$ BPLT^{+} $: A Bayesian-based personalized recommendation model for health care
Abstract In this paper, we propose an Advanced Bayesian-based Personalized Laboratory Tests recommendation ($ BPLT^{+} $) model. Given a patient, we estimate whether a new laboratory test should belong to a "taken" or "not-taken" class. We use the bayesian method to build a weigh...
Ausführliche Beschreibung
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| Autor*in: |
Zhao, Jiashu [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2013 |
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| Anmerkung: |
© Zhao et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( |
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| Übergeordnetes Werk: |
Enthalten in: BMC genomics - London : BioMed Central, 2000, 14(2013), Suppl 4 vom: 01. Okt. |
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| Übergeordnetes Werk: |
volume:14 ; year:2013 ; number:Suppl 4 ; day:01 ; month:10 |
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| DOI / URN: |
10.1186/1471-2164-14-S4-S6 |
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| Katalog-ID: |
SPR027086437 |
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| 520 | |a Abstract In this paper, we propose an Advanced Bayesian-based Personalized Laboratory Tests recommendation ($ BPLT^{+} $) model. Given a patient, we estimate whether a new laboratory test should belong to a "taken" or "not-taken" class. We use the bayesian method to build a weighting function for a laboratory test and the given patient. A higher weight represents that the laboratory test has a higher probability of being "taken" by the patient and lower probability of being "not-taken" by the patient. For the sake of effectiveness and robustness, we further integrate several modified smoothing techniques into the model. In order to evaluate $ BPLT^{+} $ model objectively, we propose a framework where the data set is randomly split into a training set, a validation input set and a validation label set. A training matrix is generated from the training data set. Then instead of accessing the training data set repeatedly, we utilize this training matrix to predict the laboratory test on the validation input set. Finally, the recommended ranking list is compared with the validation label set using our proposed metric CorrectRateM. We conduct experiments on real medical data, and the experimental results show the effectiveness of the proposed $ BPLT^{+} $ model. | ||
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| 700 | 1 | |a Hu, Xiaohua |4 aut | |
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10.1186/1471-2164-14-S4-S6 doi (DE-627)SPR027086437 (SPR)1471-2164-14-S4-S6-e DE-627 ger DE-627 rakwb eng Zhao, Jiashu verfasserin aut $ BPLT^{+} $: A Bayesian-based personalized recommendation model for health care 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Zhao et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Abstract In this paper, we propose an Advanced Bayesian-based Personalized Laboratory Tests recommendation ($ BPLT^{+} $) model. Given a patient, we estimate whether a new laboratory test should belong to a "taken" or "not-taken" class. We use the bayesian method to build a weighting function for a laboratory test and the given patient. A higher weight represents that the laboratory test has a higher probability of being "taken" by the patient and lower probability of being "not-taken" by the patient. For the sake of effectiveness and robustness, we further integrate several modified smoothing techniques into the model. In order to evaluate $ BPLT^{+} $ model objectively, we propose a framework where the data set is randomly split into a training set, a validation input set and a validation label set. A training matrix is generated from the training data set. Then instead of accessing the training data set repeatedly, we utilize this training matrix to predict the laboratory test on the validation input set. Finally, the recommended ranking list is compared with the validation label set using our proposed metric CorrectRateM. We conduct experiments on real medical data, and the experimental results show the effectiveness of the proposed $ BPLT^{+} $ model. Smoothing Parameter (dpeaa)DE-He213 Mean Average Precision (dpeaa)DE-He213 Bayesian Classifier (dpeaa)DE-He213 Ranking List (dpeaa)DE-He213 Smoothing Technique (dpeaa)DE-He213 Huang, Jimmy Xiangji aut Hu, Xiaohua aut Enthalten in BMC genomics London : BioMed Central, 2000 14(2013), Suppl 4 vom: 01. Okt. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:14 year:2013 number:Suppl 4 day:01 month:10 https://dx.doi.org/10.1186/1471-2164-14-S4-S6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_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_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_2111 GBV_ILN_2113 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 Suppl 4 01 10 |
| spelling |
