Synthetic data generation: State of the art in health care domain

Recent progress in artificial intelligence and machine learning has led to the growth of research in every aspect of life including the health care domain. However, privacy risks and legislations hinder the availability of patient data to researchers. Synthetic data (SD) has been regarded as a priva...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Murtaza, Hajra [verfasserIn] Ahmed, Musharif [verfasserIn] Khan, Naurin Farooq [verfasserIn] Murtaza, Ghulam [verfasserIn] Zafar, Saad [verfasserIn] Bano, Ambreen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Synthetic data Health informatics Data privacy Privacy preserving data publishing Medical informatics Generative adversarial networks Electronic health records

Übergeordnetes Werk:	Enthalten in: Computer science review - Amsterdam [u.a.] : Elsevier, 2007, 48
Übergeordnetes Werk:	volume:48

DOI / URN:	10.1016/j.cosrev.2023.100546

Katalog-ID:	ELV009764895

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650		4	\|a Health informatics
650		4	\|a Data privacy
650		4	\|a Privacy preserving data publishing
650		4	\|a Medical informatics
650		4	\|a Generative adversarial networks
650		4	\|a Electronic health records
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700	1		\|a Zafar, Saad \|e verfasserin \|4 aut
700	1		\|a Bano, Ambreen \|e verfasserin \|4 aut
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allfields	10.1016/j.cosrev.2023.100546 doi (DE-627)ELV009764895 (ELSEVIER)S1574-0137(23)00013-8 DE-627 ger DE-627 rda eng 004 VZ Murtaza, Hajra verfasserin (orcid)0000-0002-9516-9110 aut Synthetic data generation: State of the art in health care domain 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recent progress in artificial intelligence and machine learning has led to the growth of research in every aspect of life including the health care domain. However, privacy risks and legislations hinder the availability of patient data to researchers. Synthetic data (SD) has been regarded as a privacy-safe alternative to real data and has lately been employed in many research and academic endeavors. This growing body of research needs to be consolidated for the researchers and practitioners to gain a quick and fruitful comprehension of the state of the art in synthetic data generation in health care. The purpose of this study is to collate and synthesize the current state of synthetic data generation following a narrative review of 70 peer-reviewed studies discussing privacy-preserving synthetic medical data generation techniques. The literature shows the effectiveness of synthetic datasets for different applications in research, academics, and testing according to existing statistical and task-based utility metrics. However, the focus on longitudinal synthetic data seems deficient. Moreover, a unified metric for generic quality assessment of synthetic data is lacking. The results of this review will serve as a quick reference guide for the researchers and practitioners in the healthcare domain to select a suitable synthetic data strategy for their application based on its strengths and weaknesses and pave the path for further research and development in healthcare. Synthetic data Health informatics Data privacy Privacy preserving data publishing Medical informatics Generative adversarial networks Electronic health records Ahmed, Musharif verfasserin aut Khan, Naurin Farooq verfasserin aut Murtaza, Ghulam verfasserin aut Zafar, Saad verfasserin aut Bano, Ambreen verfasserin aut Enthalten in Computer science review Amsterdam [u.a.] : Elsevier, 2007 48 Online-Ressource (DE-627)546503101 (DE-600)2390720-4 (DE-576)284748439 1876-7745 nnns volume:48 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48
spelling	10.1016/j.cosrev.2023.100546 doi (DE-627)ELV009764895 (ELSEVIER)S1574-0137(23)00013-8 DE-627 ger DE-627 rda eng 004 VZ Murtaza, Hajra verfasserin (orcid)0000-0002-9516-9110 aut Synthetic data generation: State of the art in health care domain 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recent progress in artificial intelligence and machine learning has led to the growth of research in every aspect of life including the health care domain. However, privacy risks and legislations hinder the availability of patient data to researchers. Synthetic data (SD) has been regarded as a privacy-safe alternative to real data and has lately been employed in many research and academic endeavors. This growing body of research needs to be consolidated for the researchers and practitioners to gain a quick and fruitful comprehension of the state of the art in synthetic data generation in health care. The purpose of this study is to collate and synthesize the current state of synthetic data generation