RETRACTED ARTICLE: Development of a medical big-data mining process using topic modeling

Abstract With the development of convergence information technology, all of the spaces and objects of human living have become digitized. In the health- and medical-service areas, IT supports Internet of things (IoT)-based medical services and health-care systems for patients. Medical facilities hav...
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Autor*in:	Song, Chang-Woo [verfasserIn] Jung, Hoill Chung, Kyungyong

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	IT convergence Big-data analysis Data mining Topic modeling Medical data Healthcare

Anmerkung:	© Springer Science+Business Media New York 2017. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Cluster computing - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998, 22(2017), Suppl 1 vom: 09. Juni, Seite 1949-1958
Übergeordnetes Werk:	volume:22 ; year:2017 ; number:Suppl 1 ; day:09 ; month:06 ; pages:1949-1958

Links:	Volltext

DOI / URN:	10.1007/s10586-017-0942-0

Katalog-ID:	SPR011512865

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520			\|a Abstract With the development of convergence information technology, all of the spaces and objects of human living have become digitized. In the health- and medical-service areas, IT supports Internet of things (IoT)-based medical services and health-care systems for patients. Medical facilities have been advanced on the basis of such IoT devices, and the digitized information on human behaviors and health makes the delivery of efficient and convenient health care possible. Under the given circumstances, health and medical care have been researched. For some of this research, the patient-health data were collected using IoT-based medical devices, and they served as a tool for medical diagnosis and treatment. This study proposes the development of a medical big-data mining process for which topic modeling is employed. The proposed method uses the big data that are offered by the open system of the health- and medical-services big data from the Health Insurance Review and Assessment Service, and their application follows the guidelines of the knowledge discovery in big-data process for data mining and topic modeling. For the medical data regarding the topic modeling, the public structured health- and medical-services big data, Open API, and patient datasets were used. For the document classification in the semantic situation of a topic, the Bag of Words technique and the latent Dirichlet allocation method were applied to find the document association for the development of the medical big-data mining process. In addition, this study conducted a performance evaluation of the topic-modeling accuracy based on the medical big-data mining process and the topic-modeling efficiency, and the effectiveness of the proposed method was examined.
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allfields	10.1007/s10586-017-0942-0 doi (DE-627)SPR011512865 (SPR)s10586-017-0942-0-e DE-627 ger DE-627 rakwb eng Song, Chang-Woo verfasserin aut RETRACTED ARTICLE: Development of a medical big-data mining process using topic modeling 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2017. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract With the development of convergence information technology, all of the spaces and objects of human living have become digitized. In the health- and medical-service areas, IT supports Internet of things (IoT)-based medical services and health-care systems for patients. Medical facilities have been advanced on the basis of such IoT devices, and the digitized information on human behaviors and health makes the delivery of efficient and convenient health care possible. Under the given circumstances, health and medical care have been researched. For some of this research, the patient-health data were collected using IoT-based medical devices, and they served as a tool for medical diagnosis and treatment. This study proposes the development of a medical big-data mining process for which topic modeling is employed. The proposed method uses the big data that are offered by the open system of the health- and medical-services big data from the Health Insurance Review and Assessment Service, and their application follows the guidelines of the knowledge discovery in big-data process for data mining and topic modeling. For the medical data regarding the topic modeling, the public structured health- and medical-services big data, Open API, and patient datasets were used. For the document classification in the semantic situation of a topic, the Bag of Words technique and the latent Dirichlet allocation method were applied to find the document association for the development of the medical big-data mining process. In addition, this study conducted a performance evaluation of the topic-modeling accuracy based on the medical big-data mining process and the topic-modeling efficiency, and the effectiveness of the proposed method was examined. IT convergence (dpeaa)DE-He213 Big-data analysis (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Topic modeling (dpeaa)DE-He213 Medical data (dpeaa)DE-He213 Healthcare (dpeaa)DE-He213 Jung, Hoill aut Chung, Kyungyong aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 22(2017), Suppl 1 vom: 09. Juni, Seite 1949-1958 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:22 year:2017 number:Suppl 1 day:09 month:06 pages:1949-1958 https://dx.doi.org/10.1007/s10586-017-0942-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2017 Suppl 1 09 06 1949-1958
