Authors: Ittai Dayan Holger R. Roth Aoxiao Zhong Ahmed Harouni Amilcare Gentili Anas Z. Abidin Andrew Liu Anthony Beardsworth Costa Bradford J. Wood Chien-Sung Tsai Chih-Hung Wang Chun-Nan Hsu C. K. Lee Peiying Ruan Daguang Xu Dufan Wu Eddie Huang Felipe Campos Kitamura Griffin Lacey Gustavo César de Antônio Corradi Gustavo Nino Hao-Hsin Shin Hirofumi Obinata Hui Ren Jason C. Crane Jesse Tetreault Jiahui Guan John W. Garrett Joshua D. Kaggie Jung Gil Park Keith Dreyer Krishna Juluru Kristopher Kersten Marcio Aloisio Bezerra Cavalcanti Rockenbach Marius George Linguraru Masoom A. Haider Meena AbdelMaseeh Nicola Rieke Pablo F. Damasceno Pedro Mario Cruz e Silva Pochuan Wang Sheng Xu Shuichi Kawano Sira Sriswasdi Soo Young Park Thomas M. Grist Varun Buch Watsamon Jantarabenjakul Weichung Wang Won Young Tak Xiang Li Xihong Lin Young Joon Kwon Abood Quraini Andrew Feng Andrew N. Priest Baris Turkbey Benjamin Glicksberg Bernardo Bizzo Byung Seok Kim Carlos Tor-Díez Chia-Cheng Lee Chia-Jung Hsu Chin Lin Chiu-Ling Lai Christopher P. Hess Colin Compas Deepeksha Bhatia Eric K. Oermann Evan Leibovitz Hisashi Sasaki Hitoshi Mori Isaac Yang Jae Ho Sohn Krishna Nand Keshava Murthy Li-Chen Fu Matheus Ribeiro Furtado de Mendonça Mike Fralick Min Kyu Kang Mohammad Adil Natalie Gangai Peerapon Vateekul Pierre Elnajjar Sarah Hickman Sharmila Majumdar Shelley L. McLeod Sheridan Reed Stefan Gräf Stephanie Harmon Tatsuya Kodama Thanyawee Puthanakit Tony Mazzulli Vitor Lima de Lavor Yothin Rakvongthai Yu Rim Lee Yuhong Wen Fiona J. Gilbert Mona G. Flores Quanzheng Li Authors: Итај Дајан Холгер Р. Рот Aoxiao Zhong Ахмед Харуни Amilcare Gentili Anas Z. Abidin Andrew Liu Ентони Беардсворт Коста Бредфорд Џ. Вуд Chien-Sung Tsai Chih-Hung Wang Chun-Nan Hsu К. К. Ли Peiying Ruan Daguang Xu Dufan Wu Eddie Huang Felipe Campos Kitamura Грифин Лацеи Густаво Цезар де Антонио Корради Gustavo Nino Hao-Hsin Shin Hirofumi Obinata Hui Ren Jason C. Crane Џеси Тетреаулт Jiahui Guan John W. Garrett Joshua D. Kaggie Јунг Гил Парк Keith Dreyer Кришна Јулуру Kristopher Kersten Marcio Aloisio Bezerra Cavalcanti Rockenbach Marius George Linguraru Масоом А. Хајдер Meena AbdelMaseeh Nicola Rieke Pablo F. Damasceno Pedro Mario Cruz e Silva Поцхуан Ванг Sheng Xu Shuichi Kawano Sira Sriswasdi Соо Јанг Парк Томас М. Грист Varun Buch Watsamon Jantarabenjakul Weichung Wang Won Young Tak Xiang Li Xihong Lin Млади Џон Квон Абуд Кураини Андреј Фенг Андреј Н. Прист Baris Turkbey Benjamin Glicksberg Бернардо Бизо Byung Seok Kim Carlos Tor-Díez Chia-Cheng Lee Цхиа-Јунг Хсу Chin Lin Chiu-Ling Lai Christopher P. Hess Колин компас Дипексха Бхатија Eric K. Oermann Evan Leibovitz Hisashi Sasaki Хитоши Мори Isaac Yang Jae Ho Sohn Кришна Нанд Кешава Мурти Li-Chen Fu Matheus Ribeiro Furtado de Mendonça Mike Fralick Min Kyu Kang Mohammad Adil Natalie Gangai Перапон Ватеек Pierre Elnajjar Sarah Hickman Шармила Мајумдар Шели Л. Меклеод Sheridan Reed Stefan Gräf Стефани Хармон Tatsuya Kodama Thanyawee Puthanakit Тони Мазули Витор Лима де Лабор Yothin Rakvongthai Yu Rim Lee Yuhong Wen Фиона Џилберт Mona G. Flores Quanzheng Li Abstract Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site’s data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare. Main Научне, академске, медицинске и научне заједнице су се окупиле пред пандемијском кризом ЦОВИД-19 да брзо процене нове парадигме у вештачкој интелигенцији (АИ) које су брзе и сигурне, и потенцијално подстичу дељење података и обуку и тестирање модела без уобичајених баријера приватности и власништва подацима конвенционалних сарадњи. , Здравствени радници, истраживачи и индустрија окренули су свој фокус да се баве незадовољеним и критичним клиничким потребама створеним кризом, са изванредним резултатима. , , , , , , . Clinical trial recruitment has been expedited and facilitated by national regulatory bodies and an international cooperative spirit , , . The data analytics and AI disciplines have always fostered open and collaborative approaches, embracing concepts such as open-source software, reproducible research, data repositories and making available anonymized datasets publicly , . The pandemic has emphasized the need to expeditiously conduct data collaborations that empower the clinical and scientific communities when responding to rapidly evolving and widespread global challenges. Data sharing has ethical, regulatory and legal complexities that are underscored, and perhaps somewhat complicated, by the recent entrance of large technology companies into the healthcare data world , , . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 A concrete example of these types of collaboration is our previous work on an AI-based SARS-COV-2 clinical decision support (CDS) model. This CDS model was developed at Mass General Brigham (MGB) and was validated across multiple health systems’ data. The inputs to the CDS model were chest X-ray (CXR) images, vital signs, demographic data and laboratory values that were shown in previous publications to be predictive of outcomes of patients with COVID-19 , , , ЦКСР је изабран као улаз за сликање јер је широко доступан и обично означен смерницама као што су оне које пружа АЦР , the Fleischner Society Oko WHO , national thoracic societies Национално министарство здравља ЦОВИД приручници и радиолошка друштва широм света . The output of the CDS model was a score, termed CORISK , that corresponds to oxygen support requirements and that could aid in triaging patients by frontline clinicians , , . Healthcare providers have been known to prefer models that were validated on their own data . To date most AI models, including the aforementioned CDS model, have been trained and validated on ‘narrow’ data that often lack diversity , , potentially resulting in overfitting and lower generalizability. This can be mitigated by training with diverse data from multiple sites without centralization of data using methods such as transfer learning , or FL. FL is a method used to train AI models on disparate data sources, without the data being transported or exposed outside their original location. While applicable to many industries, FL has recently been proposed for cross-institutional healthcare research . 