Contribution of Synthetic Data Generation towards an Improved Patient Stratification in Palliative Care
AI model development for synthetic data generation to improve Machine Learning (ML) methodologies is an integral part of research in Computer Science and is currently being transferred to related medical fields, such as Systems Medicine and Medical Informatics. In general, the idea of personalized d...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Journal of Personalized Medicine |
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| Online Access: | https://www.mdpi.com/2075-4426/12/8/1278 |
| _version_ | 1797409406651990016 |
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| author | Waldemar Hahn Katharina Schütte Kristian Schultz Olaf Wolkenhauer Martin Sedlmayr Ulrich Schuler Martin Eichler Saptarshi Bej Markus Wolfien |
| author_facet | Waldemar Hahn Katharina Schütte Kristian Schultz Olaf Wolkenhauer Martin Sedlmayr Ulrich Schuler Martin Eichler Saptarshi Bej Markus Wolfien |
| author_sort | Waldemar Hahn |
| collection | DOAJ |
| description | AI model development for synthetic data generation to improve Machine Learning (ML) methodologies is an integral part of research in Computer Science and is currently being transferred to related medical fields, such as Systems Medicine and Medical Informatics. In general, the idea of personalized decision-making support based on patient data has driven the motivation of researchers in the medical domain for more than a decade, but the overall sparsity and scarcity of data are still major limitations. This is in contrast to currently applied technology that allows us to generate and analyze patient data in diverse forms, such as tabular data on health records, medical images, genomics data, or even audio and video. One solution arising to overcome these data limitations in relation to medical records is the synthetic generation of tabular data based on real world data. Consequently, ML-assisted decision-support can be interpreted more conveniently, using more relevant patient data at hand. At a methodological level, several state-of-the-art ML algorithms generate and derive decisions from such data. However, there remain key issues that hinder a broad practical implementation in real-life clinical settings. In this review, we will give for the first time insights towards current perspectives and potential impacts of using synthetic data generation in palliative care screening because it is a challenging prime example of highly individualized, sparsely available patient information. Taken together, the reader will obtain initial starting points and suitable solutions relevant for generating and using synthetic data for ML-based screenings in palliative care and beyond. |
| first_indexed | 2024-03-09T04:15:12Z |
| format | Article |
| id | doaj.art-928779c7cc1141d6bfbcceb3b58889b7 |
| institution | Directory Open Access Journal |
| issn | 2075-4426 |
| language | English |
| last_indexed | 2024-03-09T04:15:12Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Personalized Medicine |
| spelling | doaj.art-928779c7cc1141d6bfbcceb3b58889b72023-12-03T13:56:07ZengMDPI AGJournal of Personalized Medicine2075-44262022-08-01128127810.3390/jpm12081278Contribution of Synthetic Data Generation towards an Improved Patient Stratification in Palliative CareWaldemar Hahn0Katharina Schütte1Kristian Schultz2Olaf Wolkenhauer3Martin Sedlmayr4Ulrich Schuler5Martin Eichler6Saptarshi Bej7Markus Wolfien8Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307 Dresden, GermanyUniversity Palliative Center, University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307 Dresden, GermanyDepartment of Systems Biology and Bioinformatics, University of Rostock, Universitätsplatz 1, 18051 Rostock, GermanyDepartment of Systems Biology and Bioinformatics, University of Rostock, Universitätsplatz 1, 18051 Rostock, GermanyInstitute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307 Dresden, GermanyUniversity Palliative Center, University Hospital Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307 Dresden, GermanyNational Center for Tumor Diseases Dresden (NCT/UCC), Fetscherstraße 74, 01307 Dresden, GermanyDepartment of Systems Biology and Bioinformatics, University of Rostock, Universitätsplatz 1, 18051 Rostock, GermanyInstitute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Fetscherstraße 74, 01307 Dresden, GermanyAI model development for synthetic data generation to improve Machine Learning (ML) methodologies is an integral part of research in Computer Science and is currently being transferred to related medical fields, such as Systems Medicine and Medical Informatics. In general, the idea of personalized decision-making support based on patient data has driven the motivation of researchers in the medical domain for more than a decade, but the overall sparsity and scarcity of data are still major limitations. This is in contrast to currently applied technology that allows us to generate and analyze patient data in diverse forms, such as tabular data on health records, medical images, genomics data, or even audio and video. One solution arising to overcome these data limitations in relation to medical records is the synthetic generation of tabular data based on real world data. Consequently, ML-assisted decision-support can be interpreted more conveniently, using more relevant patient data at hand. At a methodological level, several state-of-the-art ML algorithms generate and derive decisions from such data. However, there remain key issues that hinder a broad practical implementation in real-life clinical settings. In this review, we will give for the first time insights towards current perspectives and potential impacts of using synthetic data generation in palliative care screening because it is a challenging prime example of highly individualized, sparsely available patient information. Taken together, the reader will obtain initial starting points and suitable solutions relevant for generating and using synthetic data for ML-based screenings in palliative care and beyond.https://www.mdpi.com/2075-4426/12/8/1278palliative carescreeningpersonalized medicinesynthetic data generationGANs |
| spellingShingle | Waldemar Hahn Katharina Schütte Kristian Schultz Olaf Wolkenhauer Martin Sedlmayr Ulrich Schuler Martin Eichler Saptarshi Bej Markus Wolfien Contribution of Synthetic Data Generation towards an Improved Patient Stratification in Palliative Care Journal of Personalized Medicine palliative care screening personalized medicine synthetic data generation GANs |
| title | Contribution of Synthetic Data Generation towards an Improved Patient Stratification in Palliative Care |
| title_full | Contribution of Synthetic Data Generation towards an Improved Patient Stratification in Palliative Care |
| title_fullStr | Contribution of Synthetic Data Generation towards an Improved Patient Stratification in Palliative Care |
| title_full_unstemmed | Contribution of Synthetic Data Generation towards an Improved Patient Stratification in Palliative Care |
| title_short | Contribution of Synthetic Data Generation towards an Improved Patient Stratification in Palliative Care |
| title_sort | contribution of synthetic data generation towards an improved patient stratification in palliative care |
| topic | palliative care screening personalized medicine synthetic data generation GANs |
| url | https://www.mdpi.com/2075-4426/12/8/1278 |
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