spelling	10.1007/s10586-017-0942-0 doi (DE-627)SPR011512865 (SPR)s10586-017-0942-0-e DE-627 ger DE-627 rakwb eng Song, Chang-Woo verfasserin aut RETRACTED ARTICLE: Development of a medical big-data mining process using topic modeling 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2017. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract With the development of convergence information technology, all of the spaces and objects of human living have become digitized. In the health- and medical-service areas, IT supports Internet of things (IoT)-based medical services and health-care systems for patients. Medical facilities have been advanced on the basis of such IoT devices, and the digitized information on human behaviors and health makes the delivery of efficient and convenient health care possible. Under the given circumstances, health and medical care have been researched. For some of this research, the patient-health data were collected using IoT-based medical devices, and they served as a tool for medical diagnosis and treatment. This study proposes the development of a medical big-data mining process for which topic modeling is employed. The proposed method uses the big data that are offered by the open system of the health- and medical-services big data from the Health Insurance Review and Assessment Service, and their application follows the guidelines of the knowledge discovery in big-data process for data mining and topic modeling. For the medical data regarding the topic modeling, the public structured health- and medical-services big data, Open API, and patient datasets were used. For the document classification in the semantic situation of a topic, the Bag of Words technique and the latent Dirichlet allocation method were applied to find the document association for the development of the medical big-data mining process. In addition, this study conducted a performance evaluation of the topic-modeling accuracy based on the medical big-data mining process and the topic-modeling efficiency, and the effectiveness of the proposed method was examined. IT convergence (dpeaa)DE-He213 Big-data analysis (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Topic modeling (dpeaa)DE-He213 Medical data (dpeaa)DE-He213 Healthcare (dpeaa)DE-He213 Jung, Hoill aut Chung, Kyungyong aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 22(2017), Suppl 1 vom: 09. Juni, Seite 1949-1958 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:22 year:2017 number:Suppl 1 day:09 month:06 pages:1949-1958 https://dx.doi.org/10.1007/s10586-017-0942-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2017 Suppl 1 09 06 1949-1958
allfields_unstemmed	10.1007/s10586-017-0942-0 doi (DE-627)SPR011512865 (SPR)s10586-017-0942-0-e DE-627 ger DE-627 rakwb eng Song, Chang-Woo verfasserin aut RETRACTED ARTICLE: Development of a medical big-data mining process using topic modeling 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2017. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract With the development of convergence information technology, all of the spaces and objects of human living have become digitized. In the health- and medical-service areas, IT supports Internet of things (IoT)-based medical services and health-care systems for patients. Medical facilities have been advanced on the basis of such IoT devices, and the digitized information on human behaviors and health makes the delivery of efficient and convenient health care possible. Under the given circumstances, health and medical care have been researched. For some of this research, the patient-health data were collected using IoT-based medical devices, and they served as a tool for medical diagnosis and treatment. This study proposes the development of a medical big-data mining process for which topic modeling is employed. The proposed method uses the big data that are offered by the open system of the health- and medical-services big data from the Health Insurance Review and Assessment Service, and their application follows the guidelines of the knowledge discovery in big-data process for data mining and topic modeling. For the medical data regarding the topic modeling, the public structured health- and medical-services big data, Open API, and patient datasets were used. For the document classification in the semantic situation of a topic, the Bag of Words technique and the latent Dirichlet allocation method were applied to find the document association for the development of the medical big-data mining process. In addition, this study conducted a performance evaluation of the topic-modeling accuracy based on the medical big-data mining process and the topic-modeling efficiency, and the effectiveness of the proposed method was examined. IT convergence (dpeaa)DE-He213 Big-data analysis (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Topic modeling (dpeaa)DE-He213 Medical data (dpeaa)DE-He213 Healthcare (dpeaa)DE-He213 Jung, Hoill aut Chung, Kyungyong aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 22(2017), Suppl 1 vom: 09. Juni, Seite 1949-1958 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:22 year:2017 number:Suppl 1 day:09 month:06 pages:1949-1958 https://dx.doi.org/10.1007/s10586-017-0942-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2017 Suppl 1 09 06 1949-1958
allfieldsGer	10.1007/s10586-017-0942-0 doi (DE-627)SPR011512865 (SPR)s10586-017-0942-0-e DE-627 ger DE-627 rakwb eng Song, Chang-Woo verfasserin aut RETRACTED ARTICLE: Development of a medical big-data mining process using topic modeling 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2017. