Computer-aided diagnosis of primary membranous nephropathy using expert system

Abstract Background The diagnosis of primary membranous nephropathy (PMN) often depends on invasive renal biopsy, and the diagnosis based on clinical manifestations and target antigens may not be completely reliable as it could be affected by uncertain factors. Moreover, different experts could even...
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Autor*in:	Jie Gao [verfasserIn] Siyang Wang [verfasserIn] Liang Xu [verfasserIn] Jinyan Wang [verfasserIn] Jiao Guo [verfasserIn] Haiping Wang [verfasserIn] Jing Sun [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Expert system Belief rule-based system Primary membranous nephropathy Membranous nephropathy

Übergeordnetes Werk:	In: BioMedical Engineering OnLine - BMC, 2003, 22(2023), 1, Seite 24
Übergeordnetes Werk:	volume:22 ; year:2023 ; number:1 ; pages:24

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s12938-023-01063-5

Katalog-ID:	DOAJ080968201

Internformat


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520			\|a Abstract Background The diagnosis of primary membranous nephropathy (PMN) often depends on invasive renal biopsy, and the diagnosis based on clinical manifestations and target antigens may not be completely reliable as it could be affected by uncertain factors. Moreover, different experts could even have different diagnosis results due to their different experiences, which could further impact the reliability of the diagnosis. Therefore, how to properly integrate the knowledge of different experts to provide more reliable and comprehensive PMN diagnosis has become an urgent issue. Methods This paper develops a belief rule-based system for PMN diagnosis. The belief rule base is constructed based on the knowledge of the experts, with 9 biochemical indicators selected as the input variables. The belief rule-based system is developed of three layers: (1) input layer; (2) belief rule base layer; and (3) output layer, where 9 biochemical indicators are selected as the input variables and the diagnosis result is provided as the conclusion. The belief rule base layer is constructed based on the knowledge of the experts. The final validation was held with gold pattern clinical cases, i.e., with known and clinically confirmed diagnoses. Results 134 patients are used in this study, and the proposed method is defined by its sensitivity, specificity, accuracy and area under curve (AUC), which are 98.0%, 96.9%, 97.8% and 0.93, respectively. The results of this study present a novel and effective way for PMN diagnosis without the requirement of renal biopsy. Conclusions Through analysis of the diagnosis results and comparisons with other methods, it can be concluded that the developed system could help diagnose PMN based on biochemical indicators with relatively high accuracy. Graphical Abstract
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allfields	10.1186/s12938-023-01063-5 doi (DE-627)DOAJ080968201 (DE-599)DOAJ2817f039ad3e4872babf8f4936191a3d DE-627 ger DE-627 rakwb eng R855-855.5 Jie Gao verfasserin aut Computer-aided diagnosis of primary membranous nephropathy using expert system 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background The diagnosis of primary membranous nephropathy (PMN) often depends on invasive renal biopsy, and the diagnosis based on clinical manifestations and target antigens may not be completely reliable as it could be affected by uncertain factors. Moreover, different experts could even have different diagnosis results due to their different experiences, which could further impact the reliability of the diagnosis. Therefore, how to properly integrate the knowledge of different experts to provide more reliable and comprehensive PMN diagnosis has become an urgent issue. Methods This paper develops a belief rule-based system for PMN diagnosis. The belief rule base is constructed based on the knowledge of the experts, with 9 biochemical indicators selected as the input variables. The belief rule-based system is developed of three layers: (1) input layer; (2) belief rule base layer; and (3) output layer, where 9 biochemical indicators are selected as the input variables and the diagnosis result is provided as the conclusion. The belief rule base layer is constructed based on the knowledge of the experts. The final validation was held with gold pattern clinical cases, i.e., with known and clinically confirmed diagnoses. Results 