Pub Date : 1992-06-14DOI: 10.1109/CBMS.1992.244998
T. Rush, S. Bear
The authors have successfully applied formal specification techniques to the construction of medical products. The approach is characterized by concentrating on the use of HP-SL to construct clear and precise specifications of behavior. In a number of collaborative product developments, the effectiveness of the software development process has been improved significantly without getting involved in the 'difficult' formal methods areas of refinement and program proof.<>
{"title":"Specifying medical software","authors":"T. Rush, S. Bear","doi":"10.1109/CBMS.1992.244998","DOIUrl":"https://doi.org/10.1109/CBMS.1992.244998","url":null,"abstract":"The authors have successfully applied formal specification techniques to the construction of medical products. The approach is characterized by concentrating on the use of HP-SL to construct clear and precise specifications of behavior. In a number of collaborative product developments, the effectiveness of the software development process has been improved significantly without getting involved in the 'difficult' formal methods areas of refinement and program proof.<<ETX>>","PeriodicalId":197891,"journal":{"name":"[1992] Proceedings Fifth Annual IEEE Symposium on Computer-Based Medical Systems","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125602547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1992-06-14DOI: 10.1109/CBMS.1992.244937
J. M. Ramírez, S. Mitra, A. Kher, Jose Morales
A novel algorithm for 3D digital mapping of curved surfaces from a 2D stereo image pair is developed. This approach for visualization of curved surface topography involves fusion of a stereo depth map with a linearly stretched intensity image of the curved surface. Prior to fusion of the depth map with the intensity image, a cubic B-spline interpolation technique is applied to smooth the sparse depth map obtained from the computed stereo disparity map. The quantitative representation of the optic nerve head surface topography following this algorithm provides a technique for a possibly more reproducible parametric evaluation of the optic nerve head than just qualitative stereoscopic viewing of the fundus.<>
{"title":"3-D digital surface recovery of the optic nerve head from stereo fundus images","authors":"J. M. Ramírez, S. Mitra, A. Kher, Jose Morales","doi":"10.1109/CBMS.1992.244937","DOIUrl":"https://doi.org/10.1109/CBMS.1992.244937","url":null,"abstract":"A novel algorithm for 3D digital mapping of curved surfaces from a 2D stereo image pair is developed. This approach for visualization of curved surface topography involves fusion of a stereo depth map with a linearly stretched intensity image of the curved surface. Prior to fusion of the depth map with the intensity image, a cubic B-spline interpolation technique is applied to smooth the sparse depth map obtained from the computed stereo disparity map. The quantitative representation of the optic nerve head surface topography following this algorithm provides a technique for a possibly more reproducible parametric evaluation of the optic nerve head than just qualitative stereoscopic viewing of the fundus.<<ETX>>","PeriodicalId":197891,"journal":{"name":"[1992] Proceedings Fifth Annual IEEE Symposium on Computer-Based Medical Systems","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123479650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1992-06-14DOI: 10.1109/CBMS.1992.245017
Keith C. C. Chan, J. Y. Ching, A. Wong
An inductive knowledge acquisition method based on the probabilistic inference technique is presented. The proposed system can be applied to generate decision rules automatically for certain medical expert systems. Given a patient database containing historical diagnosis and prognosis information, the method is capable of detecting the inherent probabilistic patterns in the data. Classification knowledge can be synthesized in the form of explicit production rules with associated probabilistic weight of evidence based on the patterns detected. With these rules, new patient cases can be quickly and accurately classified. Using real-world medical data, it is shown that the proposed method performs better in terms of classification accuracy and computational efficiency than some of the major existing methods.<>
{"title":"A probabilistic inductive learning approach to the acquisition of knowledge in medical expert systems","authors":"Keith C. C. Chan, J. Y. Ching, A. Wong","doi":"10.1109/CBMS.1992.245017","DOIUrl":"https://doi.org/10.1109/CBMS.1992.245017","url":null,"abstract":"An inductive knowledge acquisition method based on the probabilistic inference technique is presented. The proposed system can be applied to generate decision rules automatically for certain medical expert systems. Given a patient database containing historical diagnosis and prognosis information, the method is capable of detecting the inherent probabilistic patterns in the data. Classification knowledge can be synthesized in the form of explicit production rules with associated probabilistic weight of evidence based on the patterns detected. With these rules, new patient cases can be quickly and accurately classified. Using real-world medical data, it is shown that the proposed method performs better in terms of classification accuracy and computational efficiency than some of the major existing methods.<<ETX>>","PeriodicalId":197891,"journal":{"name":"[1992] Proceedings Fifth Annual IEEE Symposium on Computer-Based Medical Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129457964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1992-06-14DOI: 10.1109/CBMS.1992.245038
