. |
- Ahmed, Mobyen Uddin
(författare)
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A case-based multi-modal clinical system for stress management
- 2010
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- A difficult issue in stress management is to use biomedical sensor signal in the diagnosis and treatment of stress. Clinicians often make their diagnosis and decision based on manual inspection of physiological signals such as, ECG, heart rate, finger temperature etc. However, the complexity associated with manual analysis and interpretation of the signals makes it difficult even for experienced clinicians. Today the diagnosis and decision is largely dependent on how experienced the clinician is interpreting the measurements. A computer-aided decision support system for diagnosis and treatment of stress would enable a more objective and consistent diagnosis and decisions. A challenge in the field of medicine is the accuracy of the system, it is essential that the clinician is able to judge the accuracy of the suggested solutions. Case-based reasoning systems for medical applications are increasingly multi-purpose and multi-modal, using a variety of different methods and techniques to meet the challenges of the medical domain. This research work covers the development of an intelligent clinical decision support system for diagnosis, classification and treatment in stress management. The system uses a finger temperature sensor and the variation in the finger temperature is one of the key features in the system. Several artificial intelligence techniques have been investigated to enable a more reliable and efficient diagnosis and treatment of stress such as case-based reasoning, textual information retrieval, rule-based reasoning, and fuzzy logic. Functionalities and the performance of the system have been validated by implementing a research prototype based on close collaboration with an expert in stress. The case base of the implemented system has been initiated with 53 reference cases classified by an experienced clinician. A case study also shows that the system provides results close to a human expert. The experimental results suggest that such a system is valuable both for less experienced clinicians and for experts where the system may function as a second option.
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| 2. |
- 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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| 3. |
- Ahmed, Mobyen Uddin, 1976-
(författare)
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A Multimodal Approach for Clinical Diagnosis and Treatment
- 2011
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- A computer-aided Clinical Decision Support System (CDSS) for diagnosis and treatment often plays a vital role and brings essential benefits for clinicians. Such a CDSS could function as an expert for a less experienced clinician or as a second option/opinion of an experienced clinician to their decision making task. Nevertheless, it has been a real challenge to design and develop such a functional system where accuracy of the system performance is an important issue. This research work focuses on development of intelligent CDSS based on a multimodal approach for diagnosis, classification and treatment in medical domains i.e. stress and post-operative pain management domains. Several Artificial Intelligence (AI) techniques such as Case-Based Reasoning (CBR), textual Information Retrieval (IR), Rule-Based Reasoning (RBR), Fuzzy Logic and clustering approaches have been investigated in this thesis work. Patient’s data i.e. their stress and pain related information are collected from complex data sources for instance, finger temperature measurements through sensor signals, pain measurements using a Numerical Visual Analogue Scale (NVAS), patient’s information from text and multiple choice questionnaires. The proposed approach considers multimedia data management to be able to use them in CDSSs for both the domains. The functionalities and performance of the systems have been evaluated based on close collaboration with experts and clinicians of the domains. In stress management, 68 measurements from 46 subjects and 1572 patients’ cases out of ≈4000 in post-operative pain have been used to design, develop and validate the systems. In the stress management domain, besides the 68 measurement cases, three trainees and one senior clinician also have been involved in order to conduct the experimental work. The result from the evaluation shows that the system reaches a level of performance close to the expert and better than the senior and trainee clinicians. Thus, the proposed CDSS could be used as an expert for a less experienced clinician (i.e. trainee) or as a second option/opinion for an experienced clinician (i.e. senior) to their decision making process in stress management. In post-operative pain treatment, the CDSS retrieves and presents most similar cases (e.g. both rare and regular) with their outcomes to assist physicians. Moreover, an automatic approach is presented in order to identify rare cases and 18% of cases from the whole cases library i.e. 276 out of 1572 are identified as rare cases by the approach. Again, among the rare cases (i.e. 276), around 57.25% of the cases are classified as ‘unusually bad’ i.e. the average pain outcome value is greater or equal to 5 on the NVAS scale 0 to 10. Identification of rear cases is an important part of the PAIN OUT project and can be used to improve the quality of individual pain treatment.
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| 4. |
- Ahmed, Mobyen Uddin, et al.