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract With the development of convergence information technology, all of the spaces and objects of human living have become digitized. In the health- and medical-service areas, IT supports Internet of things (IoT)-based medical services and health-care systems for patients. Medical facilities have been advanced on the basis of such IoT devices, and the digitized information on human behaviors and health makes the delivery of efficient and convenient health care possible. Under the given circumstances, health and medical care have been researched. For some of this research, the patient-health data were collected using IoT-based medical devices, and they served as a tool for medical diagnosis and treatment. This study proposes the development of a medical big-data mining process for which topic modeling is employed. The proposed method uses the big data that are offered by the open system of the health- and medical-services big data from the Health Insurance Review and Assessment Service, and their application follows the guidelines of the knowledge discovery in big-data process for data mining and topic modeling. For the medical data regarding the topic modeling, the public structured health- and medical-services big data, Open API, and patient datasets were used. For the document classification in the semantic situation of a topic, the Bag of Words technique and the latent Dirichlet allocation method were applied to find the document association for the development of the medical big-data mining process. In addition, this study conducted a performance evaluation of the topic-modeling accuracy based on the medical big-data mining process and the topic-modeling efficiency, and the effectiveness of the proposed method was examined. IT convergence (dpeaa)DE-He213 Big-data analysis (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Topic modeling (dpeaa)DE-He213 Medical data (dpeaa)DE-He213 Healthcare (dpeaa)DE-He213 Jung, Hoill aut Chung, Kyungyong aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 22(2017), Suppl 1 vom: 09. Juni, Seite 1949-1958 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:22 year:2017 number:Suppl 1 day:09 month:06 pages:1949-1958 https://dx.doi.org/10.1007/s10586-017-0942-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2017 Suppl 1 09 06 1949-1958
allfieldsSound	10.1007/s10586-017-0942-0 doi (DE-627)SPR011512865 (SPR)s10586-017-0942-0-e DE-627 ger DE-627 rakwb eng Song, Chang-Woo verfasserin aut RETRACTED ARTICLE: Development of a medical big-data mining process using topic modeling 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2017. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract With the development of convergence information technology, all of the spaces and objects of human living have become digitized. In the health- and medical-service areas, IT supports Internet of things (IoT)-based medical services and health-care systems for patients. Medical facilities have been advanced on the basis of such IoT devices, and the digitized information on human behaviors and health makes the delivery of efficient and convenient health care possible. Under the given circumstances, health and medical care have been researched. For some of this research, the patient-health data were collected using IoT-based medical devices, and they served as a tool for medical diagnosis and treatment. This study proposes the development of a medical big-data mining process for which topic modeling is employed. The proposed method uses the big data that are offered by the open system of the health- and medical-services big data from the Health Insurance Review and Assessment Service, and their application follows the guidelines of the knowledge discovery in big-data process for data mining and topic modeling. For the medical data regarding the topic modeling, the public structured health- and medical-services big data, Open API, and patient datasets were used. For the document classification in the semantic situation of a topic, the Bag of Words technique and the latent Dirichlet allocation method were applied to find the document association for the development of the medical big-data mining process. In addition, this study conducted a performance evaluation of the topic-modeling accuracy based on the medical big-data mining process and the topic-modeling efficiency, and the effectiveness of the proposed method was examined. IT convergence (dpeaa)DE-He213 Big-data analysis (dpeaa)DE-He213 Data mining (dpeaa)DE-He213 Topic modeling (dpeaa)DE-He213 Medical data (dpeaa)DE-He213 Healthcare (dpeaa)DE-He213 Jung, Hoill aut Chung, Kyungyong aut Enthalten in Cluster computing Dordrecht [u.a.] : Springer Science + Business Media B.V, 1998 22(2017), Suppl 1 vom: 09. Juni, Seite 1949-1958 (DE-627)320505332 (DE-600)2012757-1 1573-7543 nnns volume:22 year:2017 number:Suppl 1 day:09 month:06 pages:1949-1958 https://dx.doi.org/10.1007/s10586-017-0942-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2017 Suppl 1 09 06 1949-1958
language	English
source	Enthalten in Cluster computing 22(2017), Suppl 1 vom: 09. Juni, Seite 1949-1958 volume:22 year:2017 number:Suppl 1 day:09 month:06 pages:1949-1958
sourceStr	Enthalten in Cluster computing 22(2017), Suppl 1 vom: 09. Juni, Seite 1949-1958 volume:22 year:2017 number:Suppl 1 day:09 month:06 pages:1949-1958
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	IT convergence Big-data analysis Data mining Topic modeling Medical data Healthcare
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container_title	Cluster computing
authorswithroles_txt_mv	Song, Chang-Woo @@aut@@ Jung, Hoill @@aut@@ Chung, Kyungyong @@aut@@
publishDateDaySort_date	2017-06-09T00:00:00Z
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author	Song, Chang-Woo
spellingShingle	Song, Chang-Woo misc IT convergence misc Big-data analysis misc Data mining misc Topic modeling misc Medical data misc Healthcare RETRACTED ARTICLE: Development of a medical big-data mining process using topic modeling
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title	RETRACTED ARTICLE: Development of a medical big-data mining process using topic modeling
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title_sort	retracted article: development of a medical big-data mining process using topic modeling
title_auth	RETRACTED ARTICLE: Development of a medical big-data mining process using topic modeling