10.1186/1471-2164-14-S4-S6 doi (DE-627)SPR027086437 (SPR)1471-2164-14-S4-S6-e DE-627 ger DE-627 rakwb eng Zhao, Jiashu verfasserin aut $ BPLT^{+} $: A Bayesian-based personalized recommendation model for health care 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Zhao et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Abstract In this paper, we propose an Advanced Bayesian-based Personalized Laboratory Tests recommendation ($ BPLT^{+} $) model. Given a patient, we estimate whether a new laboratory test should belong to a "taken" or "not-taken" class. We use the bayesian method to build a weighting function for a laboratory test and the given patient. A higher weight represents that the laboratory test has a higher probability of being "taken" by the patient and lower probability of being "not-taken" by the patient. For the sake of effectiveness and robustness, we further integrate several modified smoothing techniques into the model. In order to evaluate $ BPLT^{+} $ model objectively, we propose a framework where the data set is randomly split into a training set, a validation input set and a validation label set. A training matrix is generated from the training data set. Then instead of accessing the training data set repeatedly, we utilize this training matrix to predict the laboratory test on the validation input set. Finally, the recommended ranking list is compared with the validation label set using our proposed metric CorrectRateM. We conduct experiments on real medical data, and the experimental results show the effectiveness of the proposed $ BPLT^{+} $ model. Smoothing Parameter (dpeaa)DE-He213 Mean Average Precision (dpeaa)DE-He213 Bayesian Classifier (dpeaa)DE-He213 Ranking List (dpeaa)DE-He213 Smoothing Technique (dpeaa)DE-He213 Huang, Jimmy Xiangji aut Hu, Xiaohua aut Enthalten in BMC genomics London : BioMed Central, 2000 14(2013), Suppl 4 vom: 01. Okt. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:14 year:2013 number:Suppl 4 day:01 month:10 https://dx.doi.org/10.1186/1471-2164-14-S4-S6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_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_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_2111 GBV_ILN_2113 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 Suppl 4 01 10 |
| allfields_unstemmed |
10.1186/1471-2164-14-S4-S6 doi (DE-627)SPR027086437 (SPR)1471-2164-14-S4-S6-e DE-627 ger DE-627 rakwb eng Zhao, Jiashu verfasserin aut $ BPLT^{+} $: A Bayesian-based personalized recommendation model for health care 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Zhao et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Abstract In this paper, we propose an Advanced Bayesian-based Personalized Laboratory Tests recommendation ($ BPLT^{+} $) model. Given a patient, we estimate whether a new laboratory test should belong to a "taken" or "not-taken" class. We use the bayesian method to build a weighting function for a laboratory test and the given patient. A higher weight represents that the laboratory test has a higher probability of being "taken" by the patient and lower probability of being "not-taken" by the patient. For the sake of effectiveness and robustness, we further integrate several modified smoothing techniques into the model. In order to evaluate $ BPLT^{+} $ model objectively, we propose a framework where the data set is randomly split into a training set, a validation input set and a validation label set. A training matrix is generated from the training data set. Then instead of accessing the training data set repeatedly, we utilize this training matrix to predict the laboratory test on the validation input set. Finally, the recommended ranking list is compared with the validation label set using our proposed metric CorrectRateM. We conduct experiments on real medical data, and the experimental results show the effectiveness of the proposed $ BPLT^{+} $ model. Smoothing Parameter (dpeaa)DE-He213 Mean Average Precision (dpeaa)DE-He213 Bayesian Classifier (dpeaa)DE-He213 Ranking List (dpeaa)DE-He213 Smoothing Technique (dpeaa)DE-He213 Huang, Jimmy Xiangji aut Hu, Xiaohua aut Enthalten in BMC genomics London : BioMed Central, 2000 14(2013), Suppl 4 vom: 01. Okt. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:14 year:2013 number:Suppl 4 day:01 month:10 https://dx.doi.org/10.1186/1471-2164-14-S4-S6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_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_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_2111 GBV_ILN_2113 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 Suppl 4 01 10 |
| allfieldsGer |
10.1186/1471-2164-14-S4-S6 doi (DE-627)SPR027086437 (SPR)1471-2164-14-S4-S6-e DE-627 ger DE-627 rakwb eng Zhao, Jiashu verfasserin aut $ BPLT^{+} $: A Bayesian-based personalized recommendation model for health care 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Zhao et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Abstract In this paper, we propose an Advanced Bayesian-based Personalized Laboratory Tests recommendation ($ BPLT^{+} $) model. Given a patient, we estimate whether a new laboratory test should belong to a "taken" or "not-taken" class. We use the bayesian method to build a weighting function for a laboratory test and the given patient. A higher weight represents that the laboratory test has a higher probability of being "taken" by the patient and lower probability of being "not-taken" by the patient. For the sake of effectiveness and robustness, we further integrate several modified smoothing techniques into the model. In order to evaluate $ BPLT^{+} $ model objectively, we propose a framework where the data set is randomly split into a training set, a validation input set and a validation label set. A training matrix is generated from the training data set. Then instead of accessing the training data set repeatedly, we utilize this training matrix to predict the laboratory test on the validation input set. Finally, the recommended ranking list is compared with the validation label set using our proposed metric CorrectRateM. We conduct experiments on real medical data, and the experimental results show the effectiveness of the proposed $ BPLT^{+} $ model. Smoothing Parameter (dpeaa)DE-He213 Mean Average Precision (dpeaa)DE-He213 Bayesian Classifier (dpeaa)DE-He213 Ranking List (dpeaa)DE-He213 Smoothing Technique (dpeaa)DE-He213 Huang, Jimmy Xiangji aut Hu, Xiaohua aut Enthalten in BMC genomics London : BioMed Central, 2000 14(2013), Suppl 4 vom: 01. Okt. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:14 year:2013 number:Suppl 4 day:01 month:10 https://dx.doi.org/10.1186/1471-2164-14-S4-S6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_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_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_2111 GBV_ILN_2113 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 Suppl 4 01 10 |
