Authors: Shkëndija 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 Vllaznia Wang Won Young Tak Xiang Li Xihong Lin Young Joon Kwon Abood Quraini Andrew Feng Andrew N. Priest Baris Turkbey Benjamin Glicksberg në 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 në Prishtinë 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: Shkëndija Dayan Holger R. Roth në Prishtinë Aoxiao Zhong Ahmed Harunë Amilcare mirësjellje Anas Z. Abidin Andrew Liu Anthony Beardsworth Costa Bradford J. Wood në Chien-Sung Tsai Chih-Hung Wang në Shqipëri Chun-Nan Hsu C. K. Le të Peshkimi i Ruanit Daguang Xhu Dufan Wu Eddie Huang Felipe Campos Kitamura në Tiranë Griffin Lacey Gustav Cezari nga Antonio Corradi Gustavo Nino Hao-Hsin Shin Hirofumi Obinata në Prishtinë Hui Ren Jason C. Crane Jesse Tetreault Jiahui Guan John W. Garrett Joshua D. Kaggie më shumë Jung Gil Park Keith Dreyer në Krishna Xhuliu Krishtlindje Marcio Aloisio Bezerra Cavalcanti Rockenbach Marius George Linguraru Masoom A. Haider Meena AbdelMaseeh Nicola Rieke Pablo F. Damasceno Pedro Mario Cruz e Silva Qytetarë Wang Sheng Xu Shuichi Kawano në Prishtinë Sira Sriswasdi Parku i ri Soo Thomas M. Grist Varun Buch Ushqim me qumësht të ngrohtë Vllaznia Wang Won Young Tak Xiang Li Xihong Lin në Young Joon Kwon Abood Quraini Andrew Feng Andrew N. Priest në Prishtinë Baris Turkbey Benjamin Glicksberg në Bernardo Bizzo Byung Seok Kim Carlos Tor-Díez Chia-Cheng Lee më shumë Chia-Jung Hsu Kinë Lin Shkëndija Chiu-Ling Christopher P. Hess Kolin Kompas Deepeksha Bhatia Eric K. Oermann në Prishtinë Evan Leibovitz Hisashi Sasaki Hitoshi Mori Isaac Jani Jae Ho Djali Krishna Nand Keshava Murthy Shkëndija Fu Matheus Ribeiro Furtado de Mendonça në Prishtinë Mike Fralick Min Kyu Kang Muhamedi i drejtë Natali Gangaj Pjesë Vateekul Pierre Elnajjar Sarah Hickman Sharmila Majumdar Shelley L. McLeod Sheridan Reed Stefan Gräf Stephanie Harmon Tatsuya Kodamë Thanyawee Puthanakit Tony Mazzuli Vitor Lima në punë Yothin Rakvongthai Yu Rim Lee Yuhong Wen Fiona J. Gilbert Mona G. Lule Shkëndija Li abstraktë 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 The scientific, academic, medical and data science communities have come together in the face of the COVID-19 pandemic crisis to rapidly assess novel paradigms in artificial intelligence (AI) that are rapid and secure, and potentially incentivize data sharing and model training and testing without the usual privacy and data ownership hurdles of conventional collaborations , Ofruesit e kujdesit shëndetësor, hulumtuesit dhe industria kanë kthyer fokusin e tyre për të adresuar nevojat klinike të papërmbushura dhe kritike të krijuara nga kriza, me rezultate të jashtëzakonshme. , , , , , , Rekrutimi i studimeve klinike është përshpejtuar dhe lehtësuar nga organet rregullatore kombëtare dhe një frymë bashkëpunimi ndërkombëtar. , , Disiplinat e analizës së të dhënave dhe të AI-së gjithmonë kanë nxitur qasje të hapura dhe bashkëpunuese, duke përqafuar koncepte të tilla si softueri me burim të hapur, hulumtimi i riprodhueshëm, depozitat e të dhënave dhe duke e bërë të disponueshme publikisht grupet e të dhënave anonime. , Pandemia ka theksuar nevojën për të kryer me shpejtësi bashkëpunime të të dhënave që fuqizojnë komunitetet klinike dhe shkencore kur përgjigjen ndaj sfidave të shpejta dhe të përhapura globale.Ndërtimi i të dhënave ka kompleksitetet etike, rregullatore dhe ligjore që janë theksuar, dhe ndoshta disi të komplikuara, nga hyrja e kohëve të fundit e kompanive të mëdha teknologjike në botën e të dhënave shëndetësore. , , . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Një shembull konkret i këtyre llojeve të bashkëpunimit është puna jonë e mëparshme në një model të mbështetjes klinike të vendimit (CDS) SARS-COV-2 të bazuar në IA. Ky model CDS u zhvillua në Mass General Brigham (MGB) dhe u validua në të dhënat e shumë sistemeve shëndetësore. , , , CXR u zgjodh si hyrja e imazhit sepse është gjerësisht e disponueshme dhe zakonisht tregohet nga udhëzime të tilla si ato të ofruara nga ACR , the Fleischner Society , the WHO , national thoracic societies , Ministria Kombëtare e Shëndetësisë udhëzime COVID dhe Shoqëritë Radiologjike në mbarë botën . 