| 1. |
- Ahmed, Mobyen Uddin
(författare)
-
A case-based multi-modal clinical system for stress management
- 2010
-
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.
|
|
| 2. |
|
|
| 3. |
|
|
| 4. |
|
|
| 5. |
|
|
| 6. |
|
|
| 7. |
|
|
| 8. |
|
|
| 9. |
- Karlsson, Daniel, et al.
(författare)
-
Extended telemedical consultation using Arden Syntax based decision support, hypertext and WWW technique
- 1997
-
Ingår i: Methods of Information in Medicine. - 0026-1270. ; 36:2, s. 108-114
-
Tidskriftsartikel (refereegranskat)abstract
- There is an obvious need for geographic distribution of expert knowledge among several health care units without increasing the cost of on-site expertise in locations where health care is provided. This paper describes the design of a knowledge-based decision-support system for extended consultation in clinical medicine. The system is based on Arden Syntax for Medical Logic Modules and hypertext using World Wide Web technology. It provides advice and explanations regarding the given advice. The explanations are presented in a hypertext format allowing the user to browse related information and to verify the relevance of the given advice. The system is intended to be used in a closed local network. With special precautions regarding issues of safety and patient security, the system can be used over wider areas such as in rural medicine. A prototype has been developed in the field of clinical microbiology and infectious diseases regarding infective endocarditis.
|
|
| 10. |
|
|
| 11. |
|
|
| 12. |
- Lind, Leili, et al.
(författare)
-
Requirements and prototyping of a home health care application based on emerging JAVA technology.
- 2002
-
Ingår i: International Journal of Medical Informatics. - 1386-5056 .- 1872-8243. ; 68:1-3, s. 129-139
-
Tidskriftsartikel (refereegranskat)abstract
- IT support for home health care is an expanding area within health care IT development. Home health care differs from other in- or outpatient care delivery forms in a number of ways, and thus, the introduction of home health care applications must be based on a rigorous analysis of necessary requirements to secure safe and reliable health care. This article reports early experiences from the development of a home health care application based on emerging technologies. A prototype application for the follow-up of diabetes patients is presented and discussed in relation to a list of general requirements on home health care applications.
|
|
| 13. |
- Lyth, Johan, et al.
(författare)
-
A decision support model for cost-effectiveness of radical prostatectomy in localized prostate cancer
- 2012
-
Ingår i: Scandinavian Journal of Urology and Nephrology. - London, United Kingdom : Informa Healthcare. - 0036-5599 .- 1651-2065. ; 46:1, s. 19-25
-
Tidskriftsartikel (refereegranskat)abstract
- Objective: This study aimed to develop a probabilistic decision support model to calculate the lifetime incremental cost-effectiveness ratio (ICER) between radical prostatectomy and watchful waiting for different patient groups.Material and methods: A randomized trial (SPCG-4) provided most data for this study. Data on survival, costs and quality of life were inputs in a decision analysis, and a decision support model was developed. The model can generate cost-effectiveness information on subgroups of patients with different characteristics.Results: Age was the most important independent factor explaining cost-effectiveness. The cost-effectiveness value varied from 21,026 Swedish kronor (SEK) to 858,703 SEK for those aged 65 to 75 years, depending on Gleason scores and prostate-specific antigen (PSA) values. Information from the decision support model can support decision makers in judging whether or not radical prostatectomy (RP) should be used to treat a specific patient group.Conclusions: The cost-effectiveness ratio for RP varies with age, Gleason scores, and PSA values. Assuming a threshold value of 200,000 SEK per quality-adjusted life-year (QALY) gained, for patients aged ≤70 years the treatment was always cost-effective, except at age 70, Gleason 0-4 and PSA ≤10. Using the same threshold value at age 75, Gleason 7-9 (regardless of PSA) and Gleason 5-6 (with PSA >20) were cost-effective. Hence, RP was not perceived to be cost-effective in men aged 75 years with low Gleason and low PSA. Higher threshold values for patients with clinically localized prostate cancer could be discussed
|
|
| 14. |
- Nyström, Mikael, 1977-, et al.