18 19 20 21 22 23 24 25 26 27 28 29 30 27 31 32 33 34 35 36 Federated learning supports the rapid launch of centrally orchestrated experiments with improved traceability of data and assessment of algorithmic changes and impact . One approach to FL, called client-server, sends an ‘untrained’ model to other servers (‘nodes’) that conduct partial training tasks, in turn sending the results back to be merged in the central (‘federated’) server. This is conducted as an iterative process until training is complete . 37 36 Управљање подацима за ФЛ се одржава локално, ублажавајући бриге о приватности, са само теговима модела или градијентима који се комуницирају између клијентских локација и федерисаног сервера , . FL has already shown promise in recent medical imaging applications , , , , including in COVID-19 analysis , , Занимљив пример је модел предвиђања смртности код пацијената заражених САРС-ЦОВ-2 који користи клиничке карактеристике, иако ограничене у смислу броја модалитета и скале. . 38 39 40 41 42 43 8 44 45 46 Our objective was to develop a robust, generalizable model that could assist in triaging patients. We theorized that the CDS model can be federated successfully, given its use of data inputs that are relatively common in clinical practice and that do not rely heavily on operator-dependent assessments of patient condition (such as clinical impressions or reported symptoms). Rather, laboratory results, vital signs, an imaging study and a commonly captured demographic (that is, age), were used. We therefore retrained the CDS model with diverse data using a client-server FL approach to develop a new global FL model, which was named EXAM, using CXR and EMR features as input. By leveraging FL, the participating institutions would not have to transfer data to a central repository, but rather leverage a distributed data framework. Our hypothesis was that EXAM would perform better than local models and would generalize better across healthcare systems. Резултати The EXAM model architecture ЕКСАМ модел се заснива на ЦДС моделу поменутом горе . In total, 20 features (19 from the EMR and one CXR) were used as input to the model. The outcome (that is, ‘ground truth’) labels were assigned based on patient oxygen therapy after 24- and 72-hour periods from initial admission to the emergency department (ED). A detailed list of the requested features and outcomes can be seen in Table . 27 1 The outcome labels of patients were set to 0, 0.25, 0.50 and 0.75 depending on the most intensive oxygen therapy the patient received in the prediction window. The oxygen therapy categories were, respectively, room air (RA), low-flow oxygen (LFO), high-flow oxygen (HFO)/noninvasive ventilation (NIV) or mechanical ventilation (MV). If the patient died within the prediction window, the outcome label was set to 1. This resulted in each case being assigned two labels in the range 0–1, corresponding to each of the prediction windows (that is, 24 and 72 h). For EMR features, only the first values captured in the ED were used and data preprocessing included deidentification, missing value imputation and normalization to zero-mean and unit variance. For CXR images, only the first obtained in the ED was used. The model therefore fuses information from both EMR and CXR features, using a 34-layer convolutional neural network (ResNet34) to extract features from a CXR and a Deep & Cross network to concatenate the features together with the EMR features (for more expanded details, see Излаз модела је резултат ризика, назван ЕКСАМ резултат, који је континуирана вредност у опсегу 0-1 за сваку од 24 и 72 сата предвиђања која одговарају ознакама описаним горе. Methods Federating the model The EXAM model was trained using a cohort of 16,148 cases, making it not only among the first FL models for COVID-19 but also a very large and multicontinent development project in clinically relevant AI (Fig. Подаци између локација нису били хармонизовани пре екстракције и, с обзиром на реалне клиничке информатичке околности, аутори нису спровели детаљну хармонизацију уноса података (слика). ). 1a,b 1c,d , World map indicating the 20 different client sites contributing to the EXAM study. , Number of cases contributed by each institution or site (client 1 represents the site contributing the largest number of cases). , Интензитет рендгенске дистрибуције грудног коша на свакој локацији клијента. , Age of patients at each client site, showing minimum and maximum ages (asterisks), mean age (triangles) and standard deviation (horizontal bars). The number of samples of each client site is shown in Supplementary Table . a b c d 1 We compared locally trained models with the global FL model on each client’s test data. Training the model through FL resulted in a significant performance improvement ( « 1 × 10–3, Wilcoxon signed-rank test) of 16% (as defined by average AUC when running the model on respective local test sets: from 0.795 to 0.920, or 12.5 percentage points) (Fig. ). It also resulted in 38% generalizability improvement (as defined by average AUC when running the model on all test sets: from 0.667 to 0.920, or 25.3 percentage points) of the best global model for prediction of 24-h oxygen treatment compared with models trained only on a site’s own data (Fig. ). For the prediction results of 72-h oxygen treatment, the best global model training resulted in an average performance improvement of 18% compared to locally trained models, while generalizability of the global model