abstract	Abstract With the development of convergence information technology, all of the spaces and objects of human living have become digitized. In the health- and medical-service areas, IT supports Internet of things (IoT)-based medical services and health-care systems for patients. Medical facilities have been advanced on the basis of such IoT devices, and the digitized information on human behaviors and health makes the delivery of efficient and convenient health care possible. Under the given circumstances, health and medical care have been researched. For some of this research, the patient-health data were collected using IoT-based medical devices, and they served as a tool for medical diagnosis and treatment. This study proposes the development of a medical big-data mining process for which topic modeling is employed. The proposed method uses the big data that are offered by the open system of the health- and medical-services big data from the Health Insurance Review and Assessment Service, and their application follows the guidelines of the knowledge discovery in big-data process for data mining and topic modeling. For the medical data regarding the topic modeling, the public structured health- and medical-services big data, Open API, and patient datasets were used. For the document classification in the semantic situation of a topic, the Bag of Words technique and the latent Dirichlet allocation method were applied to find the document association for the development of the medical big-data mining process. In addition, this study conducted a performance evaluation of the topic-modeling accuracy based on the medical big-data mining process and the topic-modeling efficiency, and the effectiveness of the proposed method was examined. © Springer Science+Business Media New York 2017. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract With the development of convergence information technology, all of the spaces and objects of human living have become digitized. In the health- and medical-service areas, IT supports Internet of things (IoT)-based medical services and health-care systems for patients. Medical facilities have been advanced on the basis of such IoT devices, and the digitized information on human behaviors and health makes the delivery of efficient and convenient health care possible. Under the given circumstances, health and medical care have been researched. For some of this research, the patient-health data were collected using IoT-based medical devices, and they served as a tool for medical diagnosis and treatment. This study proposes the development of a medical big-data mining process for which topic modeling is employed. The proposed method uses the big data that are offered by the open system of the health- and medical-services big data from the Health Insurance Review and Assessment Service, and their application follows the guidelines of the knowledge discovery in big-data process for data mining and topic modeling. For the medical data regarding the topic modeling, the public structured health- and medical-services big data, Open API, and patient datasets were used. For the document classification in the semantic situation of a topic, the Bag of Words technique and the latent Dirichlet allocation method were applied to find the document association for the development of the medical big-data mining process. In addition, this study conducted a performance evaluation of the topic-modeling accuracy based on the medical big-data mining process and the topic-modeling efficiency, and the effectiveness of the proposed method was examined. © Springer Science+Business Media New York 2017. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract With the development of convergence information technology, all of the spaces and objects of human living have become digitized. In the health- and medical-service areas, IT supports Internet of things (IoT)-based medical services and health-care systems for patients. Medical facilities have been advanced on the basis of such IoT devices, and the digitized information on human behaviors and health makes the delivery of efficient and convenient health care possible. Under the given circumstances, health and medical care have been researched. For some of this research, the patient-health data were collected using IoT-based medical devices, and they served as a tool for medical diagnosis and treatment. This study proposes the development of a medical big-data mining process for which topic modeling is employed. The proposed method uses the big data that are offered by the open system of the health- and medical-services big data from the Health Insurance Review and Assessment Service, and their application follows the guidelines of the knowledge discovery in big-data process for data mining and topic modeling. For the medical data regarding the topic modeling, the public structured health- and medical-services big data, Open API, and patient datasets were used. For the document classification in the semantic situation of a topic, the Bag of Words technique and the latent Dirichlet allocation method were applied to find the document association for the development of the medical big-data mining process. In addition, this study conducted a performance evaluation of the topic-modeling accuracy based on the medical big-data mining process and the topic-modeling efficiency, and the effectiveness of the proposed method was examined. © Springer Science+Business Media New York 2017. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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container_issue	Suppl 1
title_short	RETRACTED ARTICLE: Development of a medical big-data mining process using topic modeling
url	https://dx.doi.org/10.1007/s10586-017-0942-0
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author2	Jung, Hoill Chung, Kyungyong
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doi_str	10.1007/s10586-017-0942-0
up_date	2024-07-03T23:06:20.826Z
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