A CASE-BASED DECISION SUPPORT SYSTEM FOR INDIVIDUAL STRESS DIAGNOSIS USING FUZZY SIMILARITY MATCHING

Stress diagnosis based on finger temperature (FT) 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 sequent...

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Vydané v:	Computational intelligence Ročník 25; číslo 3; s. 180 - 195
Hlavní autori:	Begum, Shahina, Ahmed, Mobyen Uddin, Funk, Peter, Xiong, Ning, Von Schéele, Bo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Malden, USA Blackwell Publishing Inc 01.08.2009 Blackwell Publishing Ltd
Predmet:	Algorithms case-based reasoning Classification Cognition & reasoning Decision making decision support system Decision support systems diagnosis Fuzzy logic Medical diagnosis Physiology Studies Subject specialists Time series
ISSN:	0824-7935, 1467-8640, 1467-8640
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Abstract	Stress diagnosis based on finger temperature (FT) 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 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 (CBR) 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 CBR 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 that shows a level of performance close to an experienced expert. Therefore, we have suggested that a fuzzy matching algorithm in combination with CBR 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.
AbstractList	Stress diagnosis based on finger temperature (FT) 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 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 (CBR) 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 CBR 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 that shows a level of performance close to an experienced expert. Therefore, we have suggested that a fuzzy matching algorithm in combination with CBR 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. 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. Stress diagnosis based on finger temperature (FT) 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 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 (CBR) 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 CBR 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 that shows a level of performance close to an experienced expert. Therefore, we have suggested that a fuzzy matching algorithm in combination with CBR 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. [PUBLICATION ABSTRACT]
Author	Ahmed, Mobyen Uddin Begum, Shahina Von Schéele, Bo Xiong, Ning Funk, Peter
Author_xml	– sequence: 1 givenname: Shahina surname: Begum fullname: Begum, Shahina organization: School of Innovation, Design, and Engineering, Mälardalen University, SE-72123 Västerås, Sweden – sequence: 2 givenname: Mobyen Uddin surname: Ahmed fullname: Ahmed, Mobyen Uddin organization: School of Innovation, Design, and Engineering, Mälardalen University, SE-72123 Västerås, Sweden – sequence: 3 givenname: Peter surname: Funk fullname: Funk, Peter organization: School of Innovation, Design, and Engineering, Mälardalen University, SE-72123 Västerås, Sweden – sequence: 4 givenname: Ning surname: Xiong fullname: Xiong, Ning organization: School of Innovation, Design, and Engineering, Mälardalen University, SE-72123 Västerås, Sweden – sequence: 5 givenname: Bo surname: Von Schéele fullname: Von Schéele, Bo organization: School of Innovation, Design, and Engineering, Mälardalen University, SE-72123 Västerås, Sweden
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References	Von Schéele, B. H. C., and I. A. M. Von Schéele. 1999. The measurement of respiratory and metabolic parameters of patients and controls before and after incremental exercise on bicycle: Supporting the effort syndrome hypothesis. Applied Psychophysiology and Biofeedback, 24:167-177. Marling, C., and P. Whitehouse. 2001. Case-based reasoning in the care of Alzheimer's disease patients. Case-Based Research and Development, 2080: 702-715. Watson, I. 1997. Applying Case-Based Reasoning: Techniques for Enterprise Systems. Morgan Kaufmann Publishers, San Fransisco , CA . Nilsson, M., P. Funk, E. Olsson, B. H. C. Von Schéele, and N. Xiong. 