following a narrative review of 70 peer-reviewed studies discussing privacy-preserving synthetic medical data generation techniques. The literature shows the effectiveness of synthetic datasets for different applications in research, academics, and testing according to existing statistical and task-based utility metrics. However, the focus on longitudinal synthetic data seems deficient. Moreover, a unified metric for generic quality assessment of synthetic data is lacking. The results of this review will serve as a quick reference guide for the researchers and practitioners in the healthcare domain to select a suitable synthetic data strategy for their application based on its strengths and weaknesses and pave the path for further research and development in healthcare. Synthetic data Health informatics Data privacy Privacy preserving data publishing Medical informatics Generative adversarial networks Electronic health records Ahmed, Musharif verfasserin aut Khan, Naurin Farooq verfasserin aut Murtaza, Ghulam verfasserin aut Zafar, Saad verfasserin aut Bano, Ambreen verfasserin aut Enthalten in Computer science review Amsterdam [u.a.] : Elsevier, 2007 48 Online-Ressource (DE-627)546503101 (DE-600)2390720-4 (DE-576)284748439 1876-7745 nnns volume:48 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48
allfields_unstemmed	10.1016/j.cosrev.2023.100546 doi (DE-627)ELV009764895 (ELSEVIER)S1574-0137(23)00013-8 DE-627 ger DE-627 rda eng 004 VZ Murtaza, Hajra verfasserin (orcid)0000-0002-9516-9110 aut Synthetic data generation: State of the art in health care domain 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recent progress in artificial intelligence and machine learning has led to the growth of research in every aspect of life including the health care domain. However, privacy risks and legislations hinder the availability of patient data to researchers. Synthetic data (SD) has been regarded as a privacy-safe alternative to real data and has lately been employed in many research and academic endeavors. This growing body of research needs to be consolidated for the researchers and practitioners to gain a quick and fruitful comprehension of the state of the art in synthetic data generation in health care. The purpose of this study is to collate and synthesize the current state of synthetic data generation following a narrative review of 70 peer-reviewed studies discussing privacy-preserving synthetic medical data generation techniques. The literature shows the effectiveness of synthetic datasets for different applications in research, academics, and testing according to existing statistical and task-based utility metrics. However, the focus on longitudinal synthetic data seems deficient. Moreover, a unified metric for generic quality assessment of synthetic data is lacking. The results of this review will serve as a quick reference guide for the researchers and practitioners in the healthcare domain to select a suitable synthetic data strategy for their application based on its strengths and weaknesses and pave the path for further research and development in healthcare. Synthetic data Health informatics Data privacy Privacy preserving data publishing Medical informatics Generative adversarial networks Electronic health records Ahmed, Musharif verfasserin aut Khan, Naurin Farooq verfasserin aut Murtaza, Ghulam verfasserin aut Zafar, Saad verfasserin aut Bano, Ambreen verfasserin aut Enthalten in Computer science review Amsterdam [u.a.] : Elsevier, 2007 48 Online-Ressource (DE-627)546503101 (DE-600)2390720-4 (DE-576)284748439 1876-7745 nnns volume:48 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48
allfieldsGer	10.1016/j.cosrev.2023.100546 doi (DE-627)ELV009764895 (ELSEVIER)S1574-0137(23)00013-8 DE-627 ger DE-627 rda eng 004 VZ Murtaza, Hajra verfasserin (orcid)0000-0002-9516-9110 aut Synthetic data generation: State of the art in health care domain 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recent progress in artificial intelligence and machine learning has led to the growth of research in every aspect of life including the health care domain. However, privacy risks and legislations hinder the availability of patient data to researchers. Synthetic data (SD) has been regarded as a privacy-safe alternative to real data and has lately been employed in many research and academic endeavors. This growing body of research needs to be consolidated for the researchers and practitioners to gain a quick and fruitful comprehension of the state of the art in synthetic data generation in health care. The purpose of this study is to collate and synthesize the current state of synthetic data generation following a narrative review of 70 peer-reviewed studies discussing privacy-preserving synthetic medical data