(författare)
-
Det krävs en riktad satsning på e-hälsa
- 2016
-
Ingår i: Dagens medicin. - Stockholm. - 1104-7488. ; :18, s. 22-
-
Tidskriftsartikel (populärvet., debatt m.m.)
|
|
| 15. |
- Pirnejad, H., et al.
(författare)
-
The nature of unintended effects of health information systems concerning patient safety: A systematic review with thematic synthesis.
- 2010
-
Konferensbidrag (refereegranskat)abstract
- In order to understand the nature and causes through which Health Information Systems (HIS) can affect patient safety negatively, a systematic review with thematic synthesis of the qualitative studies was performed. 26 papers met our criteria and were included into content analysis. 40 error contributing factors in working with HIS were recognized. Upon which, 4 main categories of contributing factors were defined. Analysis of the semantic relation between contributing reasons and common types of errors in healthcare practice revealed 6 mechanisms that can function as secondary contributing reasons. Results of this study can support care providers, system designers, and system implementers to avoid unintended negative effects for patient safety.
|
|
| 16. |
- Razavi, Amir Reza, et al.
(författare)
-
A Data Mining Approach to Analyze Non-compliance with a Guideline for the Treatment of Breast Cancer
- 2007
-
Ingår i: Studies in Health Technology and Informatics. - 0926-9630 .- 1879-8365. ; 129, s. 591-597
-
Tidskriftsartikel (refereegranskat)abstract
- Postmastectomy radiotherapy (PMRT) is prescribed in order to reduce the local recurrence of breast cancer and improve overall survival. A guideline supports the trade-off between benefits and adverse effects of PMRT. However, this guideline is not always followed in practice. This study tries to find a method for revealing patterns of non-compliance between the actual treatment and the PMRT guideline. Data from breast cancer patients admitted to Linköping University Hospital between 1990 and 2000 were analyzed in this study. Cases that were not treated in accordance with the guideline were selected and analyzed by decision tree induction (DTI). Thereafter, four resulting rules, as representations for groups of patients, were compared to the guideline. Finding patterns of non-compliance with guidelines by means of rules can be an appropriate alternative to manual methods, i.e. a case-by-case comparison when studying very large datasets. The resulting rules can be used in a knowledge base of a guideline-based decision support system to alert when inconsistencies with the guidelines may appear.
|
|
| 17. |
|
|
| 18. |
- Razavi, Amir Reza, et al.
(författare)
-
A Data Pre-processing Method to Increase Efficiency and Accuracy in Data Mining
- 2005
-
Ingår i: 10th Conference on Artificial Intelligence in Medicine, AIME2005 - Aberdeen, UK. - Berlin, Heidelberg : Springer Berlin Heidelberg. - 9783540278313 ; , s. 434-443
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- In medicine, data mining methods such as Decision Tree Induction (DTI) can be trained for extracting rules to predict the outcomes of new patients. However, incompleteness and high dimensionality of stored data are a problem. Canonical Correlation Analysis (CCA) can be used prior to DTI as a dimension reduction technique to preserve the character of the original data by omitting non-essential data. In this study, data from 3949 breast cancer patients were analysed. Raw data were cleaned by running a set of logical rules. Missing values were replaced using the Expectation Maximization algorithm. After dimension reduction with CCA, DTI was employed to analyse the resulting dataset. The validity of the predictive model was confirmed by ten-fold cross validation and the effect of pre-processing was analysed by applying DTI to data without pre-processing. Replacing missing values and using CCA for data reduction dramatically reduced the size of the resulting tree and increased the accuracy of the prediction of breast cancer recurrence.
|
|
| 19. |
- Razavi, Amir Reza, 1973-, et al.