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 , , Ofruesit e kujdesit shëndetësor kanë qenë të njohur për të preferuar modelet që janë validuar në të dhënat e tyre. . 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 duke përdorur metoda të tilla si transferimi i të mësuarit , 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 Qeverisja e të dhënave për FL mbahet në vend, duke lehtësuar shqetësimet e privatësisë, me vetëm peshat e modelit ose gradientët e komunikuar midis vendeve të klientit dhe serverit të federuar , . FL has already shown promise in recent medical imaging applications , , , , including in COVID-19 analysis , , Një shembull i shquar është një model parashikimi i vdekshmërisë në pacientët e infektuar me SARS-COV-2 që përdor karakteristikat klinike, megjithëse të kufizuara në aspektin e numrit të modaliteteve dhe shkallës. . 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. Hipoteza jonë ishte se EXAM do të kryente më mirë se modelet lokale dhe do të përgjithësonte më mirë në të gjitha sistemet e kujdesit shëndetësor. Rezultatet Arkitektura e modelit të provimit The EXAM model is based on the CDS model mentioned above . 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). Për karakteristikat EMR, u përdorën vetëm vlerat e para të kapura në ED dhe para-procesimi i të dhënave përfshinte deidentifikimin, imputimin e vlerave të munguara dhe normalizimin në mesatare zero dhe variancën e njësive. Modeli kështu bashkon informacionin nga të dy EMR dhe CXR karakteristika, duke përdorur një rrjet nervor konvolucional 34-shtresë (ResNet34) për të nxjerrë karakteristika nga një CXR dhe një rrjet të thellë dhe kryq për të bashkuar karakteristikat së bashku me karakteristikat EMR (për më shumë detaje të zgjeruara, shih Rezultatet e modelit janë një vlerësim i rrezikut, i quajtur vlerësimi EXAM, i cili është një vlerë e vazhdueshme në gamën 0-1 për secilën nga parashikimet e 24 dhe 72 orëve që korrespondojnë me etiketat e përshkruara më sipër. Methods Federalizimi i modelit Modeli EXAM u trajnua duke përdorur një kohortë prej 16.148 rasteve, duke e bërë atë jo vetëm në mesin e modeleve të para FL për COVID-19 por edhe një projekt shumë i madh dhe multi-kontinentale zhvillimi në IA klinike relevante (Fig. ). Data between sites were not harmonized before extraction and, in light of real-life clinical informatics circumstances, a meticulous harmonization of the data input was not conducted by the authors (Fig. ). 1a, b të 1c,d , World map indicating the 20 different client sites contributing to the EXAM study. , Numri i rasteve të kontribuara nga çdo institucion ose vend (klienti 1 përfaqëson faqen që kontribuon me numrin më të madh të rasteve). Shpërndarja e intensitetit të rrezeve X të gjoksit në çdo vend të klientit. , 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. Për rezultatet e parashikimit të trajtimit të oksigjenit 72 orë, trajnimi më i mirë global i modelit rezultoi në një përmirësim mesatar të performancës prej 18% krahasuar me modelet e trajnuar në vend, ndërsa përgjithësueshmëria e modelit global u përmirësua mesatarisht me 34% (Figura e të dhënave të zgjeruara. ). The stability of our results was validated by repeating three runs of local and FL training on different randomized data splits. P 2a 2b 1 , Performance on each client’s test set in prediction of 24-h oxygen treatment for models trained on local data only (Local) versus that of the best global model available on the server (FL (gl. best)). Av., average test performance across all sites. , 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. ) dhe klienti 14 kishte raste vetëm me trajtim RA, kështu që metrika e vlerësimit (nga AUC) nuk ishte e zbatueshme në asnjë nga këto raste ( Të dhënat për klientin 14 u përjashtuan gjithashtu nga llogaritja e përgjithësimit mesatar në modelet lokale. a b 1 Methods Modelet lokale që u trajnuan duke përdorur kohortat e pabalancuara (p.sh., rastet më të lehta të COVID-19) përfituan dukshëm nga qasja FL, me një përmirësim të konsiderueshëm në performancën e parashikimit të AUC mesatare për kategoritë me vetëm disa raste.Kjo ishte e dukshme në faqen e klientit 16 (një dataset i pabalancuar), me shumicën e pacientëve që përjetojnë rëndësi të butë të sëmundjes dhe me vetëm disa raste të rënda.Modeli FL arriti