From hype to reality: data science enabling personalized medicine

Abstract Background Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individ...
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Autor*in:	Holger Fröhlich [verfasserIn] Rudi Balling [verfasserIn] Niko Beerenwinkel [verfasserIn] Oliver Kohlbacher [verfasserIn] Santosh Kumar [verfasserIn] Thomas Lengauer [verfasserIn] Marloes H. Maathuis [verfasserIn] Yves Moreau [verfasserIn] Susan A. Murphy [verfasserIn] Teresa M. Przytycka [verfasserIn] Michael Rebhan [verfasserIn] Hannes Röst [verfasserIn] Andreas Schuppert [verfasserIn] Matthias Schwab [verfasserIn] Rainer Spang [verfasserIn] Daniel Stekhoven [verfasserIn] Jimeng Sun [verfasserIn] Andreas Weber [verfasserIn] Daniel Ziemek [verfasserIn] Blaz Zupan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Personalized medicine Precision medicine Stratified medicine P4 medicine Machine learning Artificial intelligence

Übergeordnetes Werk:	In: BMC Medicine - BMC, 2003, 16(2018), 1, Seite 15
Übergeordnetes Werk:	volume:16 ; year:2018 ; number:1 ; pages:15

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s12916-018-1122-7

Katalog-ID:	DOAJ068457227

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allfields	10.1186/s12916-018-1122-7 doi (DE-627)DOAJ068457227 (DE-599)DOAJ55faf984462542e699767e229ee03c8e DE-627 ger DE-627 rakwb eng Holger Fröhlich verfasserin aut From hype to reality: data science enabling personalized medicine 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of ‘big data’ and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future. Conclusions There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice. Personalized medicine Precision medicine Stratified medicine P4 medicine Machine learning Artificial intelligence Medicine R Rudi Balling verfasserin aut Niko Beerenwinkel verfasserin aut Oliver Kohlbacher verfasserin aut Santosh Kumar verfasserin aut Thomas Lengauer verfasserin aut Marloes H. Maathuis verfasserin aut Yves Moreau verfasserin aut Susan A. Murphy verfasserin aut Teresa M. Przytycka verfasserin aut Michael Rebhan verfasserin aut Hannes Röst verfasserin aut Andreas Schuppert verfasserin aut Matthias Schwab verfasserin aut Rainer Spang verfasserin aut Daniel Stekhoven verfasserin aut Jimeng Sun verfasserin aut Andreas Weber verfasserin aut Daniel Ziemek verfasserin aut Blaz Zupan verfasserin aut In BMC Medicine BMC, 2003 16(2018), 1, Seite 15 (DE-627)377271225 (DE-600)2131669-7 17417015 nnns volume:16 year:2018 number:1 pages:15 https://doi.org/10.1186/s12916-018-1122-7 kostenfrei https://doaj.org/article/55faf984462542e699767e229ee03c8e kostenfrei http://link.springer.com/article/10.1186/s12916-018-1122-7 kostenfrei https://doaj.org/toc/1741-7015 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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 16 2018 1 15
spelling	10.1186/s12916-018-1122-7 doi (DE-627)DOAJ068457227 (DE-599)DOAJ55faf984462542e699767e229ee03c8e DE-627 ger DE-627 rakwb eng Holger Fröhlich verfasserin aut From hype to reality: data science enabling personalized medicine 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of ‘big data’ and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future. Conclusions There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice. Personalized medicine Precision medicine Stratified medicine P4 medicine Machine learning Artificial intelligence Medicine R Rudi Balling verfasserin aut Niko Beerenwinkel verfasserin aut Oliver Kohlbacher verfasserin aut Santosh Kumar verfasserin aut Thomas Lengauer verfasserin aut Marloes H. Maathuis verfasserin aut Yves Moreau verfasserin aut Susan A. Murphy verfasserin aut Teresa M. Przytycka verfasserin aut Michael Rebhan verfasserin aut Hannes Röst verfasserin aut Andreas Schuppert verfasserin aut Matthias Schwab verfasserin aut Rainer Spang verfasserin aut Daniel Stekhoven verfasserin aut Jimeng Sun verfasserin aut Andreas Weber verfasserin aut Daniel Ziemek verfasserin aut Blaz Zupan verfasserin aut In BMC Medicine BMC, 2003 16(2018), 1, Seite 15 (DE-627)377271225 (DE-600)2131669-7 17417015 nnns volume:16 year:2018 number:1 pages:15 https://doi.org/10.1186/s12916-018-1122-7 kostenfrei https://doaj.org/article/55faf984462542e699767e229ee03c8e kostenfrei http://link.springer.com/article/10.1186/s12916-018-1122-7 kostenfrei https://doaj.org/toc/1741-7015 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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 16 2018 1 15