(författare)
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A Three Phase Computer Assisted Biofeedback Training System Using Case-Based Reasoning
- 2008
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Ingår i: Proc. 9th European Conference on Case-based Reasoning. ; , s. 57-68
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Konferensbidrag (refereegranskat)abstract
- Biofeedback is a method gaining increased interest and showing good results for a number of physical and psychological problems. Biofeedback training is mostly guided by an experienced clinician and the results largely rely on the clinician's competence. In this paper we propose a three phase computer assisted sensor-based biofeedback decision support system assisting less experienced clinicians, acting as second opinion for experienced clinicians. The three phase CBR framework is deployed to classify a patient, estimate initial parameters and to make recommendations for biofeedback training by retrieving and comparing with previous similar cases in terms of features extracted. The three phases work independently from each other. Moreover, fuzzy techniques are incorporated into our CBR system to better accommodate uncertainty in clinicians reasoning as well as decision analysis. All parts in the proposed framework have been implemented and primarily validated in a prototypical system. The initial result shows how the three phases functioned with CBR technique to assist biofeedback training. Eventually the system enables the clinicians to allow a patient to train himself/herself unsupervised.
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| 5. |
- Ahmed, Mobyen Uddin, et al.
(författare)
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Case-based Reasoning for Diagnosis of Stress using Enhanced Cosine and Fuzzy Similarity
- 2008
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Ingår i: Case-based Reasoning for Diagnosis of Stress using Enhanced Cosine and Fuzzy Similarity. - 1867-366X. ; 1, s. 3-19
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Tidskriftsartikel (refereegranskat)abstract
- Intelligent analysis of heterogeneous data and information sources for efficient decision support presents an interesting yet challenging task in clinical envi-ronments. This is particularly the case in stress medicine where digital patient re-cords are becoming popular which contain not only lengthy time series measurements but also unstructured textual documents expressed in form of natural languages. This paper develops a hybrid case-based reasoning system for stress di-agnosis which is capable of coping with both numerical signals and textual data at the same time. The total case index consists of two sub-parts corresponding to signal and textual data respectively. For matching of cases on the signal aspect we present a fuzzy similarity matching metric to accommodate and tackle the imprecision and uncertainty in sensor measurements. Preliminary evaluations have revealed that this fuzzy matching algorithm leads to more accurate similarity estimates for improved case ranking and retrieval compared with traditional distance-based matching crite-ria. For evaluation of similarity on the textual dimension we propose an enhanced cosine matching function augmented with related domain knowledge. This is im-plemented by incorporating Wordnet and domain specific ontology into the textual case-based reasoning process for refining weights of terms according to available knowledge encoded therein. Such knowledge-based reasoning for matching of tex-tual cases has empirically shown its merit in improving both precision and recall of retrieved cases with our initial medical databases. Experts in the domain are very positive to our system and they deem that it will be a valuable tool to foster wide-spread experience reuse and transfer in the area of stress diagnosis and treatment.
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| 6. |
- Ahmed, Mobyen Uddin, et al.
(författare)
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Intelligent Stress Management System
- 2009
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Ingår i: Medicinteknikdagarna 2009.
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Konferensbidrag (refereegranskat)abstract
- Today, in our daily life we are subjected to a wide range of pressures. When the pressures exceed the extent that we are able to deal with then stress is trigged. High level of stress may cause serious health problems i.e. it reduces awareness of bodily symptoms. So, people may first notice it weeks or months later meanwhile the stress could cause more serious effect in the body and health. A difficult issue in stress management is to use biomedical sensor signals in the diagnosis and treatment of stress. This paper presents a case-based system that assists a clinician in diagnosis and treatment of stress. The system uses a finger temperature sensor and the variation in the finger temperature is one of the key features in the system. Several artificial intelligence techniques such as textual information retrieval, rule-based reasoning (RBR), and fuzzy logic have been combined together with case-based reasoning to enable more reliable and efficient diagnosis and treatment of stress. The performance has been validated implementing a research prototype and close collaboration with experts.
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| 7. |
- Begum, Shahina, 1977-, et al.
