dataesr/scientific-paragraphs-categorization huggingface dataset:
Both the raw cohort data and the curated cohort data, as well as, the harmonized cohort data are stored in these private cloud databases. The data providers manage their private cloud spaces, similarly to the Google Drive, for personal use. SSL/TLS communication protocols were used for the communication with the private database of each data provider. 2. 2. 3. Data quality assessment 2. 2. 3. 1. Metadata extraction and outlier detection. Useful metadata were automatically extracted from the 21 cohort databases, regarding the names of the available features and the value ranges, followed by a short description of the clinical domain knowledge. Then, the data curation workflow ensures that the structure of each shared dataset fulfills the following requirements: (i) the shared pseudonymized data are stored in a tabular format, (ii) each row in the tabular format corresponds to a patient record, and (iii) each column in the tabular format corresponds to a feature (e.
Final annotation and data quality approval. A data evaluation report was generated, where the available features within the raw cohort data were classified according to their quality status into three types, namely the: (i) ''bad" features (having more than 50% missing values), (ii) ''good" features (no missing values), and (iii) ''fair" features (having less than 50% missing values). The ''bad" features are excluded from the analysis. Features with detected outliers and/or unknown data types are marked to be excluded from the analysis. The cohort data curation workflow can be recursively applied until the cohort data quality metrics (completeness, conformity, and relevance) are fulfilled. The data evaluation report along with the curated cohort data were finally stored in the private cloud spaces which are linked with each individual cohort database.
Data sharing management module The data sharing management module is responsible for: (i) handling the requests for cohort data access that are made by the data processors through the application of the data analytics services (this process is referred to as ''handshaking"), and (ii) providing regular reports to the data providers regarding the usage of the cohort data. The ''bring the analysis to the data" design is adopted according to which the data never leave from their private cloud spaces during when a data analytics workflow takes place. In the HarmonicSS platform, a federated data analytics workflow is executed only when the data provider approves the request that is sent by the data processor who invokes this workflow. Thus, when a data processor wishes to train a lymphoma classification model across multiple cohorts, the handshaking service informs, in real time, the data providers who own these cohort databases for this specific request.
Cohort data harmonization module 2. 4. 1. Construction of the pSS reference ontology The core of the cohort data harmonization process is based on the use of the pSS reference ontology  [37]. The latter is a hierarchical data model which consists of classes, subclasses and object properties which capture the clinical domain knowledge of pSS. The reference ontology was constructed in cooperation with the clinical experts to reflect the minimal requirements of the pSS domain, i. e. , a set of clinical, demographic, laboratory and therapeutic-related parameters which describe the inclusion criteria for pSS. The pSS reference ontology includes 5 main classes (i. e. demographics, therapies, biopsies, medical conditions, laboratory tests) with more than 150 terminologies and was expressed into a.
Semantic matching and data standardization Based on the metadata extracted from each cohort, an ontology was constructed to represent the structure of each cohort as an hierarchical data model (Fig. 2 ) through the definition of entities, data properties (data types) and object properties (hierarchical relationships), using Protégé  [39]. The extracted metadata include information regarding the terminologies, the range values, and a short description of each concept. The pSS reference ontology from Section 2. 4. 1 was utilized as a gold-standard data model to align the structure and the range values of the individual ontologies. More specifically, the terminologies of the individual ontologies were semi-automatically matched with those from the pSS reference ontology through the definition of pairing rules according to clinical guidance, as well as, through the suggestion of relevant matches.
Upon the precise definition of the pairing rules, the range value of each matched terminology was standardized through an additional computational procedure which involves the alignment of the heterogeneous value ranges in each individual ontology with the pre-defined range values in the pSS reference ontology which was expressed into the form of a mapping file (similar to a. log file) which was stored in the secure private cloud space of each cohort. The data access handler (Fig. 2 ) was used to monitor the data access as part of the data sharing management module (Section 2. 3).