improved on average by 34% (Extended Data Fig. Стабилност наших резултата потврђена је понављањем три тренинга локалног и ФЛ тренинга на различитим рандомизованим поделама података. P 2a 2b 1 , Перформансе на тесту сваког клијента постављене су у предвиђању 24-часовног третмана кисеоником за моделе обучене само на локалним подацима (Локално) у односу на најбољи глобални модел који је доступан на серверу (ФЛ (види најбоље). , Generalizability (average performance on other sites’ test data, as represented by average AUC) as a function of a client’s dataset size (no. of cases). The green horizontal line denotes the generalizability performance of the best global model. The performance for 18 of 20 clients is shown, because client 12 had outcomes only for 72-h oxygen (Extended Data Fig. ) и клијент 14 је имао случајеве само са РА третманом, тако да оцењивање метрике (од АУЦ) није било применљиво у било ком од ових случајева ( Подаци за клијента 14 су такође искључени из израчунавања просечне генерализабилности у локалним моделима. a b 1 Methods Локални модели који су обучени користећи неуравнотежене кохорте (на пример, углавном благи случајеви ЦОВИД-19) значајно су имали користи од ФЛ приступа, са значајним побољшањем у прогнозирању просечне АУЦ перформансе за категорије са само неколико случајева. Ово је било очигледно на клијентском сајту 16 (неуравнотежен скуп података), са већином пацијената који доживљавају благу тежину болести и са само неколико тешких случајева. Проширени подаци Фиг. ). More important, the generalizability of the FL model was considerably increased over the locally trained model. 3a 2 , ROC at client site 16, with unbalanced data and mostly mild cases. , РОЦ локалног модела на сајту клијента 12 (мали скуп података), просечан РОЦ модела обучених на већим скуповима података који одговарају пет клијентских локација у подручју Бостона (1, 4, 5, 6, 8) и РОЦ најбољег глобалног модела у предвиђању третмана кисеоником 72 сата за различите прагове испитивања (лево, средина, десно). Просечан РОЦ се израчунава на основу пет локално обучених модела, док сива област означава стандардно одступање РОЦ. ) of the EXAM risk score are shown. Pos and neg denote the number of positive and negative cases, respectively, as defined by this range of EXAM score. a b t In the case of client sites with relatively small datasets, the best FL model markedly outperformed not only the local model but also those trained on larger datasets from five client sites in the Boston area of the USA (Fig. ) 3b The global model performed well in predicting oxygen needs at 24/72 h in patients both COVID positive and negative (Extended Data Fig. ). 3 Validation at independent sites Following initial training, EXAM was subsequently tested at three independent validation sites: Cooley Dickinson Hospital (CDH), Martha’s Vineyard Hospital (MVH) and Nantucket Cottage Hospital (NCH), all in Massachusetts, USA. The model was not retrained at these sites and it was used only for validation purposes. The cohort size and model inference results are summarized in Table , and the ROC curves and confusion matrices for the largest dataset (from CDH) are shown in Fig. Оперативна тачка је постављена на разлику између не-механичке вентилације и механичке вентилације (МВ) третмана (или смрти). ФЛ глобални обучени модел, ЕКСАМ, постигао је просечну АУЦ од 0,944 и 0,924 за задатке предвиђања од 24 и 72 сата, респективно (Табела). ), which exceeded the average performance among sites used in training EXAM. For prediction of MV treatment (or death) at 24 h, EXAM achieved a sensitivity of 0.950 and specificity of 0.882 at CDH, and a sensitivity of 1.000 specificity of 0.934 at MVH. NCH did not have any cases with MV/death at 24 h. In regard to 72-h MV prediction, EXAM achieved a sensitivity of 0.929 and specificity of 0.880 at CDH, sensitivity of 1.000 and specificity of 0.976 at MVH and sensitivity of 1.000 and specificity of 0.929 at NCH. 2 4 2 , , Performance (ROC) (top) and confusion matrices (bottom) of the EXAM FL model on the CDH dataset for prediction of oxygen requirement at 24 h ( ) and 72 h ( ). ROCs for three different cutoff values ( ) of the EXAM risk score are shown. a b a b t For MV at CDH at 72 h, EXAM had a low false-negative rate of 7.1%. Representative failure cases are presented in Extended Data Fig. , showing two false-negative cases from CDH where one case had many missing EMR data features and the other had a CXR with a motion artifact and some missing EMR features. 4 Use of differential privacy A primary motivation for healthcare institutes to use FL is to preserve the security and privacy of their data, as well as adherence to data compliance measures. For FL, there remains the potential risk of model ‘inversion’ or even the reconstruction of training images from the model gradients themselves . To counter these risks, security-enhancing measures were used to mitigate risk in the event of data ‘interception’ during site-server communication . We experimented with techniques to avoid interception of FL data, and added a security feature that we believe could encourage more institutions to use FL. We thus validated previous findings showing that partial weight sharing, and other differential privacy techniques, can successfully be applied in FL Преко истраживања шеме делимичне дељења тежине , , , we showed that models can reach a comparable performance even when only 25% of weight updates are shared (Extended Data Fig. ) 47 48 49 50 50 51 52 5 Discussion This study features a large, real-world healthcare FL study in terms of number of sites and number of data points used. We believe that it provides a powerful proof-of-concept of the feasibility of using FL for fast and collaborative development of needed AI models in healthcare. Our study involved multiple sites across four continents and