generation techniques. The literature shows the effectiveness of synthetic datasets for different applications in research, academics, and testing according to existing statistical and task-based utility metrics. However, the focus on longitudinal synthetic data seems deficient. Moreover, a unified metric for generic quality assessment of synthetic data is lacking. The results of this review will serve as a quick reference guide for the researchers and practitioners in the healthcare domain to select a suitable synthetic data strategy for their application based on its strengths and weaknesses and pave the path for further research and development in healthcare. Synthetic data Health informatics Data privacy Privacy preserving data publishing Medical informatics Generative adversarial networks Electronic health records Ahmed, Musharif verfasserin aut Khan, Naurin Farooq verfasserin aut Murtaza, Ghulam verfasserin aut Zafar, Saad verfasserin aut Bano, Ambreen verfasserin aut Enthalten in Computer science review Amsterdam [u.a.] : Elsevier, 2007 48 Online-Ressource (DE-627)546503101 (DE-600)2390720-4 (DE-576)284748439 1876-7745 nnns volume:48 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48
allfieldsSound	10.1016/j.cosrev.2023.100546 doi (DE-627)ELV009764895 (ELSEVIER)S1574-0137(23)00013-8 DE-627 ger DE-627 rda eng 004 VZ Murtaza, Hajra verfasserin (orcid)0000-0002-9516-9110 aut Synthetic data generation: State of the art in health care domain 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recent progress in artificial intelligence and machine learning has led to the growth of research in every aspect of life including the health care domain. However, privacy risks and legislations hinder the availability of patient data to researchers. Synthetic data (SD) has been regarded as a privacy-safe alternative to real data and has lately been employed in many research and academic endeavors. This growing body of research needs to be consolidated for the researchers and practitioners to gain a quick and fruitful comprehension of the state of the art in synthetic data generation in health care. The purpose of this study is to collate and synthesize the current state of synthetic data generation following a narrative review of 70 peer-reviewed studies discussing privacy-preserving synthetic medical data generation techniques. The literature shows the effectiveness of synthetic datasets for different applications in research, academics, and testing according to existing statistical and task-based utility metrics. However, the focus on longitudinal synthetic data seems deficient. Moreover, a unified metric for generic quality assessment of synthetic data is lacking. The results of this review will serve as a quick reference guide for the researchers and practitioners in the healthcare domain to select a suitable synthetic data strategy for their application based on its strengths and weaknesses and pave the path for further research and development in healthcare. Synthetic data Health informatics Data privacy Privacy preserving data publishing Medical informatics Generative adversarial networks Electronic health records Ahmed, Musharif verfasserin aut Khan, Naurin Farooq verfasserin aut Murtaza, Ghulam verfasserin aut Zafar, Saad verfasserin aut Bano, Ambreen verfasserin aut Enthalten in Computer science review Amsterdam [u.a.] : Elsevier, 2007 48 Online-Ressource (DE-627)546503101 (DE-600)2390720-4 (DE-576)284748439 1876-7745 nnns volume:48 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4338 GBV_ILN_4393 GBV_ILN_4700 AR 48
language	English
source	Enthalten in Computer science review 48 volume:48
sourceStr	Enthalten in Computer science review 48 volume:48
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Synthetic data Health informatics Data privacy Privacy preserving data publishing Medical informatics Generative adversarial networks Electronic health records
dewey-raw	004
isfreeaccess_bool	false
container_title	Computer science review
authorswithroles_txt_mv	Murtaza, Hajra @@aut@@ Ahmed, Musharif @@aut@@ Khan, Naurin Farooq @@aut@@ Murtaza, Ghulam @@aut@@ Zafar, Saad @@aut@@ Bano, Ambreen @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	546503101
dewey-sort	14
id	ELV009764895
language_de	englisch
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topic_title	004 VZ Synthetic data generation: State of the art in health care domain Synthetic data Health informatics Data privacy Privacy preserving data publishing Medical informatics Generative adversarial networks Electronic health records
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title_sort	synthetic data generation: state of the art in health care domain
title_auth	Synthetic data generation: State of the art in health care domain