134 patients are used in this study, and the proposed method is defined by its sensitivity, specificity, accuracy and area under curve (AUC), which are 98.0%, 96.9%, 97.8% and 0.93, respectively. The results of this study present a novel and effective way for PMN diagnosis without the requirement of renal biopsy. Conclusions Through analysis of the diagnosis results and comparisons with other methods, it can be concluded that the developed system could help diagnose PMN based on biochemical indicators with relatively high accuracy. Graphical Abstract Expert system Belief rule-based system Primary membranous nephropathy Membranous nephropathy Medical technology Siyang Wang verfasserin aut Liang Xu verfasserin aut Jinyan Wang verfasserin aut Jiao Guo verfasserin aut Haiping Wang verfasserin aut Jing Sun verfasserin aut In BioMedical Engineering OnLine BMC, 2003 22(2023), 1, Seite 24 (DE-627)35210547X (DE-600)2084374-4 1475925X nnns volume:22 year:2023 number:1 pages:24 https://doi.org/10.1186/s12938-023-01063-5 kostenfrei https://doaj.org/article/2817f039ad3e4872babf8f4936191a3d kostenfrei https://doi.org/10.1186/s12938-023-01063-5 kostenfrei https://doaj.org/toc/1475-925X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2023 1 24
spelling	10.1186/s12938-023-01063-5 doi (DE-627)DOAJ080968201 (DE-599)DOAJ2817f039ad3e4872babf8f4936191a3d DE-627 ger DE-627 rakwb eng R855-855.5 Jie Gao verfasserin aut Computer-aided diagnosis of primary membranous nephropathy using expert system 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background The diagnosis of primary membranous nephropathy (PMN) often depends on invasive renal biopsy, and the diagnosis based on clinical manifestations and target antigens may not be completely reliable as it could be affected by uncertain factors. Moreover, different experts could even have different diagnosis results due to their different experiences, which could further impact the reliability of the diagnosis. Therefore, how to properly integrate the knowledge of different experts to provide more reliable and comprehensive PMN diagnosis has become an urgent issue. Methods This paper develops a belief rule-based system for PMN diagnosis. The belief rule base is constructed based on the knowledge of the experts, with 9 biochemical indicators selected as the input variables. The belief rule-based system is developed of three layers: (1) input layer; (2) belief rule base layer; and (3) output layer, where 9 biochemical indicators are selected as the input variables and the diagnosis result is provided as the conclusion. The belief rule base layer is constructed based on the knowledge of the experts. The final validation was held with gold pattern clinical cases, i.e., with known and clinically confirmed diagnoses. Results 134 patients are used in this study, and the proposed method is defined by its sensitivity, specificity, accuracy and area under curve (AUC), which are 98.0%, 96.9%, 97.8% and 0.93, respectively. The results of this study present a novel and effective way for PMN diagnosis without the requirement of renal biopsy. Conclusions Through analysis of the diagnosis results and comparisons with other methods, it can be concluded that the developed system could help diagnose PMN based on biochemical indicators with relatively high accuracy. Graphical Abstract Expert system Belief rule-based system Primary membranous nephropathy Membranous nephropathy Medical technology Siyang Wang verfasserin aut Liang Xu verfasserin aut Jinyan Wang verfasserin aut Jiao Guo verfasserin aut Haiping Wang verfasserin aut Jing Sun verfasserin aut In BioMedical Engineering OnLine BMC, 2003 22(2023), 1, Seite 24 (DE-627)35210547X (DE-600)2084374-4 1475925X nnns volume:22 year:2023 number:1 pages:24 https://doi.org/10.1186/s12938-023-01063-5 kostenfrei https://doaj.org/article/2817f039ad3e4872babf8f4936191a3d kostenfrei https://doi.org/10.1186/s12938-023-01063-5 kostenfrei https://doaj.org/toc/1475-925X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2023 1 24