(författare)
-
An approach for generating fuzzy rules from decision trees
- 2006
-
Ingår i: Ubiquity. - : IOS Press. - 9781586036478 ; , s. 581-586
-
Konferensbidrag (refereegranskat)abstract
- Identifying high-risk breast cancer patients is vital both for clinicians and for patients. Some variables for identifying these patients such as tumor size are good candidates for fuzzification. In this study, Decision Tree Induction (DTI) has been applied to 3949 female breast cancer patients and crisp If-Then rules has been acquired from the resulting tree. After assigning membership functions for each variable in the crisp rules, they were converted into fuzzy rules and a mathematical model was constructed. One hundred randomly selected cases were examined by this model and compared with crisp rules predictions. The outcomes were examined by the area under the ROC curve (AUC). No significant difference was noticed between these two approaches for prediction of recurrence of breast cancer. By soft discretization of variables according to resulting rules from DTI, a predictive model, which is both more robust to noise and more comprehensible for clinicians, can be built.
|
|
| 20. |
- Razavi, Amir Reza, 1973-
(författare)
-
Applications of Knowledge Discovery in Quality Registries - Predicting Recurrence of Breast Cancer and Analyzing Non-compliance with a Clinical Guideline
- 2007
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In medicine, data are produced from different sources and continuously stored in data depositories. Examples of these growing databases are quality registries. In Sweden, there are many cancer registries where data on cancer patients are gathered and recorded and are used mainly for reporting survival analyses to high level health authorities.In this thesis, a breast cancer quality registry operating in South-East of Sweden is used as the data source for newer analytical techniques, i.e. data mining as a part of knowledge discovery in databases (KDD) methodology. Analyses are done to sift through these data in order to find interesting information and hidden knowledge. KDD consists of multiple steps, starting with gathering data from different sources and preparing them in data pre-processing stages prior to data mining.Data were cleaned from outliers and noise and missing values were handled. Then a proper subset of the data was chosen by canonical correlation analysis (CCA) in a dimensionality reduction step. This technique was chosen because there were multiple outcomes, and variables had complex relationship to one another.After data were prepared, they were analyzed with a data mining method. Decision tree induction as a simple and efficient method was used to mine the data. To show the benefits of proper data pre-processing, results from data mining with pre-processing of the data were compared with results from data mining without data pre-processing. The comparison showed that data pre-processing results in a more compact model with a better performance in predicting the recurrence of cancer.An important part of knowledge discovery in medicine is to increase the involvement of medical experts in the process. This starts with enquiry about current problems in their field, which leads to finding areas where computer support can be helpful. The experts can suggest potentially important variables and should then approve and validate new patterns or knowledge as predictive or descriptive models. If it can be shown that the performance of a model is comparable to domain experts, it is more probable that the model will be used to support physicians in their daily decision-making. In this thesis, we validated the model by comparing predictions done by data mining and those made by domain experts without finding any significant difference between them.Breast cancer patients who are treated with mastectomy are recommended to receive radiotherapy. This treatment is called postmastectomy radiotherapy (PMRT) and there is a guideline for prescribing it. A history of this treatment is stored in breast cancer registries. We analyzed these datasets using rules from a clinical guideline and identified cases that had not been treated according to the PMRT guideline. Data mining revealed some patterns of non-compliance with the PMRT guideline. Further analysis with data mining revealed some reasons for guideline non-compliance. These patterns were then compared with reasons acquired from manual inspection of patient records. The comparisons showed that patterns resulting from data mining were limited to the stored variables in the registry. A prerequisite for better results is availability of comprehensive datasets.Medicine can take advantage of KDD methodology in different ways. The main advantage is being able to reuse information and explore hidden knowledge that can be obtained using advanced analysis techniques. The results depend on good collaboration between medical informaticians and domain experts and the availability of high quality data.
|
|
| 21. |
- Razavi, Amir Reza, 1973-, et al.