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$ BPLT^{+} $: A Bayesian-based personalized recommendation model for health care |
| abstract |
Abstract In this paper, we propose an Advanced Bayesian-based Personalized Laboratory Tests recommendation ($ BPLT^{+} $) model. Given a patient, we estimate whether a new laboratory test should belong to a "taken" or "not-taken" class. We use the bayesian method to build a weighting function for a laboratory test and the given patient. A higher weight represents that the laboratory test has a higher probability of being "taken" by the patient and lower probability of being "not-taken" by the patient. For the sake of effectiveness and robustness, we further integrate several modified smoothing techniques into the model. In order to evaluate $ BPLT^{+} $ model objectively, we propose a framework where the data set is randomly split into a training set, a validation input set and a validation label set. A training matrix is generated from the training data set. Then instead of accessing the training data set repeatedly, we utilize this training matrix to predict the laboratory test on the validation input set. Finally, the recommended ranking list is compared with the validation label set using our proposed metric CorrectRateM. We conduct experiments on real medical data, and the experimental results show the effectiveness of the proposed $ BPLT^{+} $ model. © Zhao et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( |
| abstractGer |
Abstract In this paper, we propose an Advanced Bayesian-based Personalized Laboratory Tests recommendation ($ BPLT^{+} $) model. Given a patient, we estimate whether a new laboratory test should belong to a "taken" or "not-taken" class. We use the bayesian method to build a weighting function for a laboratory test and the given patient. A higher weight represents that the laboratory test has a higher probability of being "taken" by the patient and lower probability of being "not-taken" by the patient. For the sake of effectiveness and robustness, we further integrate several modified smoothing techniques into the model. In order to evaluate $ BPLT^{+} $ model objectively, we propose a framework where the data set is randomly split into a training set, a validation input set and a validation label set. A training matrix is generated from the training data set. Then instead of accessing the training data set repeatedly, we utilize this training matrix to predict the laboratory test on the validation input set. Finally, the recommended ranking list is compared with the validation label set using our proposed metric CorrectRateM. We conduct experiments on real medical data, and the experimental results show the effectiveness of the proposed $ BPLT^{+} $ model. © Zhao et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( |
| abstract_unstemmed |
Abstract In this paper, we propose an Advanced Bayesian-based Personalized Laboratory Tests recommendation ($ BPLT^{+} $) model. Given a patient, we estimate whether a new laboratory test should belong to a "taken" or "not-taken" class. We use the bayesian method to build a weighting function for a laboratory test and the given patient. A higher weight represents that the laboratory test has a higher probability of being "taken" by the patient and lower probability of being "not-taken" by the patient. For the sake of effectiveness and robustness, we further integrate several modified smoothing techniques into the model. In order to evaluate $ BPLT^{+} $ model objectively, we propose a framework where the data set is randomly split into a training set, a validation input set and a validation label set. A training matrix is generated from the training data set. Then instead of accessing the training data set repeatedly, we utilize this training matrix to predict the laboratory test on the validation input set. Finally, the recommended ranking list is compared with the validation label set using our proposed metric CorrectRateM. We conduct experiments on real medical data, and the experimental results show the effectiveness of the proposed $ BPLT^{+} $ model. © Zhao et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( |
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| title_short |
$ BPLT^{+} $: A Bayesian-based personalized recommendation model for health care |
| url |
https://dx.doi.org/10.1186/1471-2164-14-S4-S6 |
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| author2 |
Huang, Jimmy Xiangji Hu, Xiaohua |
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| doi_str |
10.1186/1471-2164-14-S4-S6 |
| up_date |
2024-07-04T00:17:57.869Z |
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