allfields_unstemmed	10.1186/s12938-023-01063-5 doi (DE-627)DOAJ080968201 (DE-599)DOAJ2817f039ad3e4872babf8f4936191a3d DE-627 ger DE-627 rakwb eng R855-855.5 Jie Gao verfasserin aut Computer-aided diagnosis of primary membranous nephropathy using expert system 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background The diagnosis of primary membranous nephropathy (PMN) often depends on invasive renal biopsy, and the diagnosis based on clinical manifestations and target antigens may not be completely reliable as it could be affected by uncertain factors. Moreover, different experts could even have different diagnosis results due to their different experiences, which could further impact the reliability of the diagnosis. Therefore, how to properly integrate the knowledge of different experts to provide more reliable and comprehensive PMN diagnosis has become an urgent issue. Methods This paper develops a belief rule-based system for PMN diagnosis. The belief rule base is constructed based on the knowledge of the experts, with 9 biochemical indicators selected as the input variables. The belief rule-based system is developed of three layers: (1) input layer; (2) belief rule base layer; and (3) output layer, where 9 biochemical indicators are selected as the input variables and the diagnosis result is provided as the conclusion. The belief rule base layer is constructed based on the knowledge of the experts. The final validation was held with gold pattern clinical cases, i.e., with known and clinically confirmed diagnoses. Results 134 patients are used in this study, and the proposed method is defined by its sensitivity, specificity, accuracy and area under curve (AUC), which are 98.0%, 96.9%, 97.8% and 0.93, respectively. The results of this study present a novel and effective way for PMN diagnosis without the requirement of renal biopsy. Conclusions Through analysis of the diagnosis results and comparisons with other methods, it can be concluded that the developed system could help diagnose PMN based on biochemical indicators with relatively high accuracy. Graphical Abstract Expert system Belief rule-based system Primary membranous nephropathy Membranous nephropathy Medical technology Siyang Wang verfasserin aut Liang Xu verfasserin aut Jinyan Wang verfasserin aut Jiao Guo verfasserin aut Haiping Wang verfasserin aut Jing Sun verfasserin aut In BioMedical Engineering OnLine BMC, 2003 22(2023), 1, Seite 24 (DE-627)35210547X (DE-600)2084374-4 1475925X nnns volume:22 year:2023 number:1 pages:24 https://doi.org/10.1186/s12938-023-01063-5 kostenfrei https://doaj.org/article/2817f039ad3e4872babf8f4936191a3d kostenfrei https://doi.org/10.1186/s12938-023-01063-5 kostenfrei https://doaj.org/toc/1475-925X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2023 1 24
allfieldsGer	10.1186/s12938-023-01063-5 doi (DE-627)DOAJ080968201 (DE-599)DOAJ2817f039ad3e4872babf8f4936191a3d DE-627 ger DE-627 rakwb eng R855-855.5 Jie Gao verfasserin aut Computer-aided diagnosis of primary membranous nephropathy using expert system 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background The diagnosis of primary membranous nephropathy (PMN) often depends on invasive renal biopsy, and the diagnosis based on clinical manifestations and target antigens may not be completely reliable as it could be affected by uncertain factors. Moreover, different experts could even have different diagnosis results due to their different experiences, which could further impact the reliability of the diagnosis. Therefore, how to properly integrate the knowledge of different experts to provide more reliable and comprehensive PMN diagnosis has become an urgent issue. Methods This paper develops a belief rule-based system for PMN diagnosis. The belief rule base is constructed based on the knowledge of the experts, with 9 biochemical indicators selected as the input variables. The belief rule-based system is developed of three layers: (1) input layer; (2) belief rule base layer; and (3) output layer, where 9 biochemical indicators are selected as the input variables and the diagnosis result is provided as the conclusion. The belief rule base layer is constructed based on the knowledge of the experts. The final validation was held with gold pattern clinical cases, i.e., with known and clinically confirmed diagnoses. Results 134 patients are used in this study, and the proposed method is defined by its sensitivity, specificity, accuracy and area under curve (AUC), which are 98.0%, 96.9%, 97.8% and 0.93, respectively. The results of this study present a novel and effective way for PMN diagnosis without the requirement of renal biopsy. Conclusions Through analysis of the diagnosis results and comparisons with other methods, it can be concluded that the developed system could help diagnose PMN based on biochemical indicators with relatively high accuracy. Graphical Abstract Expert system