(författare)
-
Canonical correlation analysis for data reduction in data mining applied to predictive models for breast cancer recurrence
- 2005
-
Ingår i: The XIXth International Congress of the European Federation for Medical Informatics,2005. - Amsterdam : IOSPress. ; , s. 175-180
-
Konferensbidrag (refereegranskat)abstract
- Data mining methods can be used for extracting specific medical knowledge such as important predictors for recurrence of breast cancer in pertinent data material. However, when there is a huge quantity of variables in the data material it is first necessary to identify and select important variables. In this study we present a preprocessing method for selecting important variables in a dataset prior to building a predictive model. In the dataset, data from 5787 female patients were, analysed. To cover more predictors and obtain a better assessment of the outcomes, data were retrieved from three different registers: the regional breast cancer, tumour markers, and cause of death registers. After retrieving information about selected predictors and outcomes from the different registers, the raw data were cleaned by running different logical rules. Thereafter, domain experts selected predictors assumed to be important regarding recurrence of breast cancer. After that, Canonical Correlation Analysis (CCA) was applied as a dimension reduction technique to preserve the character of the original data. Artificial Neural Network (ANN) was applied to the resulting dataset for two different analyses with the same settings. Performance of the predictive models was confirmed by ten-fold cross validation. The results showed an increase in the accuracy of the prediction and reduction of the mean absolute error.
|
|
| 22. |
- Razavi, Amir Reza, 1973-, et al.
(författare)
-
Data Mining Approach to Analyze Non-compliance with a Guideline for the Treatment of Breast Cancer
- 2007
-
Ingår i: MEDINFO 2007: PROCEEDINGS OF THE 12TH WORLD CONGRESS ON HEALTH (MEDICAL) INFORMATICS, PTS 1 AND 2. - : IOS Press. - 9781586037741 ; , s. 591-595
-
Konferensbidrag (refereegranskat)abstract
- Postmastectomy radiotherapy (PAMT) is prescribed in order to reduce the local recurrence of breast cancer and improve overall survival. A guideline supports the trade-off between benefits and adverse effects of PMRT However, this guideline is not always followed in practice. This study tries to find a method for revealing patterns of noncompliance between the actual treatment and the PMRT guideline.Data from breast cancer patients admitted to Linkoping University Hospital between 1990 and 2000 were analyzed in this study. Cases that were not treated in accordance with the guideline were selected and analyzed by decision tree induction (DTI). Thereafter, four resulting rules, as representations for groups of patients, were compared to the guideline.Finding patterns of non-compliance with guidelines by means of rules can be an appropriate alternative to manual methods, i.e. a case-by-case comparison when studying very large datasets. The resulting rules can be used in a knowledge base of a guideline-based decision support system to alert when inconsistencies with the guidelines may appear.
|
|
| 23. |
- Razavi, Amir Reza, et al.
(författare)
-
Exploring cancer register data to find risk factors for recurrence of breast cancer : Application of Canonical Correlation Analysis
- 2005
-
Ingår i: BMC Medical Informatics and Decision Making. - : Springer Science and Business Media LLC. - 1472-6947. ; 5:29, s. 29-35
-
Tidskriftsartikel (refereegranskat)abstract
- Background A common approach in exploring register data is to find relationships between outcomes and predictors by using multiple regression analysis (MRA). If there is more than one outcome variable, the analysis must then be repeated, and the results combined in some arbitrary fashion. In contrast, Canonical Correlation Analysis (CCA) has the ability to analyze multiple outcomes at the same time. One essential outcome after breast cancer treatment is recurrence of the disease. It is important to understand the relationship between different predictors and recurrence, including the time interval until recurrence. This study describes the application of CCA to find important predictors for two different outcomes for breast cancer patients, loco-regional recurrence and occurrence of distant metastasis and to decrease the number of variables in the sets of predictors and outcomes without decreasing the predictive strength of the model. Methods Data for 637 malignant breast cancer patients admitted in the south-east region of Sweden were analyzed. By using CCA and looking at the structure coefficients (loadings), relationships between tumor specifications and the two outcomes during different time intervals were analyzed and a correlation model was built. Results The analysis successfully detected known predictors for breast cancer recurrence during the first two years and distant metastasis 2–4 years after diagnosis. Nottingham Histologic Grading (NHG) was the most important predictor, while age of the patient at the time of diagnosis was not an important predictor. Conclusion In cancer registers with high dimensionality, CCA can be used for identifying the importance of risk factors for breast cancer recurrence. This technique can result in a model ready for further processing by data mining methods through reducing the number of variables to important ones.