under the oversight of different regulatory bodies, and thus holds the promise of being provided to different regulated markets in an expedited way. The global FL model, EXAM, proved to be more robust and achieved better results at individual sites than any model trained on only local data. We believe that consistent improvement was achieved owing to a larger, but also a more diverse, dataset, the use of data inputs that can be standardized and avoidance of clinical impressions/reported symptoms. These factors played an important part in increasing the benefits from this FL approach and its impact on performance, generalizability and, ultimately, the model’s usability. For a client site with a relatively small dataset, two typical approaches could be used for fitting a useful model: one is to train locally with its own data, the other is to apply a model trained on a larger dataset. For sites with small datasets, it would have been virtually impossible to build a performant deep learning model using only their local data. The finding, that these two approaches were outperformed on all three prediction tasks by the global FL model, indicates that the benefit for client sites with small datasets arising from participation in FL collaborations is substantial. This is probaby a reflection of FL’s ability to capture more diversity than local training, and to mitigate the bias present in models trained on a homogenous population. An under-represented population or age group in one hospital/region might be highly represented in another region—such as children who might be differentially affected by COVID-19, including disease manifestations in lung imaging . 46 Резултати валидације потврдили су да је глобални модел робустан, подржавајући нашу хипотезу да су модели обучени ФЛ генерализовани у свим системима здравствене заштите. Они пружају убедљив случај за употребу предиктивних алгоритма у бризи о пацијентима ЦОВИД-19 и употребу ФЛ у креирању модела и тестирању. Учешћем у овој студији клијентске локације добиле су приступ ЕКСАМ-у, да би биле даље валидисане пре тражења било каквог регулаторног одобрења или будућег увођења у клиничку негу. , as well as at different sites that were not a part of the EXAM training. 53 Over 200 prediction models to support decision-making in patients with COVID-19 have been published . Unlike the majority of publications focused on diagnosis of COVID-19 or prediction of mortality, we predicted oxygen requirements that have implications for patient management. We also used cases with unknown SARS-COV-2 status, and so the model could provide input to the physician ahead of receiving a result for PCR with reverse transcription (RT–PCR), making it useful for a real-life clinical setting. The model’s imaging input is used in common practice, in contrast with models that use chest computed tomography, a nonconsensual diagnostic modality. The model’s design was constrained to objective predictors, unlike many published studies that leveraged subjective clinical impressions. The data collected reflect varied incidence rates, and thus the ‘population momentum’ we encountered is more diverse. This implies that the algorithm can be useful in populations with different incidence rates. 19 Идентификација кохорте пацијената и хармонизација података нису нова питања у истраживању и науци о подацима , but are further complicated, when using FL, given the lack of visibility on other sites’ datasets. Improvements to clinical information systems are needed to streamline data preparation, leading to better leverage of a network of sites participating in FL. This, in conjunction with hyperparameter engineering, can allow algorithms to ‘learn’ more effectively from larger data batches and adapt model parameters to a particular site for further personalization—for example, through further fine-tuning on that site Систем који би омогућио беспрекорно, близу реалном времену моделовање и обраду резултата такође би био користан и "затворио петљу" од обуке до распореда модела. 54 39 Because data were not centralized they are not readily accessible. Given that, any future analysis of the results, beyond what was derived and collected, is limited. Similar to other machine learning models, EXAM is limited by the quality of the training data. Institutions interested in deploying this algorithm for clinical care need to understand potential biases in the training. For example, the labels used as ground truth in the training of the EXAM model were derived from 24- and 72-h oxygen consumption in the patient; it is assumed that oxygen delivered to the patient equates the oxygen need. However, in the early phase of the COVID-19 pandemic, many patients were provided high-flow oxygen prophylactically regardless of their oxygen need. Such clinical practice could skew the predictions made by this model. Пошто је наш приступ подацима био ограничен, нисмо имали довољно доступних информација за генерисање детаљних статистика о узроцима неуспјеха, постхоц, на већини локација. Међутим, проучили смо случајеве неуспјеха са највећег независног теста сајта, ЦДХ, и могли смо да генеришемо хипотезе које можемо тестирати у будућности. За локације са високим перформансама, чини се да већина случајева неуспјеха спада у једну од две категорије: (1) нискоквалитетни улазни подаци - на пример, недостају подаци или покретни артефакт у ЦКСР-у; или (2) подаци изван дистрибуције - на пример, врло млади пацијент. In future, we also intend to investigate the potential for a ‘population drift’ due to different phases of disease progression. We believe that, owing to the diversity across the 20 sites, this risk may have been mitigated. A feature that would enhance these kinds of large-scale collaboration is the ability to predict the contribution of each client site towards improving the global FL model. This will help in client site selection, and in prioritization of data acquisition and annotation efforts. The latter is especially important given the high costs and difficult logistics of these large-consortia endeavors, and it will enable these endeavors to capture diversity rather than the sheer quantity of data samples. Future approaches may incorporate automated hyperparameter searching Neural Architecture Претраживање and other automated machine learning approaches to find the optimal training parameters for each client site more efficiently. 