(författare)
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A Case-Based Decision Support System for Individual Stress Diagnosis Using Fuzzy Similarity Matching
- 2009
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Ingår i: Computational intelligence. - : Blackwell Publishing. - 0824-7935 .- 1467-8640. ; 25:3, s. 180-195
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Tidskriftsartikel (refereegranskat)abstract
- Stress diagnosis based on finger temperature signals is receiving increasing interest in the psycho-physiological domain. However, in practice, it is difficult and tedious for a clinician and particularly less experienced clinicians to understand, interpret and analyze complex, lengthy sequential measurements in order to make a diagnosis and treatment plan. The paper presents a case-based decision support system to assist clinicians in performing such tasks. Case-based reasoning is applied as the main methodology to facilitate experience reuse and decision explanation by retrieving previous similar temperature profiles. Further fuzzy techniques are also employed and incorporated into the case-based reasoning system to handle vagueness, uncertainty inherently existing in clinicians reasoning as well as imprecision of feature values. Thirty nine time series from 24 patients have been used to evaluate the approach (matching algorithms) and an expert has ranked and estimated similarity. On average goodness-of-fit for the fuzzy matching algorithm is 90% in ranking and 81% in similarity estimation which shows a level of performance close to an experienced expert. Therefore, we have suggested that a fuzzy matching algorithm in combination with case-based reasoning is a valuable approach in domains where the fuzzy matching model similarity and case preference is consistent with the views of domain expert. This combination is also valuable where domain experts are aware that the crisp values they use have a possibility distribution that can be estimated by the expert and is used when experienced experts reason about similarity. This is the case in the psycho-physiological domain and experienced experts can estimate this distribution of feature values and use them in their reasoning and explanation process.
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| 8. |
- Begum, Shahina, et al.
(författare)
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A computer-based system for the assessment and diagnosis of individual sensitivity to stress in Psychophysiology
- 2007
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Konferensbidrag (refereegranskat)abstract
- Increased exposure to stress may cause serious health problems leading to long term sick leave if undiagnosed and untreated. The practice amongst clinicians' to use a standardized procedure measuring blood pressure, ECG, finger temperature, breathing speed etc. to make a reliable diagnosis of stress and stress sensitivity is increasing. But even with these measurements it is still difficult to diagnose due to large individual variations. A computer-based system as a second option for the assessment and diagnosis of individual stress level is valuable in this domain.A combined approach based on a calibration phase and case-based reasoning is proposed exploiting data from finger temperature sensor readings from 24 individuals. In calibration phase, a standard clinical procedure with six different steps helps to establish a person's stress profile and set up a number of individual parameters. When acquiring a new case, patients are also asked to provide a fuzzy evaluation on how reliable was the procedure to define the case itself. Such a reliability "level" could be used to further discriminate among similar cases. The system extracts key features from the signal and classifies individual sensitivity to stress. These features are stored into a case library and similarity measurements are taken to assess the degrees of matching and create a ranked list containing the most similar cases retrieved by using the nearest-neighbor algorithm.A current case (CC) is compared with two other stored cases (C_92 and C_115) in the case library. The global similarity between the case CC and case C_92 is 67% and case CC and case C_115 is 80% shown by the system. So the case C_115 has ranked higher than the case C_92 and is more similar to current case CC. If necessary, the solution for the best matching case can be revised by the clinician to fit the new patient. The current problem with confirmed solution is then retained as a new case and added to the case library for future use.The system allows us to utilize previous experience and at the same time diagnose stress along with a stress sensitivity profile. This information enables the clinician to make a more informed decision of treatment plan for the patients. Such a system may also be used to actively notify a person's stress levels even in the home environment.
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| 9. |
- 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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| 10. |
- Begum, Shahina, et al.
(författare)
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Development of a Stress Questionnaire : A Tool for Diagnosing Mental Stress
- 2010
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Stress and its relation with health, behavioral and environmental factors are known today. The stress questionnaire is a scientific screening instrument to understand individual’s causes of stress in different parts of life e.g. in the work place and at home. The 38-item stress questionnaire (SQ) is developed to assess the appraisal of stress personally experienced in a patient’s life. This questionnaire cannot diagnose any illness or psychological disorder. However it can be a helpful tool for developing the individual stress management plan by assessing data about the current demands of individual’s life and work.
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