Analysis of epidemiological association patterns of serum thyrotropin by combining random forests and Bayesian networks
Approaching epidemiological data with flexible machine learning algorithms is of great value for understanding disease-specific association patterns. However, it can be difficult to correctly extract and understand those patterns due to the lack of model interpretability. We here propose a machine l...
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| Vydáno v: | PloS one Ročník 17; číslo 7; s. e0271610 |
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| Jazyk: | angličtina |
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21.07.2022
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| ISSN: | 1932-6203, 1932-6203 |
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| Abstract | Approaching epidemiological data with flexible machine learning algorithms is of great value for understanding disease-specific association patterns. However, it can be difficult to correctly extract and understand those patterns due to the lack of model interpretability. We here propose a machine learning workflow that combines random forests with Bayesian network surrogate models to allow for a deeper level of interpretation of complex association patterns. We first evaluate the proposed workflow on synthetic data. We then apply it to data from the large population-based Study of Health in Pomerania (SHIP). Based on this combination, we discover and interpret broad patterns of individual serum TSH concentrations, an important marker of thyroid functionality. We demonstrate that the combination of random forest and Bayesian network analysis is helpful to reveal and interpret broad association patterns of individual TSH concentrations. The discovered patterns are in line with state-of-the-art literature. They may be useful for future thyroid research and improved dosing of therapeutics. |
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| AbstractList | Background Approaching epidemiological data with flexible machine learning algorithms is of great value for understanding disease-specific association patterns. However, it can be difficult to correctly extract and understand those patterns due to the lack of model interpretability. Method We here propose a machine learning workflow that combines random forests with Bayesian network surrogate models to allow for a deeper level of interpretation of complex association patterns. We first evaluate the proposed workflow on synthetic data. We then apply it to data from the large population-based Study of Health in Pomerania (SHIP). Based on this combination, we discover and interpret broad patterns of individual serum TSH concentrations, an important marker of thyroid functionality. Results Evaluations using simulated data show that feature associations can be correctly recovered by combining random forests and Bayesian networks. The presented model achieves predictive accuracy that is similar to state-of-the-art models (root mean square error of 0.66, mean absolute error of 0.55, coefficient of determination of R.sup.2 = 0.15). We identify 62 relevant features from the final random forest model, ranging from general health variables over dietary and genetic factors to physiological, hematological and hemostasis parameters. The Bayesian network model is used to put these features into context and make the black-box random forest model more understandable. Conclusion We demonstrate that the combination of random forest and Bayesian network analysis is helpful to reveal and interpret broad association patterns of individual TSH concentrations. The discovered patterns are in line with state-of-the-art literature. They may be useful for future thyroid research and improved dosing of therapeutics. Approaching epidemiological data with flexible machine learning algorithms is of great value for understanding disease-specific association patterns. However, it can be difficult to correctly extract and understand those patterns due to the lack of model interpretability.BACKGROUNDApproaching epidemiological data with flexible machine learning algorithms is of great value for understanding disease-specific association patterns. However, it can be difficult to correctly extract and understand those patterns due to the lack of model interpretability.We here propose a machine learning workflow that combines random forests with Bayesian network surrogate models to allow for a deeper level of interpretation of complex association patterns. We first evaluate the proposed workflow on synthetic data. We then apply it to data from the large population-based Study of Health in Pomerania (SHIP). Based on this combination, we discover and interpret broad patterns of individual serum TSH concentrations, an important marker of thyroid functionality.METHODWe here propose a machine learning workflow that combines random forests with Bayesian network surrogate models to allow for a deeper level of interpretation of complex association patterns. We first evaluate the proposed workflow on synthetic data. We then apply it to data from the large population-based Study of Health in Pomerania (SHIP). Based on this combination, we discover and interpret broad patterns of individual serum TSH concentrations, an important marker of thyroid functionality.Evaluations using simulated data show that feature associations can be correctly recovered by combining random forests and Bayesian networks. The presented model achieves predictive accuracy that is similar to state-of-the-art models (root mean square error of 0.66, mean absolute error of 0.55, coefficient of determination of R2 = 0.15). We identify 62 relevant features from the final random forest model, ranging from general health variables over dietary and genetic factors to physiological, hematological and hemostasis parameters. The Bayesian network model is used to put these features into context and make the black-box random forest model more understandable.RESULTSEvaluations using simulated data show that feature associations can be correctly recovered by combining random