allfields_unstemmed	10.1186/s12916-018-1122-7 doi (DE-627)DOAJ068457227 (DE-599)DOAJ55faf984462542e699767e229ee03c8e DE-627 ger DE-627 rakwb eng Holger Fröhlich verfasserin aut From hype to reality: data science enabling personalized medicine 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of ‘big data’ and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future. Conclusions There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice. Personalized medicine Precision medicine Stratified medicine P4 medicine Machine learning Artificial intelligence Medicine R Rudi Balling verfasserin aut Niko Beerenwinkel verfasserin aut Oliver Kohlbacher verfasserin aut Santosh Kumar verfasserin aut Thomas Lengauer verfasserin aut Marloes H. Maathuis verfasserin aut Yves Moreau verfasserin aut Susan A. Murphy verfasserin aut Teresa M. Przytycka verfasserin aut Michael Rebhan verfasserin aut Hannes Röst verfasserin aut Andreas Schuppert verfasserin aut Matthias Schwab verfasserin aut Rainer Spang verfasserin aut Daniel Stekhoven verfasserin aut Jimeng Sun verfasserin aut Andreas Weber verfasserin aut Daniel Ziemek verfasserin aut Blaz Zupan verfasserin aut In BMC Medicine BMC, 2003 16(2018), 1, Seite 15 (DE-627)377271225 (DE-600)2131669-7 17417015 nnns volume:16 year:2018 number:1 pages:15 https://doi.org/10.1186/s12916-018-1122-7 kostenfrei https://doaj.org/article/55faf984462542e699767e229ee03c8e kostenfrei http://link.springer.com/article/10.1186/s12916-018-1122-7 kostenfrei https://doaj.org/toc/1741-7015 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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 16 2018 1 15
allfieldsGer	10.1186/s12916-018-1122-7 doi (DE-627)DOAJ068457227 (DE-599)DOAJ55faf984462542e699767e229ee03c8e DE-627 ger DE-627 rakwb eng Holger Fröhlich verfasserin aut From hype to reality: data science enabling personalized medicine 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of ‘big data’ and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future. Conclusions There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice. Personalized medicine Precision medicine Stratified medicine P4 medicine Machine learning Artificial intelligence Medicine R Rudi Balling verfasserin aut Niko Beerenwinkel verfasserin aut Oliver Kohlbacher verfasserin aut Santosh Kumar verfasserin aut Thomas Lengauer verfasserin aut Marloes H. Maathuis verfasserin aut Yves Moreau verfasserin aut Susan A. Murphy verfasserin aut Teresa M. Przytycka verfasserin aut Michael Rebhan verfasserin aut Hannes Röst verfasserin aut Andreas Schuppert verfasserin aut Matthias Schwab verfasserin aut Rainer Spang verfasserin aut Daniel Stekhoven verfasserin aut Jimeng Sun verfasserin aut Andreas Weber verfasserin aut Daniel Ziemek verfasserin aut Blaz Zupan verfasserin aut In BMC Medicine BMC, 2003 16(2018), 1, Seite 15 (DE-627)377271225 (DE-600)2131669-7 17417015 nnns volume:16 year:2018 number:1 pages:15 https://doi.org/10.1186/s12916-018-1122-7 kostenfrei https://doaj.org/article/55faf984462542e699767e229ee03c8e kostenfrei http://link.springer.com/article/10.1186/s12916-018-1122-7 kostenfrei https://doaj.org/toc/1741-7015 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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 16 2018 1 15