Su-wen Chen, M. Evens, D. Trace, F. Naeymi-Rad
The authors introduce a novel patient severity measurement model using neural networks. A three layer, fully connected backpropagation neural network was used in the pilot experiment. The results are promising and demonstrate that the backpropagation neural network technique is capable of assessing the severity value by learning from raw data. The neural network is easy to improve and of relatively low cost. It saves the expert's valuable time used in assigning numerical values to variables.<>
{"title":"Severity measurements using neural networks","authors":"Su-wen Chen, M. Evens, D. Trace, F. Naeymi-Rad","doi":"10.1109/CBMS.1992.245038","DOIUrl":"https://doi.org/10.1109/CBMS.1992.245038","url":null,"abstract":"The authors introduce a novel patient severity measurement model using neural networks. A three layer, fully connected backpropagation neural network was used in the pilot experiment. The results are promising and demonstrate that the backpropagation neural network technique is capable of assessing the severity value by learning from raw data. The neural network is easy to improve and of relatively low cost. It saves the expert's valuable time used in assigning numerical values to variables.<<ETX>>","PeriodicalId":197891,"journal":{"name":"[1992] Proceedings Fifth Annual IEEE Symposium on Computer-Based Medical Systems","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1992-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132959977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/CBMS.1992.245011
Michael James Schement, P. H. Hartline
The authors describe an intelligent controller (ICON) for neurophysiology experiments that employs approximate reasoning techniques and a dynamic control planner (DYNCON) to perform real-time analysis and control of the experiment. Results from experiments simulated from real data show that ICON reached the same conclusions as did the investigator during and after the actual experiment but that ICON did so in fewer experimental trials. An evaluation showed that ICON's performance in controlling the experiment was limited by the time required to collect the experimental trial data (experiment time) and not the time required to analyze the data (analysis time); analysis time was always less than 11% of the experiment time, indicating that the current hardware and software technology is fast enough for real-time control of experiments similar to the one described. The evaluation also showed that the time spent in the DYNCON was from 4.5% to 15% of the analysis time. The results indicate that advantages achieved by the DYNCON, such as greater flexibility, responsivity to incoming data, and adaptability to the changing demands placed on the system as the experiment progresses, outweigh the cost in terms of computation time.<>
{"title":"An intelligent controller for neurophysiological experiments","authors":"Michael James Schement, P. H. Hartline","doi":"10.1109/CBMS.1992.245011","DOIUrl":"https://doi.org/10.1109/CBMS.1992.245011","url":null,"abstract":"The authors describe an intelligent controller (ICON) for neurophysiology experiments that employs approximate reasoning techniques and a dynamic control planner (DYNCON) to perform real-time analysis and control of the experiment. Results from experiments simulated from real data show that ICON reached the same conclusions as did the investigator during and after the actual experiment but that ICON did so in fewer experimental trials. An evaluation showed that ICON's performance in controlling the experiment was limited by the time required to collect the experimental trial data (experiment time) and not the time required to analyze the data (analysis time); analysis time was always less than 11% of the experiment time, indicating that the current hardware and software technology is fast enough for real-time control of experiments similar to the one described. The evaluation also showed that the time spent in the DYNCON was from 4.5% to 15% of the analysis time. The results indicate that advantages achieved by the DYNCON, such as greater flexibility, responsivity to incoming data, and adaptability to the changing demands placed on the system as the experiment progresses, outweigh the cost in terms of computation time.<<ETX>>","PeriodicalId":197891,"journal":{"name":"[1992] Proceedings Fifth Annual IEEE Symposium on Computer-Based Medical Systems","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131845639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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