forests and Bayesian networks. The presented model achieves predictive accuracy that is similar to state-of-the-art models (root mean square error of 0.66, mean absolute error of 0.55, coefficient of determination of R2 = 0.15). We identify 62 relevant features from the final random forest model, ranging from general health variables over dietary and genetic factors to physiological, hematological and hemostasis parameters. The Bayesian network model is used to put these features into context and make the black-box random forest model more understandable.We demonstrate that the combination of random forest and Bayesian network analysis is helpful to reveal and interpret broad association patterns of individual TSH concentrations. The discovered patterns are in line with state-of-the-art literature. They may be useful for future thyroid research and improved dosing of therapeutics.CONCLUSIONWe demonstrate that the combination of random forest and Bayesian network analysis is helpful to reveal and interpret broad association patterns of individual TSH concentrations. The discovered patterns are in line with state-of-the-art literature. They may be useful for future thyroid research and improved dosing of therapeutics. Background Approaching epidemiological data with flexible machine learning algorithms is of great value for understanding disease-specific association patterns. However, it can be difficult to correctly extract and understand those patterns due to the lack of model interpretability. Method We here propose a machine learning workflow that combines random forests with Bayesian network surrogate models to allow for a deeper level of interpretation of complex association patterns. We first evaluate the proposed workflow on synthetic data. We then apply it to data from the large population-based Study of Health in Pomerania (SHIP). Based on this combination, we discover and interpret broad patterns of individual serum TSH concentrations, an important marker of thyroid functionality. Results Evaluations using simulated data show that feature associations can be correctly recovered by combining random forests and Bayesian networks. The presented model achieves predictive accuracy that is similar to state-of-the-art models (root mean square error of 0.66, mean absolute error of 0.55, coefficient of determination of R 2 = 0.15). We identify 62 relevant features from the final random forest model, ranging from general health variables over dietary and genetic factors to physiological, hematological and hemostasis parameters. The Bayesian network model is used to put these features into context and make the black-box random forest model more understandable. Conclusion We demonstrate that the combination of random forest and Bayesian network analysis is helpful to reveal and interpret broad association patterns of individual TSH concentrations. The discovered patterns are in line with state-of-the-art literature. They may be useful for future thyroid research and improved dosing of therapeutics. BackgroundApproaching epidemiological data with flexible machine learning algorithms is of great value for understanding disease-specific association patterns. However, it can be difficult to correctly extract and understand those patterns due to the lack of model interpretability.MethodWe here propose a machine learning workflow that combines random forests with Bayesian network surrogate models to allow for a deeper level of interpretation of complex association patterns. We first evaluate the proposed workflow on synthetic data. We then apply it to data from the large population-based Study of Health in Pomerania (SHIP). Based on this combination, we discover and interpret broad patterns of individual serum TSH concentrations, an important marker of thyroid functionality.ResultsEvaluations using simulated data show that feature associations can be correctly recovered by combining random forests and Bayesian networks. The presented model achieves predictive accuracy that is similar to state-of-the-art models (root mean square error of 0.66, mean absolute error of 0.55, coefficient of determination of R2 = 0.15). We identify 62 relevant features from the final random forest model, ranging from general health variables over dietary and genetic factors to physiological, hematological and hemostasis parameters. The Bayesian network model is used to put these features into context and make the black-box random forest model more understandable.ConclusionWe demonstrate that the combination of random forest and Bayesian network analysis is helpful to reveal and interpret broad association patterns of individual TSH concentrations. The discovered patterns are in line with state-of-the-art literature. They may be useful for future thyroid research and improved dosing of therapeutics. Background Approaching epidemiological data with flexible machine learning algorithms is of great value for understanding disease-specific association patterns. However, it can be difficult to correctly extract and understand those patterns due to the lack of model interpretability. Method We here propose a machine learning workflow that combines random forests with Bayesian network surrogate models to allow for a deeper level of interpretation of complex association patterns. We first evaluate the proposed workflow on synthetic data. We then apply it to data from the large population-based Study of Health in Pomerania (SHIP). Based on this combination, we discover and interpret broad patterns of individual serum TSH concentrations, an important marker of thyroid functionality. Results Evaluations using simulated data show that feature associations can be correctly recovered by combining random forests and Bayesian networks. The presented model achieves predictive accuracy that is similar to state-of-the-art models (root mean square error of 0.66, mean absolute error of 0.55, coefficient of determination of R2 = 0.15). We identify 62 relevant features from the final random forest model, ranging from general health variables over dietary and genetic factors to physiological, hematological