Deep transfer learning for reducing health care disparities arising from biomedical data inequality.
Deep transfer learning for reducing health care disparities arising from biomedical data inequality.
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深度迁移学习：减少由生物医学数据不平等引起的医疗差异
DOI：
10.1038/s41467-020-18918-3
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发表时间：
2020-10-12
影响因子：
16.6
通讯作者：
Cui Y
中科院分区：
文献类型：
--
作者：
Gao Y;Cui Y
随着人工智能（AI）越来越多地应用于生物医学研究和临床决策，开发对所有种族都同样有效的公正的AI模型对于预防和减少健康差距至关重要。然而，不同族裔群体之间的生物医学数据不平等将通过数据驱动、基于算法的生物医学研究和临床决策产生新的医疗保健差异。通过对癌症组学数据进行广泛的机器学习实验，我们发现当前流行的多种族机器学习方案容易在种族群体之间产生显着的模型性能差异。研究表明，这些绩效差异是由数据不平等和族裔群体之间的数据分布差异造成的。我们还发现迁移学习可以提高数据劣势族裔的机器学习模型性能，从而为减少族裔间数据不平等导致的医疗保健差异提供了一种有效的方法。开发同样适用于所有族裔群体的机器学习模型对于预防和减少健康差距至关重要。在这里，通过对癌症组学数据进行广泛的机器学习实验，作者发现迁移学习可以提高数据劣势族裔的模型性能。
As artificial intelligence (AI) is increasingly applied to biomedical research and clinical decisions, developing unbiased AI models that work equally well for all ethnic groups is of crucial importance to health disparity prevention and reduction. However, the biomedical data inequality between different ethnic groups is set to generate new health care disparities through data-driven, algorithm-based biomedical research and clinical decisions. Using an extensive set of machine learning experiments on cancer omics data, we find that current prevalent schemes of multiethnic machine learning are prone to generating significant model performance disparities between ethnic groups. We show that these performance disparities are caused by data inequality and data distribution discrepancies between ethnic groups. We also find that transfer learning can improve machine learning model performance for data-disadvantaged ethnic groups, and thus provides an effective approach to reduce health care disparities arising from data inequality among ethnic groups. Developing machine learning models that work equally well for all ethnic groups is of crucial importance to health disparity prevention and reduction. Here, using an extensive set of machine learning experiments on cancer omics data, the authors find that transfer learning can improve model performance for data-disadvantaged ethnic groups.
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影响因子：
64.8
作者：
Capper D;Jones DTW;Sill M;Hovestadt V;Schrimpf D;Sturm D;Koelsche C;Sahm F;Chavez L;Reuss DE;Kratz A;Wefers AK;Huang K;Pajtler KW;Schweizer L;Stichel D;Olar A;Engel NW;Lindenberg K;Harter PN;Braczynski AK;Plate KH;Dohmen H;Garvalov BK;Coras R;Hölsken A;Hewer E;Bewerunge-Hudler M;Schick M;Fischer R;Beschorner R;Schittenhelm J;Staszewski O;Wani K;Varlet P;Pages M;Temming P;Lohmann D;Selt F;Witt H;Milde T;Witt O;Aronica E;Giangaspero F;Rushing E;Scheurlen W;Geisenberger C;Rodriguez FJ;Becker A;Preusser M;Haberler C;Bjerkvig R;Cryan J;Farrell M;Deckert M;Hench J;Frank S;Serrano J;Kannan K;Tsirigos A;Brück W;Hofer S;Brehmer S;Seiz-Rosenhagen M;Hänggi D;Hans V;Rozsnoki S;Hansford JR;Kohlhof P;Kristensen BW;Lechner M;Lopes B;Mawrin C;Ketter R;Kulozik A;Khatib Z;Heppner F;Koch A;Jouvet A;Keohane C;Mühleisen H;Mueller W;Pohl U;Prinz M;Benner A;Zapatka M;Gottardo NG;Driever PH;Kramm CM;Müller HL;Rutkowski S;von Hoff K;Frühwald MC;Gnekow A;Fleischhack G;Tippelt S;Calaminus G;Monoranu CM;Perry A;Jones C;Jacques TS;Radlwimmer B;Gessi M;Pietsch T;Schramm J;Schackert G;Westphal M;Reifenberger G;Wesseling P;Weller M;Collins VP;Blümcke I;Bendszus M;Debus J;Huang A;Jabado N;Northcott PA;Paulus W;Gajjar A;Robinson GW;Taylor MD;Jaunmuktane Z;Ryzhova M;Platten M;Unterberg A;Wick W;Karajannis MA;Mittelbronn M;Acker T;Hartmann C;Aldape K;Schüller U;Buslei R;Lichter P;Kool M;Herold-Mende C;Ellison DW;Hasselblatt M;Snuderl M;Brandner S;Korshunov A;von Deimling A;Pfister SM
通讯作者：
Pfister SM
影响因子：
64.5
作者：
Malta TM;Sokolov A;Gentles AJ;Burzykowski T;Poisson L;Weinstein JN;Kamińska B;Huelsken J;Omberg L;Gevaert O;Colaprico A;Czerwińska P;Mazurek S;Mishra L;Heyn H;Krasnitz A;Godwin AK;Lazar AJ;Cancer Genome Atlas Research Network;Stuart JM;Hoadley KA;Laird PW;Noushmehr H;Wiznerowicz M
通讯作者：
Wiznerowicz M
影响因子：
64.5
作者：
Liu J;Lichtenberg T;Hoadley KA;Poisson LM;Lazar AJ;Cherniack AD;Kovatich AJ;Benz CC;Levine DA;Lee AV;Omberg L;Wolf DM;Shriver CD;Thorsson V;Cancer Genome Atlas Research Network;Hu H
通讯作者：
Hu H
影响因子：
5
作者：
Daumé, H;Marcu, D
通讯作者：
Marcu, D
影响因子：
--
作者：
Phung, Son Lam;Bouzerdoum, Abdesselam
通讯作者：
Bouzerdoum, Abdesselam