Belief rule-based system Primary membranous nephropathy Membranous nephropathy Medical technology Siyang Wang verfasserin aut Liang Xu verfasserin aut Jinyan Wang verfasserin aut Jiao Guo verfasserin aut Haiping Wang verfasserin aut Jing Sun verfasserin aut In BioMedical Engineering OnLine BMC, 2003 22(2023), 1, Seite 24 (DE-627)35210547X (DE-600)2084374-4 1475925X nnns volume:22 year:2023 number:1 pages:24 https://doi.org/10.1186/s12938-023-01063-5 kostenfrei https://doaj.org/article/2817f039ad3e4872babf8f4936191a3d kostenfrei https://doi.org/10.1186/s12938-023-01063-5 kostenfrei https://doaj.org/toc/1475-925X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2023 1 24
allfieldsSound	10.1186/s12938-023-01063-5 doi (DE-627)DOAJ080968201 (DE-599)DOAJ2817f039ad3e4872babf8f4936191a3d DE-627 ger DE-627 rakwb eng R855-855.5 Jie Gao verfasserin aut Computer-aided diagnosis of primary membranous nephropathy using expert system 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background The diagnosis of primary membranous nephropathy (PMN) often depends on invasive renal biopsy, and the diagnosis based on clinical manifestations and target antigens may not be completely reliable as it could be affected by uncertain factors. Moreover, different experts could even have different diagnosis results due to their different experiences, which could further impact the reliability of the diagnosis. Therefore, how to properly integrate the knowledge of different experts to provide more reliable and comprehensive PMN diagnosis has become an urgent issue. Methods This paper develops a belief rule-based system for PMN diagnosis. The belief rule base is constructed based on the knowledge of the experts, with 9 biochemical indicators selected as the input variables. The belief rule-based system is developed of three layers: (1) input layer; (2) belief rule base layer; and (3) output layer, where 9 biochemical indicators are selected as the input variables and the diagnosis result is provided as the conclusion. The belief rule base layer is constructed based on the knowledge of the experts. The final validation was held with gold pattern clinical cases, i.e., with known and clinically confirmed diagnoses. Results 134 patients are used in this study, and the proposed method is defined by its sensitivity, specificity, accuracy and area under curve (AUC), which are 98.0%, 96.9%, 97.8% and 0.93, respectively. The results of this study present a novel and effective way for PMN diagnosis without the requirement of renal biopsy. Conclusions Through analysis of the diagnosis results and comparisons with other methods, it can be concluded that the developed system could help diagnose PMN based on biochemical indicators with relatively high accuracy. Graphical Abstract Expert system Belief rule-based system Primary membranous nephropathy Membranous nephropathy Medical technology Siyang Wang verfasserin aut Liang Xu verfasserin aut Jinyan Wang verfasserin aut Jiao Guo verfasserin aut Haiping Wang verfasserin aut Jing Sun verfasserin aut In BioMedical Engineering OnLine BMC, 2003 22(2023), 1, Seite 24 (DE-627)35210547X (DE-600)2084374-4 1475925X nnns volume:22 year:2023 number:1 pages:24 https://doi.org/10.1186/s12938-023-01063-5 kostenfrei https://doaj.org/article/2817f039ad3e4872babf8f4936191a3d kostenfrei https://doi.org/10.1186/s12938-023-01063-5 kostenfrei https://doaj.org/toc/1475-925X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2023 1 24
language	English
source	In BioMedical Engineering OnLine 22(2023), 1, Seite 24 volume:22 year:2023 number:1 pages:24
sourceStr	In BioMedical Engineering OnLine 22(2023), 1, Seite 24 volume:22 year:2023 number:1 pages:24
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Expert system Belief rule-based system Primary membranous nephropathy Membranous nephropathy Medical technology
isfreeaccess_bool	true
container_title	BioMedical Engineering OnLine
authorswithroles_txt_mv	Jie Gao @@aut@@ Siyang Wang @@aut@@ Liang Xu @@aut@@ Jinyan Wang @@aut@@ Jiao Guo @@aut@@ Haiping Wang @@aut@@ Jing Sun @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	35210547X
id	DOAJ080968201
language_de	englisch