një normë më të lartë të vërtetë-pozitive për dy rastet pozitive (të rënda) dhe një normë të dukshëm më të ulët të rreme-pozitive në krahasim me modelin lokal, të dyja të treguara në komplotet e karakter and Extended Data Fig. Më e rëndësishmja, përgjithësueshmëria e modelit FL u rrit ndjeshëm mbi modelin e trajnuar në vend. 3a 2 , ROC at client site 16, with unbalanced data and mostly mild cases. , ROC e modelit lokal në faqen e klientit 12 (një grup i vogël i të dhënave), ROC mesatare e modeleve të trajnuar në grupet më të mëdha të të dhënave që korrespondojnë me pesë faqet e klientit në zonën e Bostonit (1, 4, 5, 6, 8) dhe ROC e modelit më të mirë global në parashikimin e trajtimit të oksigjenit 72 orë për kufi të ndryshëm të vlerës së EXAM (e majtë, e mesme, e djathtë). Pos dhe neg nënkuptojnë numrin e rasteve pozitive dhe negative, respektivisht, siç përcaktohet nga kjo gamë e pikëve të EXAM. 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. Pika e operimit u vendos për të dalluar midis ventilimit jo-mekanik dhe ventilimit mekanik (MV) trajtim (ose vdekje). modeli i trajnuar global FL, EXAM, arriti një AUC mesatare prej 0.944 dhe 0.924 për detyrat e parashikimit 24 dhe 72 orë, respektivisht (Tabela). ), 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) dhe matricat e konfuzionit (bottom) të modelit EXAM FL në datasetin CDH për parashikimin e kërkesës së oksigjenit në 24 orë ( ) 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 Përdorimi i privatësisë diferenciale 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 . Through investigation of a partial weight-sharing scheme , , , we showed that models can reach a comparable performance even when only 25% of weight updates are shared (Extended Data Fig. ) të 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 Rezultatet e validimit konfirmuan se modeli global është i fuqishëm, duke mbështetur hipotezën tonë se modelet e trajnuar nga FL janë të përgjithshme në të gjithë sistemet e kujdesit shëndetësor.Ata ofrojnë një rast bindës për përdorimin e algoritmeve parashikuese në kujdesin e pacientëve të COVID-19 dhe përdorimin e FL në krijimin dhe testimin e modeleve.Me pjesëmarrjen në këtë studim faqet e klientëve morën qasje në EXAM, për t'u validuar më tej para ndjekjes së ndonjë miratimi rregullator ose futjes së ardhshme në kujdesin klinik. , si dhe në vende të ndryshme që nuk ishin pjesë e trajnimit EXAM. 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 Identifikimi i kohortës së pacientëve dhe harmonizimi i të dhënave nuk janë çështje të reja në hulumtim dhe shkencë të të dhënave Përmirësimet në sistemet e informacionit klinik janë të nevojshme për të racionalizuar përgatitjen e të dhënave, duke çuar në një levizje më të mirë të një rrjeti të vendeve që marrin pjesë në FL. Kjo, në kombinim me inxhinierinë hiperparametrike, mund të lejojë algoritmet të ‘mësojnë’ më efektivisht nga grumbullimet më të mëdha të të dhënave dhe të përshtaten parametrat e modelit në një vend të veçantë për personalizim të mëtejshëm – për shembull, përmes përshtatjes së mëtejshme në atë vend. . A system that would allow seamless, close-to real-time model inference and results processing would also be of benefit and would ‘close the loop’ from training to model deployment. 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. Since our data access was limited, we did not have sufficient available information for the generation of detailed statistics regarding failure causes, post hoc, at most sites. However, we did study failure cases from the largest independent test site, CDH, and were able to generate hypotheses that we can test in the future. For high-performing sites, it seems that most failure cases fall into one of two categories: (1) low quality of input data—for example, missing data or motion artifact in CXR; or (2) out-of-distribution data—for example a very young patient. 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 search 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 , , tregojnë mbrojtje të mirë, ato mund të dobësojnë performancën e modelit. algoritmet e enkriptimit, të tilla si enkriptimi homomorfik Një mënyrë e numërueshme për të matur privatësinë do të lejonte zgjedhje më të mira për vendosjen e parametrave minimale të privatësisë të nevojshme duke ruajtur performancën e pranueshme klinikisht. , , . 