2006. Clinical decision-support for diagnosing stress-related disorders by applying psychophysiological medical knowledge to an instance-based learning system. Artificial Intelligence in Medicine, 159-176. Wang, W. J. 1997. New similarity measures on fuzzy sets and on elements. Fuzzy Sets and Systems, 85: 305-309. Schmidt, R., W. Tina, and G. Lothar. 2006. Predicting influenza waves with health insurance data. Computational Intelligence, 22:224-237. Carol, C. H., N. Balakrishnan, M. S. Nikulin, C. Huber-Carol, and M. Mesbah. 2002. Goodness-of-Fit Tests and Model Validity. Birkhauser Verlag, Basel . Aamodt, A., and E. Plaza. 1994. Case-based reasoning: Foundational issues, methodological variations and system approaches. Artificial Intelligence Communications, 7:39-59. Diaz, F., F. Fdez-Riverola, and J. M. Corchado. 2006. Gene-CBR: A case-based reasoning tool for cancer diagnosis using microarray data sets. Computational Intelligence, 22:254-268. 2001; 2080 2001 2000 2006a 2006; 22 1997; 85 1998 1999; 24 1997 2007 1996 2006 1993 2004 2003 2002 1994; 7 1999 Jang J. S. R. (e_1_2_9_14_1) 1997 Burkhard H.‐D. (e_1_2_9_8_1) 2000 Carol C. H. (e_1_2_9_9_1) 2002 e_1_2_9_11_1 e_1_2_9_10_1 e_1_2_9_13_1 Funk P. (e_1_2_9_12_1) 2007 e_1_2_9_15_1 Watson I. (e_1_2_9_28_1) 1997 e_1_2_9_17_1 e_1_2_9_16_1 e_1_2_9_19_1 e_1_2_9_18_1 e_1_2_9_20_1 e_1_2_9_22_1 e_1_2_9_21_1 e_1_2_9_24_1 e_1_2_9_23_1 e_1_2_9_7_1 e_1_2_9_6_1 e_1_2_9_4_1 e_1_2_9_3_1 e_1_2_9_26_1 e_1_2_9_25_1 Aamodt A. (e_1_2_9_2_1) 1994; 7 e_1_2_9_27_1 Bichindaritz I. (e_1_2_9_5_1) 1996
References_xml	– reference: Aamodt, A., and E. Plaza. 1994. Case-based reasoning: Foundational issues, methodological variations and system approaches. Artificial Intelligence Communications, 7:39-59. – reference: Von Schéele, B. H. C., and I. A. M. Von Schéele. 1999. The measurement of respiratory and metabolic parameters of patients and controls before and after incremental exercise on bicycle: Supporting the effort syndrome hypothesis. Applied Psychophysiology and Biofeedback, 24:167-177. – reference: Marling, C., and P. Whitehouse. 2001. Case-based reasoning in the care of Alzheimer's disease patients. Case-Based Research and Development, 2080: 702-715. – reference: Schmidt, R., W. Tina, and G. Lothar. 2006. Predicting influenza waves with health insurance data. Computational Intelligence, 22:224-237. – reference: Watson, I. 1997. Applying Case-Based Reasoning: Techniques for Enterprise Systems. Morgan Kaufmann Publishers, San Fransisco , CA . – reference: Diaz, F., F. Fdez-Riverola, and J. M. Corchado. 2006. Gene-CBR: A case-based reasoning tool for cancer diagnosis using microarray data sets. Computational Intelligence, 22:254-268. – reference: Carol, C. H., N. Balakrishnan, M. S. Nikulin, C. Huber-Carol, and M. Mesbah. 2002. Goodness-of-Fit Tests and Model Validity. Birkhauser Verlag, Basel . – reference: Wang, W. J. 1997. New similarity measures on fuzzy sets and on elements. Fuzzy Sets and Systems, 85: 305-309. – reference: Nilsson, M., P. Funk, E. Olsson, B. H. C. Von Schéele, and N. Xiong. 2006. Clinical decision-support for diagnosing stress-related disorders by applying psychophysiological medical knowledge to an instance-based learning system. Artificial Intelligence in Medicine, 159-176. – start-page: 29 year: 2000 end-page: 45 – start-page: 230 year: 1993 end-page: 236 – start-page: 159 year: 2006 end-page: 176 article-title: Clinical decision‐support for diagnosing stress‐related disorders by applying psychophysiological medical knowledge to an instance‐based learning system publication-title: Artificial Intelligence in Medicine – start-page: 348 year: 2007 end-page: 357 – start-page: 39 year: 2002 end-page: 46 – start-page: 121 year: 2001 end-page: 130 – volume: 7 start-page: 39 year: 1994 end-page: 59 article-title: Case‐based reasoning: Foundational issues, methodological variations and system approaches publication-title: Artificial Intelligence Communications – volume: 24 start-page: 167 year: 1999 end-page: 177 article-title: The measurement of respiratory and metabolic