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Moreover, different experts could even have different diagnosis results due to their different experiences, which could further impact the reliability of the diagnosis. Therefore, how to properly integrate the knowledge of different experts to provide more reliable and comprehensive PMN diagnosis has become an urgent issue. Methods This paper develops a belief rule-based system for PMN diagnosis. The belief rule base is constructed based on the knowledge of the experts, with 9 biochemical indicators selected as the input variables. The belief rule-based system is developed of three layers: (1) input layer; (2) belief rule base layer; and (3) output layer, where 9 biochemical indicators are selected as the input variables and the diagnosis result is provided as the conclusion. The belief rule base layer is constructed based on the knowledge of the experts. The final validation was held with gold pattern clinical cases, i.e., with known and clinically confirmed diagnoses. Results 134 patients are used in this study, and the proposed method is defined by its sensitivity, specificity, accuracy and area under curve (AUC), which are 98.0%, 96.9%, 97.8% and 0.93, respectively. The results of this study present a novel and effective way for PMN diagnosis without the requirement of renal biopsy. Conclusions Through analysis of the diagnosis results and comparisons with other methods, it can be concluded that the developed system could help diagnose PMN based on biochemical indicators with relatively high accuracy. 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callnumber-first	R - Medicine
author	Jie Gao
spellingShingle	Jie Gao misc R855-855.5 misc Expert system misc Belief rule-based system misc Primary membranous nephropathy misc Membranous nephropathy misc Medical technology Computer-aided diagnosis of primary membranous nephropathy using expert system
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topic_title	R855-855.5 Computer-aided diagnosis of primary membranous nephropathy using expert system Expert system Belief rule-based system Primary membranous nephropathy Membranous nephropathy
topic	misc R855-855.5 misc Expert system misc Belief rule-based system misc Primary membranous nephropathy misc Membranous nephropathy misc Medical technology
topic_unstemmed	misc R855-855.5 misc Expert system misc Belief rule-based system misc Primary membranous nephropathy misc Membranous nephropathy misc Medical technology
topic_browse	misc R855-855.5 misc Expert system misc Belief rule-based system misc Primary membranous nephropathy misc Membranous nephropathy misc Medical technology
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title	Computer-aided diagnosis of primary membranous nephropathy using expert system
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title_full	Computer-aided diagnosis of primary membranous nephropathy using expert system
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author_browse	Jie Gao Siyang Wang Liang Xu Jinyan Wang Jiao Guo Haiping Wang Jing Sun
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title_sort	computer-aided diagnosis of primary membranous nephropathy using expert system
callnumber	R855-855.5
title_auth	Computer-aided diagnosis of primary membranous nephropathy using expert system
abstract	Abstract Background The diagnosis of primary membranous nephropathy (PMN) often depends on invasive renal biopsy, and the diagnosis based on clinical manifestations and target antigens may not be completely reliable as it could be affected by uncertain factors. Moreover, different experts could even have different diagnosis results due to their different experiences, which could further impact the reliability of the diagnosis. Therefore, how to properly integrate the knowledge of different experts to provide more reliable and comprehensive PMN diagnosis has become an urgent issue. Methods This paper develops a belief rule-based system for PMN diagnosis. The belief rule base is constructed based on the knowledge of the experts, with 9 biochemical indicators selected as the input variables. The belief rule-based system is developed of three layers: (1) input layer; (2) belief rule base layer; and (3) output layer, where 9 biochemical indicators are selected as the input variables and the diagnosis result is provided as the conclusion. The belief rule base layer is constructed based on the knowledge of the experts. The final validation was held with gold pattern clinical cases, i.e., with known and clinically confirmed diagnoses. Results 134 patients are used in this study, and the proposed method is defined by its sensitivity, specificity, accuracy and area under curve (AUC), which are 98.0%, 96.9%, 97.8% and 0.93, respectively. The results of this study present a novel and effective way for PMN diagnosis without the requirement of renal biopsy. Conclusions Through analysis of the diagnosis results and comparisons with other methods, it can be concluded that the developed system could help diagnose PMN based on biochemical indicators with relatively high accuracy. Graphical Abstract