55 56 57 Known issues of batch normalization (BN) in FL motivated us to fix our base model for image feature extraction to reduce the divergence between unbalanced client sites. Future work might explore different types of normalization techniques to allow the training of AI models in FL more effectively when client data are nonindependent and identically distributed. 58 49 Recent works on privacy attacks within the FL setting have raised concerns on data leakage during model training . Meanwhile, protection algorithms remain underexplored and constrained by multiple factors. While differential privacy algorithms , , show good protection, they may weaken the model’s performance. Encryption algorithms, such as homomorphic encryption , maintain performance but may substantially increase message size and training time. A quantifiable way to measure privacy would allow better choices for deciding the minimal privacy parameters necessary while maintaining clinically acceptable performance , , . 59 36 48 49 60 36 48 49 Following further validation, we envision deployment of the EXAM model in the ED setting as a way to evaluate risk at both the per-patient and population level, and to provide clinicians with an additional reference point when making the frequently difficult task of triaging patients. We also envision using the model as a more sensitive population-level metric to help balance resources between regions, hospitals and departments. Our hope is that similar FL efforts can break the data silos and allow for faster development of much-needed AI models in the near future. Methods Ethics approval All procedures were conducted in accordance with the principles for human experimentation as defined in the Declaration of Helsinki and International Conference on Harmonization Good Clinical Practice guidelines, and were approved by the relevant institutional review boards at the following validation sites: CDH, MVH, NCH and at the following training sites: MGB, Mass General Hospital (MGH), Brigham and Women’s Hospital, Newton-Wellesley Hospital, North Shore Medical Center and Faulkner Hospital (all eight of these hospitals were covered under MGB’s ethics board reference, no. 2020P002673, and informed consent was waived by the instititional review board (IRB). Similarly, participation of the remaining sites was approved by their respective relevant institutional review processes: Children’s National Hospital in Washington, DC (no. 00014310, IRB certified exempt); NIHR Cambridge Biomedical Research Centre (no. 20/SW/0140, informed consent waived); The Self-Defense Forces Central Hospital in Tokyo (no. 02-014, informed consent waived); National Taiwan University MeDA Lab and MAHC and Taiwan National Health Insurance Administration (no. 202108026 W, informed consent waived); Tri-Service General Hospital in Taiwan (no. B202105136, informed consent waived); Kyungpook National University Hospital in South Korea (no. KNUH 2020-05-022, informed consent waived); Faculty of Medicine, Chulalongkorn University in Thailand (nos. 490/63, 291/63, informed consent waived); Diagnosticos da America SA in Brazil (no. 26118819.3.0000.5505, informed consent waived); University of California, San Francisco (no. 20-30447, informed consent waived); VA San Diego (no. H200086, IRB certified exempt); University of Toronto (no. 20-0162-C, informed consent waived); National Institutes of Health in Bethesda, Maryland (no. 12-CC-0075, informed consent waived); University of Wisconsin-Madison School of Medicine and Public Health (no. 2016-0418, informed consent waived); Memorial Sloan Kettering Cancer Center in New York (no. 20-194, informed consent waived); and Mount Sinai Health System in New York (no. IRB-20-03271, informed consent waived). MI-CLAIM guidelines for reporting of clinical AI models were followed (Supplementary Note ) 2 Проучавање постављања The study included data from 20 institutions (Fig. ): MGB, MGH, Brigham and Women’s Hospital, Newton-Wellesley Hospital, North Shore Medical Center and Faulkner Hospital; Children’s National Hospital in Washington, DC; NIHR Cambridge Biomedical Research Centre; The Self-Defense Forces Central Hospital in Tokyo; National Taiwan University MeDA Lab and MAHC and Taiwan National Health Insurance Administration; Tri-Service General Hospital in Taiwan; Kyungpook National University Hospital in South Korea; Faculty of Medicine, Chulalongkorn University in Thailand; Diagnosticos da America SA in Brazil; University of California, San Francisco; VA San Diego; University of Toronto; National Institutes of Health in Bethesda, Maryland; University of Wisconsin-Madison School of Medicine and Public Health; Memorial Sloan Kettering Cancer Center in New York; and Mount Sinai Health System in New York. Institutions were recruited between March and May 2020. Dataset curation started in June 2020 and the final data cohort was added in September 2020. Between August and October 2020, 140 independent FL runs were conducted to develop the EXAM model and, by the end of October 2020, EXAM was made public on NVIDIA NGC , , . Data from three independent sites were used for independent validation: CDH, MVH and NCH, all in Massachusetts, USA. These three hospitals had patient population characteristics different from the training sites. The data used for the algorithm validation consisted of patients admitted to the ED at these sites between March 2020 and February 2021, and that satisfied the same inclusion criteria of the data used to train the FL model. 