and hemostasis parameters. The Bayesian network model is used to put these features into context and make the black-box random forest model more understandable. Conclusion We demonstrate that the combination of random forest and Bayesian network analysis is helpful to reveal and interpret broad association patterns of individual TSH concentrations. The discovered patterns are in line with state-of-the-art literature. They may be useful for future thyroid research and improved dosing of therapeutics. Approaching epidemiological data with flexible machine learning algorithms is of great value for understanding disease-specific association patterns. However, it can be difficult to correctly extract and understand those patterns due to the lack of model interpretability. We here propose a machine learning workflow that combines random forests with Bayesian network surrogate models to allow for a deeper level of interpretation of complex association patterns. We first evaluate the proposed workflow on synthetic data. We then apply it to data from the large population-based Study of Health in Pomerania (SHIP). Based on this combination, we discover and interpret broad patterns of individual serum TSH concentrations, an important marker of thyroid functionality. We demonstrate that the combination of random forest and Bayesian network analysis is helpful to reveal and interpret broad association patterns of individual TSH concentrations. The discovered patterns are in line with state-of-the-art literature. They may be useful for future thyroid research and improved dosing of therapeutics. |
| Audience | Academic |
| Author | Teumer, Alexander Völzke, Henry Felix, Stephan B. Kaderali, Lars Becker, Ann-Kristin Ittermann, Till Nauck, Matthias Völker, Uwe Nath, Neetika Dörr, Markus |
| AuthorAffiliation | 2 DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany 1 Institute of Bioinformatics, University Medicine Greifswald, Greifswald, Germany 5 Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany 4 Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany 6 Interfaculty Institute of Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany University of Bonn, Bonn-Aachen International Center for IT, GERMANY 3 Institute for Community Medicine, SHIP/Clinical-Epidemiological Research, University Medicine Greifswald, Greifswald, Germany |
| AuthorAffiliation_xml | – name: 1 Institute of Bioinformatics, University Medicine Greifswald, Greifswald, Germany – name: 3 Institute for Community Medicine, SHIP/Clinical-Epidemiological Research, University Medicine Greifswald, Greifswald, Germany – name: 2 DZHK (German Centre for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany – name: 4 Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany – name: 6 Interfaculty Institute of Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany – name: 5 Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany – name: University of Bonn, Bonn-Aachen International Center for IT, GERMANY |
| Author_xml | – sequence: 1 givenname: Ann-Kristin orcidid: 0000-0003-1906-0583 surname: Becker fullname: Becker, Ann-Kristin – sequence: 2 givenname: Till orcidid: 0000-0002-0154-7353 surname: Ittermann fullname: Ittermann, Till – sequence: 3 givenname: Markus orcidid: 0000-0001-7471-475X surname: Dörr fullname: Dörr, Markus – sequence: 4 givenname: Stephan B. surname: Felix fullname: Felix, Stephan B. – sequence: 5 givenname: Matthias orcidid: 0000-0002-6678-7964 surname: Nauck fullname: Nauck, Matthias – sequence: 6 givenname: Alexander orcidid: 0000-0002-8309-094X surname: Teumer fullname: Teumer, Alexander – sequence: 7 givenname: Uwe orcidid: 0000-0002-5689-3448 surname: Völker fullname: Völker, Uwe – sequence: 8 givenname: Henry surname: Völzke fullname: Völzke, Henry – sequence: 9 givenname: Lars orcidid: 0000-0002-2359-2294 surname: Kaderali fullname: Kaderali, Lars – sequence: 10 givenname: Neetika orcidid: 0000-0002-2156-9576 surname: Nath fullname: Nath, Neetika |
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| CitedBy_id | crossref_primary_10_1371_journal_pone_0294489 crossref_primary_10_3390_microorganisms12112249 |
| Cites_doi | 10.1016/j.beem.2017.04.002 10.1515/jpem-2020-0031 10.1007/978-981-13-9939-8_13 10.1023/A:1010933404324 10.1371/journal.pcbi.1008735 10.1038/nrendo.2018.18 10.1007/BF01324255 10.1530/EJE-14-0898 |
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| Copyright | COPYRIGHT 2022 Public Library of Science 2022 Becker et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2022 Becker et al 2022 Becker et al |
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| Snippet | Background Approaching epidemiological data with flexible machine learning algorithms is of great value for understanding disease-specific association... Approaching epidemiological data with flexible machine learning algorithms is of great value for understanding disease-specific association patterns. However,... BackgroundApproaching epidemiological data with flexible machine learning algorithms is of great value for understanding disease-specific association patterns.... Background Approaching epidemiological data with flexible machine learning algorithms is of great value for understanding disease-specific association... |
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| SubjectTerms | Algorithms Analysis Bayesian analysis Bayesian statistical decision theory Biology and Life Sciences Computer and Information Sciences Decision making Decision trees Diet Epidemiology Evaluation Genetic factors Hemostasis Hemostatics Learning algorithms Machine learning Mathematical models Medicine and Health Sciences Model accuracy Modelling Network analysis Physiological effects Pituitary gland Population studies Properties Random variables Research and Analysis Methods Ships Thyroid Thyroid gland Thyroid-stimulating hormone Thyrotropin Workflow |
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| Title | Analysis of epidemiological association patterns of serum thyrotropin by combining random forests and Bayesian networks |
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