abstractGer	Abstract Background The diagnosis of primary membranous nephropathy (PMN) often depends on invasive renal biopsy, and the diagnosis based on clinical manifestations and target antigens may not be completely reliable as it could be affected by uncertain factors. Moreover, different experts could even have different diagnosis results due to their different experiences, which could further impact the reliability of the diagnosis. Therefore, how to properly integrate the knowledge of different experts to provide more reliable and comprehensive PMN diagnosis has become an urgent issue. Methods This paper develops a belief rule-based system for PMN diagnosis. The belief rule base is constructed based on the knowledge of the experts, with 9 biochemical indicators selected as the input variables. The belief rule-based system is developed of three layers: (1) input layer; (2) belief rule base layer; and (3) output layer, where 9 biochemical indicators are selected as the input variables and the diagnosis result is provided as the conclusion. The belief rule base layer is constructed based on the knowledge of the experts. The final validation was held with gold pattern clinical cases, i.e., with known and clinically confirmed diagnoses. Results 134 patients are used in this study, and the proposed method is defined by its sensitivity, specificity, accuracy and area under curve (AUC), which are 98.0%, 96.9%, 97.8% and 0.93, respectively. The results of this study present a novel and effective way for PMN diagnosis without the requirement of renal biopsy. Conclusions Through analysis of the diagnosis results and comparisons with other methods, it can be concluded that the developed system could help diagnose PMN based on biochemical indicators with relatively high accuracy. Graphical Abstract
abstract_unstemmed	Abstract Background The diagnosis of primary membranous nephropathy (PMN) often depends on invasive renal biopsy, and the diagnosis based on clinical manifestations and target antigens may not be completely reliable as it could be affected by uncertain factors. Moreover, different experts could even have different diagnosis results due to their different experiences, which could further impact the reliability of the diagnosis. Therefore, how to properly integrate the knowledge of different experts to provide more reliable and comprehensive PMN diagnosis has become an urgent issue. Methods This paper develops a belief rule-based system for PMN diagnosis. The belief rule base is constructed based on the knowledge of the experts, with 9 biochemical indicators selected as the input variables. The belief rule-based system is developed of three layers: (1) input layer; (2) belief rule base layer; and (3) output layer, where 9 biochemical indicators are selected as the input variables and the diagnosis result is provided as the conclusion. The belief rule base layer is constructed based on the knowledge of the experts. The final validation was held with gold pattern clinical cases, i.e., with known and clinically confirmed diagnoses. Results 134 patients are used in this study, and the proposed method is defined by its sensitivity, specificity, accuracy and area under curve (AUC), which are 98.0%, 96.9%, 97.8% and 0.93, respectively. The results of this study present a novel and effective way for PMN diagnosis without the requirement of renal biopsy. Conclusions Through analysis of the diagnosis results and comparisons with other methods, it can be concluded that the developed system could help diagnose PMN based on biochemical indicators with relatively high accuracy. Graphical Abstract
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title_short	Computer-aided diagnosis of primary membranous nephropathy using expert system
url	https://doi.org/10.1186/s12938-023-01063-5 https://doaj.org/article/2817f039ad3e4872babf8f4936191a3d https://doaj.org/toc/1475-925X
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Computer-aided diagnosis of primary membranous nephropathy using expert system

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