1а 61 62 63 Data collection 20 клијентских локација припремило је укупно 16.148 случајева (и позитивних и негативних) у сврху обуке, валидације и тестирања модела (Слика. ). Medical data were accessed in relation to patients who satisfied the study inclusion criteria. Client sites strived to include all COVID-positive cases from the beginning of the pandemic in December 2019 and up to the time they started local training for the EXAM study. All local training had started by 30 September 2020. The sites also included other patients in the same period with negative RT–PCR test results. Since most of the sites had more SARS-COV-2-negative than -positive patients, we limited the number of negative patients included to, at most, 95% of the total cases at each client site. 1b Један "случај" укључује ЦКСР и потребне уносе података из медицинске евиденције пацијента. . The distribution and patterns of CXR image intensity (pixel values) varied greatly among sites owing to a multitude of patient- and site-specific factors, such as different device manufacturers and imaging protocols, as shown in Fig. . Patient age and EMR feature distribution varied greatly among sites, as expected owing to the differing demographics between globally distributed hospitals (Extended Data Fig. ). 1б 1c,d 6 Patient inclusion criteria Критеријуми за укључивање пацијената били су: (1) пацијент је представљен болничком ЕД-у или еквивалентном; (2) пацијент је имао РТ-ПЦР тест који је изведен у било ком тренутку између презентације ЕД-у и испуштања из болнице; (3) пацијент је имао ЦКСР у ЕД-у; и (4) пацијентова евиденција је имала најмање пет вредности ЕМР-а детаљно наведених у Табели. , all obtained in the ED, and the relevant outcomes captured during hospitalization. Of note, The CXR, laboratory results and vitals used were the first available for capture during the visit to the ED. The model did not incorporate any CXR, laboratory results or vitals acquired after leaving the ED. 1 Model input In total, 21 EMR features were used as input to the model. The outcome (that is, ground truth) labels were assigned based on patient requirements after 24- and 72-h periods from initial admission to the ED. A detailed list of the requested EMR features and outcomes can be seen in Table . 1 Дистрибуција третмана кисеоником помоћу различитих уређаја на различитим клијентским локацијама приказана је у Екстендед Дата Фиг. , which details the device usage at admission to the ED and after 24- and 72-h periods. The difference in dataset distribution between the largest and smallest client sites can be seen in Extended Data Fig. . 7 8 The number of positive COVID-19 cases, as confirmed by a single RT–PCR test obtained at any time between presentation to the ED and discharge from the hospital, is listed in Supplementary Table . Each client site was asked to randomly split its dataset into three parts: 70% for training, 10% for validation and 20% for testing. For both 24- and 72-h outcome prediction models, random splits for each of the three repeated local and FL training and evaluation experiments were independently generated. 1 EXAM model development There is wide variation in the clinical course of patients who present to hospital with symptoms of COVID-19, with some experiencing rapid deterioration in respiratory function requiring different interventions to prevent or mitigate hypoxemia , . A critical decision made during the evaluation of a patient at the initial point of care, or in the ED, is whether the patient is likely to require more invasive or resource-limited countermeasures or interventions (such as MV or monoclonal antibodies), and should therefore receive a scarce but effective therapy, a therapy with a narrow risk–benefit ratio due to side effects or a higher level of care, such as admittance to the intensive care unit . In contrast, a patient who is at lower risk of requiring invasive oxygen therapy may be placed in a less intensive care setting such as a regular ward, or even released from the ED for continuing self-monitoring at home . EXAM was developed to help triage such patients. 62 63 64 65 Of note, the model is not approved by any regulatory agency at this time and it should be used only for research purposes. EXAM score EXAM was trained using FL; it outputs a risk score (termed EXAM score) similar to CORISK (Extended Data Fig. ) and can be used in the same way to triage patients. It corresponds to a patient’s oxygen support requirements within two windows—24 and 72 h—after initial presentation to the ED. Extended Data Fig. illustrates how CORISK and the EXAM score can be used for patient triage. 27 9а 9b Рентгенске слике грудног коша су претходно обрађене да би изабрале слику предње позиције и искључиле бочне слике, а затим скалиране на резолуцију од 224 × 224. , модел спаја информације из функција ЕМР и ЦКСР (на основу модификованог РесНет34 са просторном пажњом pretrained on the CheXpert dataset) Deep & Cross mreža . To converge these different data types, a 512-dimensional feature vector was extracted from each CXR image using a pretrained ResNet34, with spatial attention, then concatenated with the EMR features as the input for the Deep & Cross network. The final output was a continuous value in the range 0–1 for both 24- and 72-h predictions, corresponding to the labels described above, as shown in Extended Data Fig. . We used cross-entropy as the loss function and ‘Adam’ as the optimizer. The model was implemented in Tensorflow Преузети NVIDIA Clara Train SDK . The average AUC for the classification tasks (≥LFO, ≥HFO/NIV or ≥MV) was calculated and used as the final evaluation metric, with normalization to zero-mean and unit variance. CXR images were preprocessed to select the correct series and exclude lateral view images, then scaled to a resolution of 224 × 224 (ref. ) 9a 66 67 68 9b 69 70 27 Feature imputation and normalization A MissForest algorithm је коришћен за импутирање ЕМР карактеристика, на основу локалног скупа података о обуци. Ако је ЕМР карактеристика потпуно недостајала из скупа података о клијентском сајту, користила се просечна вредност те карактеристике, израчуната искључиво на подацима са МГБ клијентских локација. 