parameters of patients and controls before and after incremental exercise on bicycle: Supporting the effort syndrome hypothesis publication-title: Applied Psychophysiology and Biofeedback – start-page: 113 year: 2006a end-page: 122 – start-page: 14 year: 1996 end-page: 20 – start-page: 398 year: 1993 end-page: 411 – year: 1998 – start-page: 191 year: 2006 end-page: 199 – volume: 85 start-page: 305 year: 1997 end-page: 309 article-title: New similarity measures on fuzzy sets and on elements publication-title: Fuzzy Sets and Systems – start-page: 33 year: 1999 end-page: 36 – start-page: 334 year: 1998 end-page: 345 – year: 2002 – year: 1997 – start-page: 252 year: 1997 end-page: 257 – volume: 22 start-page: 254 year: 2006 end-page: 268 article-title: Gene‐CBR: A case‐based reasoning tool for cancer diagnosis using microarray data sets publication-title: Computational Intelligence – start-page: 64 year: 2003 end-page: 77 – start-page: 178 year: 2004 end-page: 183 – volume: 22 start-page: 224 year: 2006 end-page: 237 article-title: Predicting influenza waves with health insurance data publication-title: Computational Intelligence – start-page: 478 year: 2007 end-page: 491 – volume: 2080 start-page: 702 year: 2001 end-page: 715 article-title: Case‐based reasoning in the care of Alzheimer's disease patients publication-title: Case-Based Research and Development – start-page: 247 year: 2007 end-page: 284 – volume: 7 start-page: 39 year: 1994 ident: e_1_2_9_2_1 article-title: Case‐based reasoning: Foundational issues, methodological variations and system approaches publication-title: Artificial Intelligence Communications – ident: e_1_2_9_19_1 doi: 10.1016/j.artmed.2005.04.004 – ident: e_1_2_9_3_1 – ident: e_1_2_9_25_1 – ident: e_1_2_9_26_1 doi: 10.1023/A:1023484513455 – ident: e_1_2_9_27_1 doi: 10.1016/0165-0114(95)00365-7 – ident: e_1_2_9_17_1 doi: 10.1007/3-540-48229-6_17 – volume-title: A Computional Approach to Learning and Machine Intelligence year: 1997 ident: e_1_2_9_14_1 – ident: e_1_2_9_10_1 doi: 10.1111/j.1467-8640.2006.00287.x – ident: e_1_2_9_13_1 – ident: e_1_2_9_20_1 doi: 10.1007/978-3-540-39619-2_9 – start-page: 14 volume-title: Artificial Intelligence in Medicine: Applications of Current Technologies year: 1996 ident: e_1_2_9_5_1 – ident: e_1_2_9_6_1 doi: 10.1007/BFb0056345 – start-page: 29 volume-title: Soft Computing in Case‐Based Reasoning year: 2000 ident: e_1_2_9_8_1 – ident: e_1_2_9_21_1 – ident: e_1_2_9_24_1 – volume-title: Applying Case‐Based Reasoning: Techniques for Enterprise Systems year: 1997 ident: e_1_2_9_28_1 – start-page: 247 volume-title: Case‐Based Reasoning on Images and Signals year: 2007 ident: e_1_2_9_12_1 – ident: e_1_2_9_7_1 doi: 10.1201/9781420050394 – ident: e_1_2_9_15_1 doi: 10.1007/BFb0017033 – ident: e_1_2_9_4_1 doi: 10.1007/978-3-540-74141-1_33 – volume-title: Goodness‐of‐Fit Tests and Model Validity year: 2002 ident: e_1_2_9_9_1 doi: 10.1007/978-1-4612-0103-8 – ident: e_1_2_9_18_1 – ident: e_1_2_9_22_1 – ident: e_1_2_9_16_1 doi: 10.1007/3-540-44593-5_50 – ident: e_1_2_9_11_1 – ident: e_1_2_9_23_1 doi: 10.1111/j.1467-8640.2006.00285.x
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Snippet	Stress diagnosis based on finger temperature (FT) signals is receiving increasing interest in the psycho‐physiological domain. However, in practice, it is... Stress diagnosis based on finger temperature (FT) signals is receiving increasing interest in the psycho-physiological domain. However, in practice, it is... Stress diagnosis based on finger temperature signals is receiving increasing interest in the psycho-physiological domain. However, in practice, it is difficult...
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SubjectTerms	Algorithms case-based reasoning Classification Cognition & reasoning Decision making decision support system Decision support systems diagnosis Fuzzy logic Medical diagnosis Physiology Studies Subject specialists Time series
Title	A CASE-BASED DECISION SUPPORT SYSTEM FOR INDIVIDUAL STRESS DIAGNOSIS USING FUZZY SIMILARITY MATCHING
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