allfieldsSound	10.1186/s12916-018-1122-7 doi (DE-627)DOAJ068457227 (DE-599)DOAJ55faf984462542e699767e229ee03c8e DE-627 ger DE-627 rakwb eng Holger Fröhlich verfasserin aut From hype to reality: data science enabling personalized medicine 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of ‘big data’ and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future. Conclusions There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice. Personalized medicine Precision medicine Stratified medicine P4 medicine Machine learning Artificial intelligence Medicine R Rudi Balling verfasserin aut Niko Beerenwinkel verfasserin aut Oliver Kohlbacher verfasserin aut Santosh Kumar verfasserin aut Thomas Lengauer verfasserin aut Marloes H. Maathuis verfasserin aut Yves Moreau verfasserin aut Susan A. Murphy verfasserin aut Teresa M. Przytycka verfasserin aut Michael Rebhan verfasserin aut Hannes Röst verfasserin aut Andreas Schuppert verfasserin aut Matthias Schwab verfasserin aut Rainer Spang verfasserin aut Daniel Stekhoven verfasserin aut Jimeng Sun verfasserin aut Andreas Weber verfasserin aut Daniel Ziemek verfasserin aut Blaz Zupan verfasserin aut In BMC Medicine BMC, 2003 16(2018), 1, Seite 15 (DE-627)377271225 (DE-600)2131669-7 17417015 nnns volume:16 year:2018 number:1 pages:15 https://doi.org/10.1186/s12916-018-1122-7 kostenfrei https://doaj.org/article/55faf984462542e699767e229ee03c8e kostenfrei http://link.springer.com/article/10.1186/s12916-018-1122-7 kostenfrei https://doaj.org/toc/1741-7015 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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 16 2018 1 15
language	English
source	In BMC Medicine 16(2018), 1, Seite 15 volume:16 year:2018 number:1 pages:15
sourceStr	In BMC Medicine 16(2018), 1, Seite 15 volume:16 year:2018 number:1 pages:15
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Personalized medicine Precision medicine Stratified medicine P4 medicine Machine learning Artificial intelligence Medicine R
isfreeaccess_bool	true
container_title	BMC Medicine
authorswithroles_txt_mv	Holger Fröhlich @@aut@@ Rudi Balling @@aut@@ Niko Beerenwinkel @@aut@@ Oliver Kohlbacher @@aut@@ Santosh Kumar @@aut@@ Thomas Lengauer @@aut@@ Marloes H. Maathuis @@aut@@ Yves Moreau @@aut@@ Susan A. Murphy @@aut@@ Teresa M. Przytycka @@aut@@ Michael Rebhan @@aut@@ Hannes Röst @@aut@@ Andreas Schuppert @@aut@@ Matthias Schwab @@aut@@ Rainer Spang @@aut@@ Daniel Stekhoven @@aut@@ Jimeng Sun @@aut@@ Andreas Weber @@aut@@ Daniel Ziemek @@aut@@ Blaz Zupan @@aut@@
publishDateDaySort_date	2018-01-01T00:00:00Z
hierarchy_top_id	377271225
id	DOAJ068457227
language_de	englisch
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author	Holger Fröhlich
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topic_title	From hype to reality: data science enabling personalized medicine Personalized medicine Precision medicine Stratified medicine P4 medicine Machine learning Artificial intelligence
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title	From hype to reality: data science enabling personalized medicine
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author_browse	Holger Fröhlich Rudi Balling Niko Beerenwinkel Oliver Kohlbacher Santosh Kumar Thomas Lengauer Marloes H. Maathuis Yves Moreau Susan A. Murphy Teresa M. Przytycka Michael Rebhan Hannes Röst Andreas Schuppert Matthias Schwab Rainer Spang Daniel Stekhoven Jimeng Sun Andreas Weber Daniel Ziemek Blaz Zupan
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title_sort	from hype to reality: data science enabling personalized medicine
title_auth	From hype to reality: data science enabling personalized medicine
abstract	Abstract Background Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of ‘big data’ and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future. Conclusions There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice.
abstractGer	Abstract Background Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of ‘big data’ and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future. Conclusions There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice.
abstract_unstemmed	Abstract Background Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of ‘big data’ and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future. Conclusions There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice.
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title_short	From hype to reality: data science enabling personalized medicine
url	https://doi.org/10.1186/s12916-018-1122-7 https://doaj.org/article/55faf984462542e699767e229ee03c8e http://link.springer.com/article/10.1186/s12916-018-1122-7 https://doaj.org/toc/1741-7015
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