71 Details of EMR–CXR data fusion using the Deep & Cross network За моделирање интеракција функција из ЕМР и ЦКСР података на нивоу случаја, коришћена је шема дубоких функција заснована на мрежној архитектури Дееп & Цросс . Binary and categorical features for the EMR inputs, as well as 512-dimensional image features in the CXR, were transformed into fused dense vectors of real values by embedding and stacking layers. The transformed dense vectors served as input to the fusion framework, which specifically employed a crossing network to enforce fusion among input from different sources. The crossing network performed explicit feature crossing within its layers, by conducting inner products between the original input feature and output from the previous layer, thus increasing the degree of interaction across features. At the same time, two individual classic deep neural networks with several stacked, fully connected feed-forward layers were trained. The final output of our framework was then derived from the concatenation of both classic and crossing networks. 68 FL details Arguably the most established form of FL is implemention of the federated averaging algorithm as proposed by McMahan et al. , or variations thereof. This algorithm can be realized using a client-server setup where each participating site acts as a client. One can think of FL as a method aiming to minimize a global loss function by reducing a set of local loss functions, which are estimated at each site. By minimizing each client site’s local loss while also synchronizing the learned client site weights on a centralized aggregation server, one can minimize global loss without needing to access the entire dataset in a centralized location. Each client site learns locally, and shares model weight updates with a central server that aggregates contributions using secure sockets layer encryption and communication protocols. The server then sends an updated set of weights to each client site after aggregation, and sites resume training locally. The server and client site iterate back and forth until the model converges (Extended Data Fig. ) 72 9c Псеудоалгоритам ФЛ је приказан у Додатној напомени . In our experiments, we set the number of federated rounds at = 200, with one local training epoch per round at each client. The number of clients, , је био до 20 у зависности од мрежне повезаности клијената или доступних података за одређени циљани период исхода (24 или 72 сата). , depends on the dataset size at each client and is used to weigh each client’s contributions when aggregating the model weights in federated averaging. During the FL training task, each client site selects its best local model by tracking the model’s performance on its local validation set. At the same time, the server determines the best global model based on the average validation scores sent from each client site to the server after each FL round. After FL training finishes, the best local models and the best global model are automatically shared with all client sites and evaluated on their local test data. 1 T t K nk k When training on local data only (the baseline), we set the epoch number to 200. The Adam optimizer was used for both local training and FL with an initial learning rate of 5 × 10–5 and a stepwise learning rate decay with a factor 0.5 after every 40 epochs, which is important for the convergence of federated averaging . Random affine transformations, including rotation, translations, shear, scaling and random intensity noise and shifts, were applied to the images for data augmentation during training. 73 Owing to the sensitivity of BN layers when dealing with different clients in a nonindependent and identically distributed setting, we found the best model performance occurred when keeping the pretrained ResNet34 with spatial attention parameters fixed during FL training (that is, using a learning rate of zero for those layers). The Deep & Cross network that combines image features with EMR features does not contain BN layers and hence was not affected by BN instability issues. 58 47 У овој студији смо истражили шему за очување приватности која дели само делимична ажурирања модела између сервера и клијентских локација. Ажурирања тежине су рангирана током сваке итерације по величини доприноса, а само одређени проценат највећих ажурирања тежине је дељен са сервером. (t) (Extended Data Fig. ), which was computed from all non-zero gradients, Δ , and could be different for each client in each FL round Варијације ове шеме могу укључивати додатно исецање великих градијента или диференцијалних шема приватности. који додају случајну буку на градијенте, или чак на сирове податке, пре него што уђу у мрежу . k 5 Wk(t) k t 49 51 Statistical analysis We conducted a Wilcoxon signed-rank test to confirm the significance of the observed improvement in performance between the locally trained model and the FL model for the 24- and 72-h time points (Fig. and Extended Data Fig. ). The null hypothesis was rejected with one-sided « 1 × 10–3 in both cases. 