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 Study setting 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. 1a 61 62 63 Data collection The 20 client sites prepared a total of 16,148 cases (both positive and negative) for the purposes of training, validation and testing of the model (Fig. ). 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 A ‘case’ included a CXR and the requisite data inputs taken from the patient’s medical record. A breakdown of the cohort size of the dataset for each client site is shown in Fig. . 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. ) të 1b 1c,d 6 Patient inclusion criteria Patient inclusion criteria were: (1) patient presented to the hospital’s ED or equivalent; (2) patient had a RT–PCR test performed at any time between presentation to the ED and discharge from the hospital; (3) patient had a CXR in the ED; and (4) patient’s record had at least five of the EMR values detailed in Table , të gjitha të marra në ED, dhe rezultatet përkatëse të kapura gjatë spitalizimit.Në thelb, CXR, rezultatet laboratorike dhe vitals përdorur ishin të para në dispozicion për kapjen gjatë vizitës në ED. Modeli nuk përfshinte ndonjë CXR, rezultatet laboratorike ose vitals fituar pas largimit nga 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 The distribution of oxygen treatment using different devices at different client sites is shown in Extended Data Fig. , 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 Vini re, modeli nuk është miratuar nga asnjë agjenci rregullatore në këtë kohë dhe duhet të përdoret vetëm për qëllime kërkimore. EXAM score EXAM was trained using FL; it outputs a risk score (termed EXAM score) similar to CORISK (Extended Data Fig. Ajo korrespondon me kërkesat e mbështetjes së oksigjenit të një pacienti brenda dy dritareve – 24 dhe 72 orë – pas prezantimit fillestar në ED. illustrates how CORISK and the EXAM score can be used for patient triage. 27 9a 9b Chest X-ray images were preprocessed to select the anterior position image and exclude lateral view images, and then scaled to a resolution of 224 × 224. As shown in Extended Data Fig. , the model fuses information from both EMR and CXR features (based on a modified ResNet34 with spatial attention pretrained on the CheXpert dataset) and the Deep & Cross network . 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 using the NVIDIA Clara Train SDK AUC mesatare për detyrat e klasifikimit (≥LFO, ≥HFO/NIV ose ≥MV) u llogarit dhe u përdor si metrikë përfundimtare e vlerësimit, me normalizim në mesatare zero dhe variancë unitare. imazhet CXR u përpunuan paraprakisht për të zgjedhur serinë e saktë dhe për të përjashtuar imazhet e shikimit anësor, pastaj u shkallëzuan në një rezolucion prej 224 × 224 (ref. ) të 9a 66 67 68 9b 69 70 27 Feature imputation and normalization Algoritmi i keqpërdorimit was used to impute EMR features, based on the local training dataset. If an EMR feature was completely missing from a client site dataset, the mean value of that feature, calculated exclusively on data from MGB client sites, was used. Then, EMR features were rescaled to zero-mean and unit variance based on statistics calculated on data from the MGB client sites. 71 Details of EMR–CXR data fusion using the Deep & Cross network Për të modeluar ndërveprimet e karakteristikave nga të dhënat EMR dhe CXR në nivelin e rastit, është përdorur një skemë e karakteristikave të thella bazuar në një arkitekturë të rrjetit Deep & Cross. . 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 A pseudoalgorithm of FL is shown in Supplementary Note . 