|
|
| 24. |
- Razavi, Amir R, et al.
(författare)
-
Non-compliance with a postmastectomy radiotherapy guideline: Decision tree and cause analysis
- 2008
-
Ingår i: BMC Medical Informatics and Decision Making. - 1472-6947. ; 8:41
-
Tidskriftsartikel (refereegranskat)abstract
- Background: The guideline for postmastectomy radiotherapy (PMRT), which is prescribed to reduce recurrence of breast cancer in the chest wall and improve overall survival, is not always followed. Identifying and extracting important patterns of non-compliance are crucial in maintaining the quality of care in Oncology. Methods: Analysis of 759 patients with malignant breast cancer using decision tree induction (DTI) found patterns of non-compliance with the guideline. The PMRT guideline was used to separate cases according to the recommendation to receive or not receive PMRT. The two groups of patients were analyzed separately. Resulting patterns were transformed into rules that were then compared with the reasons that were extracted by manual inspection of records for the non-compliant cases. Results: Analyzing patients in the group who should receive PMRT according to the guideline did not result in a robust decision tree. However, classification of the other group, patients who should not receive PMRT treatment according to the guideline, resulted in a tree with nine leaves and three of them were representing non-compliance with the guideline. In a comparison between rules resulting from these three non-compliant patterns and manual inspection of patient records, the following was found: In the decision tree, presence of perigland growth is the most important variable followed by number of malignantly invaded lymph nodes and level of Progesterone receptor. DNA index, age, size of the tumor and level of Estrogen receptor are also involved but with less importance. From manual inspection of the cases, the most frequent pattern for non-compliance is age above the threshold followed by near cut-off values for risk factors and unknown reasons. Conclusion: Comparison of patterns of non-compliance acquired from data mining and manual inspection of patient records demonstrates that not all of the non-compliances are repetitive or important. There are some overlaps between important variables acquired from manual inspection of patient records and data mining but they are not identical. Data mining can highlight non-compliance patterns valuable for guideline authors and for medical audit. Improving guidelines by using feedback from data mining can improve the quality of care in oncology.
|
|
| 25. |
- Razavi, Amir Reza, et al.
(författare)
-
Predicting metastasis in breast cancer: comparing a decision tree with domain experts
- 2007
-
Ingår i: Journal of Medical Systems. - : Springer Science and Business Media LLC. - 0148-5598 .- 1573-689X. ; 31:4, s. 263-273
-
Tidskriftsartikel (refereegranskat)abstract
- Breast malignancy is the second most common cause of cancer death among women in Western countries. Identifying high-risk patients is vital in order to provide them with specialized treatment. In some situations, such as when access to experienced oncologists is not possible, decision support methods can be helpful in predicting the recurrence of cancer. Three thousand six hundred ninety-nine breast cancer patients admitted in south-east Sweden from 1986 to 1995 were studied. A decision tree was trained with all patients except for 100 cases and tested with those 100 cases. Two domain experts were asked for their opinions about the probability of recurrence of a certain outcome for these 100 patients. ROC curves, area under the ROC curves, and calibration for predictions were computed and compared. After comparing the predictions from a model built by data mining with predictions made by two domain experts, no significant differences were noted. In situations where experienced oncologists are not available, predictive models created with data mining techniques can be used to support physicians in decision making with acceptable accuracy.