abstract	Recent progress in artificial intelligence and machine learning has led to the growth of research in every aspect of life including the health care domain. However, privacy risks and legislations hinder the availability of patient data to researchers. Synthetic data (SD) has been regarded as a privacy-safe alternative to real data and has lately been employed in many research and academic endeavors. This growing body of research needs to be consolidated for the researchers and practitioners to gain a quick and fruitful comprehension of the state of the art in synthetic data generation in health care. The purpose of this study is to collate and synthesize the current state of synthetic data generation following a narrative review of 70 peer-reviewed studies discussing privacy-preserving synthetic medical data generation techniques. The literature shows the effectiveness of synthetic datasets for different applications in research, academics, and testing according to existing statistical and task-based utility metrics. However, the focus on longitudinal synthetic data seems deficient. Moreover, a unified metric for generic quality assessment of synthetic data is lacking. The results of this review will serve as a quick reference guide for the researchers and practitioners in the healthcare domain to select a suitable synthetic data strategy for their application based on its strengths and weaknesses and pave the path for further research and development in healthcare.
abstractGer	Recent progress in artificial intelligence and machine learning has led to the growth of research in every aspect of life including the health care domain. However, privacy risks and legislations hinder the availability of patient data to researchers. Synthetic data (SD) has been regarded as a privacy-safe alternative to real data and has lately been employed in many research and academic endeavors. This growing body of research needs to be consolidated for the researchers and practitioners to gain a quick and fruitful comprehension of the state of the art in synthetic data generation in health care. The purpose of this study is to collate and synthesize the current state of synthetic data generation following a narrative review of 70 peer-reviewed studies discussing privacy-preserving synthetic medical data generation techniques. The literature shows the effectiveness of synthetic datasets for different applications in research, academics, and testing according to existing statistical and task-based utility metrics. However, the focus on longitudinal synthetic data seems deficient. Moreover, a unified metric for generic quality assessment of synthetic data is lacking. The results of this review will serve as a quick reference guide for the researchers and practitioners in the healthcare domain to select a suitable synthetic data strategy for their application based on its strengths and weaknesses and pave the path for further research and development in healthcare.
abstract_unstemmed	Recent progress in artificial intelligence and machine learning has led to the growth of research in every aspect of life including the health care domain. However, privacy risks and legislations hinder the availability of patient data to researchers. Synthetic data (SD) has been regarded as a privacy-safe alternative to real data and has lately been employed in many research and academic endeavors. This growing body of research needs to be consolidated for the researchers and practitioners to gain a quick and fruitful comprehension of the state of the art in synthetic data generation in health care. The purpose of this study is to collate and synthesize the current state of synthetic data generation following a narrative review of 70 peer-reviewed studies discussing privacy-preserving synthetic medical data generation techniques. The literature shows the effectiveness of synthetic datasets for different applications in research, academics, and testing according to existing statistical and task-based utility metrics. However, the focus on longitudinal synthetic data seems deficient. Moreover, a unified metric for generic quality assessment of synthetic data is lacking. The results of this review will serve as a quick reference guide for the researchers and practitioners in the healthcare domain to select a suitable synthetic data strategy for their application based on its strengths and weaknesses and pave the path for further research and development in healthcare.
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title_short	Synthetic data generation: State of the art in health care domain
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author2	Ahmed, Musharif Khan, Naurin Farooq Murtaza, Ghulam Zafar, Saad Bano, Ambreen
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