2 1 P Pearson’s correlation was used to assess the generalizability (robustness of the average AUC value to other client sites’ test data) of locally trained models in relation to respective local dataset size. Only a moderate correlation was observed ( је 0,43 = 0.035, degrees of freedom (df) = 17 for the 24-h model and = 0.62, = 0.003, df = 16 for the 72-h model). This indicates that dataset size alone is not the only factor determining a model’s robustness to unseen data. r P r P To compare ROC curves from the global FL model and local models trained at different sites (Extended Data Fig. ), we bootstrapped 1,000 samples from the data and computed the resulting AUCs. We then calculated the difference between the two series and standardized using the formula = (AUC1 – AUC2)/ , where is the standardized difference, is the standard deviation of the bootstrap differences and AUC1 and AUC2 are the corresponding bootstrapped AUC series. By comparing Уз нормалну дистрибуцију, добили смо values illustrated in Supplementary Table . The results show that the null hypothesis was rejected with very low values, indicating the statistical significance of the superiority of FL outcomes. The computation of values was conducted in R with the pROC library . 3 D s D s D P 2 P P 74 Пошто модел предвиђа дискретан исход, континуирани резултат од 0 до 1, једноставна евалуација калибрације као што је qqplot није могућа. ). We conducted one-way analysis of variation (ANOVA) tests to compare local and FL model scores among four ground truth categories (RA, LFO, HFO, MV). The -statistic, calculated as the variation between the sample means divided by variation within the samples and representing the degree of dispersion among different groups, was used to quantify the models. Our results show that the -вредности пет различитих локалних локација су 245.7, 253.4, 342.3, 389.8 и 634.8, док је вредност ФЛ модела 843.5. -values mean that groups are more separable, the scores from our FL model clearly show a greater dispersion among the four ground truth categories. Furthermore, the вредност АНОВА теста на ФЛ моделу је <2 × 10–16, што указује на то да су ФЛ резултат предвиђања статистички значајно различити између различитих класа предвиђања. 10 F F F P Izveštaj sažetak Further information on research design is available in the Povezano sa ovim člankom. Природни истраживачки извештај Резиме Data availability The dataset from the 20 institutes that participated in this study remains under their custody. These data were used for training at each of the local sites and were not shared with any of the other participating institutions or with the federated server, and they are not publicly available. Data from the independent validation sites are maintained by CAMCA, and access can be requested by contacting Q.L. Based on determination by CAMCA, a data-sharing review and amendment of IRB for research purposes can be conducted by MGB research administration and in accordance with MGB IRB and policy. Code availability All code and software used in this study are publicly available at NGC. To access, log in as a guest or create a profile then enter one of the URLs below. The trained models, data preparation guidelines, code for training, validating testing of the model, readme file, installation guideline and license files are publicly available at NVIDIA NGC • : The federated learning software is available as part of the Clara Train SDK: Алтернативно, користите ову команду да бисте преузели модел "wget --content-disposition -O clara_train_covid19_exam_ehr_xray_1.zip”. 61 https://ngc.nvidia.com/catalog/models/nvidia:med:clara_train_covid19_exam_ehr_xray https://ngc.nvidia.com/catalog/containers/nvidia:clara-train-sdk https://api.ngc.nvidia.com/v2/models/nvidia/med/clara_train_covid19_exam_ehr_xray/versions/1/zip References Budd, J. et al. Digital technologies in the public-health response to COVID-19. , 1183–1192 (2020). Nat. Med. 26 Moorthy, V., Henao Restrepo, A. 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BMC Bioinformatics 12 Acknowledgements Позиције изражене у овом истраживању клиничким подацима су оне аутора, а не нужно оне НХС-а, НИХР-а, Одељења за здравље и социјалну негу или било које организације повезане са ауторима. МГБ захваљује следећим појединцима за њихову подршку: Ј. Бринк, Одељење за радиологију, Масачусетс Општа болница, Харвард Медицал Сцхоол, Бостон, МА; М. Калра, Одељење за радиологију, Масачусетс Општа болница, Харвард Медицал Сцхоол, Бостон, МА; Н. Неумарк, Центар за клиничке науке о подацима, Масачусетс Генерал Бригам, Бостон, МА; Т. Сц . The Faculty of Medicine, Chulalongkorn University thank the Ratchadapisek Sompoch Endowment Fund RA (PO) (no. 001/63) for the collection and management of COVID‐19-related clinical data and biological specimens for the Research Task Force, Faculty of Medicine, Chulalongkorn University. NIHR Cambridge Biomedical Research Centre thank A. Priest, who is supported by the NIHR (Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust). National Taiwan University MeDA Lab and the MAHC and Taiwan National Health Insurance Administration thank the MOST Joint Research Center for AI technology, the All Vista Healthcare National Health Insurance Administration, Taiwan, the Ministry of Science and Technology, Taiwan and the National Center for Theoretical Sciences Mathematics Division. National Institutes of Health (NIH) acknowledge that the NIH Medical Research Scholars Program is a public–private partnership supported jointly by the NIH and by generous contributions to the Foundation for the NIH from the Doris Duke Charitable Foundation, the American Association for Dental Research, the Colgate-Palmolive Company, Genentech, alumni of student research programs and other individual supporters via contributions to the Foundation for the NIH. https://data.ucsf.edu/covid19 Овај чланак је доступан у природи под лиценцом CC by 4.0 Deed (Attribution 4.0 International). Ovaj papir je под лиценцом CC by 4.0 Deed (Attribution 4.0 International). available on nature