|
|
| 26. |
- Shahsavar, Nosrat, 1951-
(författare)
-
Design, implementation and evaluation of a knowledge-based system to support ventilator therapy management
- 1993
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- A good deal of research has been directed toward developing medical knowledge-based systems to assist the health care provider in both diagnostic and management decisions. Different studies by various groups have resulted in clinically useful knowledge-based systems with great potential for supporting decision-making, but due to a lack of methodology in the clinical integration and evaluation of these systems, only a few of them are in regular use. This thesis is devoted to the following aspects of knowledge-based system development:Knowledge Representarion: Representing knowledge and mimicking the decision making behavior of domain experts is a central problem in the development of medical knowledge-based systems. The chosen representation scheme should cover all pieces of the domain knowledge. Reusability and shareability of the knowledge are other desirable features, since the development of useful knowledge bases is a very time and cost consuming process.Knowledge Acquisition: There are a variety of knowledge acquisition techniques, but all techniques are not suitable for all domains. A successful outcome requires comprehensive knowledge in the knowledge-base. The quality of expert knowledge in the knowledge-base determines the quality of the system.Knowledge-Base Maintenance: Verification and maintenance of the knowledge-base by the domain experts themselves is the main issue here. Since knowledge develops continuously, it is mandatory that the knowledge-base is updateable. The knowledge-base contents should be correct and free from redundancy and inconsistency.lntegration: Integrating the system into the real environment, particularly with real patient data, constitutes a critical step, because prototype environments often differ from the clinical setting. A knowledge-based system can hardly be useful if it cannot be integrated with other applications in the real environment.Evaluation: The evaluation of medical decision-support systems is important, and it is also difficult because there is no generally accepted methodology for carrying out this evaluation. The major aspect in the evaluation of a medical knowledge-based system is to find out whether the system is safe and legal, and to study the impact of the system on patients and the organization.This thesis examines and discusses the aforementioned factors based on experiences from the design, development, implementation and evaluation of VentEx, a knowledge-based decisionsupport and monitoring system we have built and applied in ventilator therapy. Our experience covers the whole development process from the prototype to an integrated on-line system. A hybrid knowledge representation has been used and a domain-specific knowledge acquisition tool (KAVE) equipped with a simulator has been developed. Real patient data has been used to validate the knowledge-base and a study to measure the impact of the system is ongoing. Evaluation results indicate a high consensus between the doctors and VentEx according to a "gold " standard.
|
|
| 27. |
|
|
| 28. |
|
|
| 29. |
|
|
| 30. |
- Shahsavar, Nosrat, 1951-
(författare)
-
Knowledge acquisition and refinement for a domain-specific expert system
- 1990
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Tools for developing expert systems (knowledge-based systems) exist and are being used increasingly. However, many such tools have poor support for knowledge base management. Three aspects are essential in the development of knowledge base management tools, namely knowledge representation, knowledge acquisition and knowledge refinement.This thesis discusses knowledge base management tools and reports an implementation for knowledge acquisition and refinement in a decision support system for artificial ventilation. The tool (KA VE) is based on a domain model and has facilities for entering, editing and refinement of the domain knowledge base. Morever an attached simulator and a knowledge editor support verification and refinement. KA VE also supports inconsistency and redundancy checking in the knowledge base.Prelirninary experiences show that the tool makes knowledge verbalization faster than is possible without computer support. The graphical interface and the rule description format have a good readability and have been used by domain experts to validate and tune the artificial ventilation knowledge base.
|
|
| 31. |
|
|
| 32. |
|
|
| 33. |
|
|
| 34. |
|
|
| 35. |
|
|
| 36. |
|
|
| 37. |
|
|
| 38. |
|
|