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, , was up to 20 depending on the network connectivity of clients or available data for a specific targeted outcome period (24 or 72 h). The number of local training iterations, , varet nga madhësia e dataset në çdo klient 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 In this study we investigated a privacy-preserving scheme that shares only partial model updates between server and client sites. The weight updates were ranked during each iteration by magnitude of contribution, and only a certain percentage of the largest weight updates was shared with the server. To be exact, weight updates (also known as gradients) were shared only if their absolute value was above a certain percentile threshold, (t) (Extended Data Fig. ), e cila është llogaritur nga të gjitha gradientët jo-zero, Δ , and could be different for each client in each FL round . Variations of this scheme could include additional clipping of large gradients or differential privacy schemes që shtojnë zhurmë të rastësishme në gradientet, ose edhe në të dhënat e papërpunuara, para se të ushqehen në rrjet . 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. Hipoteza zero u refuzua me anë të njëanshme. « 1 × 10–3 në të dy rastet. 2 1 P Korrelimi i Pearson-it është përdorur për të vlerësuar përgjithësueshmërinë (robustness e vlerës mesatare të AUC në të dhënat e testimit të faqeve të tjera të klientit) të modeleve të trajnuar në vend në lidhje me madhësinë përkatëse të datasetit lokal. = 0.43, = 0.035, degrees of freedom (df) = 17 for the 24-h model and në 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 Për të krahasuar kurvat ROC nga modeli global FL dhe modelet lokale të trajnuar në vende të ndryshme (Figura e të dhënave të zgjeruara. ), 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 është diferenca e standardizuar, is the standard deviation of the bootstrap differences and AUC1 and AUC2 are the corresponding bootstrapped AUC series. By comparing me shpërndarjen normale, ne kemi marrë 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 vlerat janë kryer në R me bibliotekën pROC . 3 D s D s D P 2 P P 74 Since the model predicts a discrete outcome, a continuous score from 0 to 1, a straightforward calibration evaluation such as a qqplot is not possible. Hence, for a quantified estimate of calibration we quantified discrimination (Extended Data Fig. Ne kemi kryer analiza të njëanshme të variacionit (ANOVA) teste për të krahasuar rezultatet e modelit lokal dhe FL në mesin e katër kategorive të së vërtetës së tokës (RA, LFO, HFO, MV). -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 -Vlerat e pesë vendeve lokale të ndryshme janë 245.7, 253.4, 342.3, 389.8 dhe 634.8, ndërsa vlera e modelit FL është 843.5. -Vlera do të thotë se grupet janë më të ndara, rezultatet nga modeli ynë FL tregojnë qartë një shpërndarje më të madhe në mesin e katër kategorive të së vërtetës themelore. value of the ANOVA test on the FL model is <2 × 10–16, indicating that the FL prediction scores are statistically significantly different among the different prediction classes. 10 F F F P Reporting Summary Further information on research design is available in the linked to this article. Nature Research Reporting Summary Disponueshmëria e të dhënave Të dhënat nga 20 institutet që morën pjesë në këtë studim mbeten nën kujdesin e tyre. Këto të dhëna u përdorën për trajnim në secilin nga faqet lokale dhe nuk u ndanë me asnjë nga institucionet e tjera pjesëmarrëse ose me serverin e federuar, dhe ato nuk janë të disponueshme publikisht. Të dhënat nga faqet e pavarura të validimit mbahen nga CAMCA, dhe qasja mund të kërkohet duke kontaktuar Q.L. Bazuar në përcaktimin nga CAMCA, një rishikim i ndarjes së të dhënave dhe ndryshimi i IRB për qëllime kërkimore mund të kryhet nga administrimi i kërkimit MGB dhe në përputhje me IRB dhe politikën MGB. 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 Në : The federated learning software is available as part of the Clara Train SDK: . Alternatively, use this command to download the model “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. M., Preziosi, M.-P. & Swaminathan, S. Data sharing for novel coronavirus (COVID-19). , 150 (2020). Bull. World Health Organ. 98 Chen, Q., Allot, A. & Lu, Z. Keep up with the latest coronavirus research. , 193 (2020). Nature 579 Fabbri, F., Bhatia, A., Mayer, A., Schlotter, B. & Kaiser, J. BCG IT spend pulse: how COVID-19 is shifting tech priorities. 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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 This paper is under CC by 4.0 Deed (Attribution 4.0 International) license. available on nature This paper is nën licencën CC by 4.0 Deed (Attribution 4.0 International). available on nature