Harmonized cohort data storage In the final stage of the cohort data harmonization process, the mapping files of each individual cohort was used to align the structure of the cohort data yielding the harmonized cohort data (Fig. 2 ). The latter were stored in secure private cloud databases within the HarmonicSS cloud computing infrastructure. The private cloud databases are also referred to as federated databases. The latter were developed in SPARK-SQL (Structured Query Language) where secure communication protocols were established to enable easier data access and management under a virtual private network (VPN) connection.
Federated AI framework For a given a set of N-databases, say fD 1 ; D 2 ; Á Á Á ; D N g; a machine learning algorithm trained on the dataset d i 2 D i is updated through the following function: F d i ð Þ ¼ F d iÀ1 ð Þþbqðd i Þ; ð4Þ where, F d i ð Þ corresponds to the estimated ML model which has been trained on the dataset d i , F d iÀ1 ð Þcorresponds to the estimated model which was trained on d iÀ1 , qðd i Þ is the learner on d j , and b is a scalar. A loss function can then be defined in the form L f d i ð Þ; y i ð Þ where f d i ð Þ is the estimator and y i is the target score. Then, the stochastic gradient descent (SGD) approach  [40]  is used to minimize the loss function through the following sequential weight update process:    An illustration of the federated AI workflow is depicted in Fig.
To do so, the DART is trained on random subsets to prevent the definition of trivial trees. For a model, say Q , where Q ðdÞ is the prediction for sample d, and L Q d ð Þ ð Þis the loss function DART creates the random subset  [43] : d; Àr t L Q d ð Þ ð Þ ð Þ f g ; ð11Þ where a new label with values Àr d L Q d ð Þ ð Þis assigned for each sample d in the training dataset.
To deal with the computational burden introduced in Eq. (  12 ), we adopt an estimation process  [45]  which reduces the complexity from OðTL2 Z Þ to OðSLD 2 Þ, where T is the number of trees, L is the total number of leaves, Z is the number of features, and D is the tree depth. The cover metric was also used to measure the number of observations which are related to a particular feature. For each feature, the relative number of observations is calculated as the number of splits that this feature participated across each ensemble and averaged across the training instances on each distributed database.
Visual analytics and user interfaces module The visual analytics methods were implemented using the HealthVision web visualization platform  [46]  which consists of visualization and data analysis components that are linked to each other in reactive workflows. Each component accepts specific inputs, either from other components or from the user, and produces outputs that can be used by other components, or renders visual components (input controls, etc. ) on the screen. The user interface (UI) serves as connecting link between the platform user and the backend services (Fig. 4 ). The contribution is shared between the actual UI client, and the UI backend server. The backend server is responsible for orchestrating: (i) access to the REST (Representational state transfer) services of other users, including user authentication, execution of data analytics services, data sharing management, etc.
Cohort data origin A summary of the overall demographic information from the 21 European databases on pSS is presented in Table  1. The total number of eligible patients who fulfilled the inclusion criteria was 7,156, where the gender information was recorded for 7,000 patients (6,512 females, 488 males with a female to male ratio 13. 34%). The average age at SS diagnosis in the female group was 51. 82 (AE 13. 96) years whereas in the male group the average age was 54. 24 (AE 13. 77) years. The lymphoma types include the B-cell Mucosa-associated Lymphoid Tissue (MALT) Lymphoma, the Diffuse Large B-cell Lymphoma (DLBCL), the B-cell Nodal Marginal Zone Lymphoma (NMZL), the B-cell Splenic Marginal Zone Lymphoma (SMZL), and other mature B-cell neoplasms. These lymphoma types were merged into a single lymphoma type with 354 positive lymphoma patients and 6,802 non-lymphoma (or missing) patients (lymphoma to non-lymphoma ratio 5.
High quality and harmonized cohort databases Data curation was applied on each individual cohort database to automatically remove outliers, data inconsistencies and duplicated fields. The LOF algorithm was combined with the Isolation Forests to track down and remove outliers with 90% accuracy and the Spearman correlation coefficient was combined with the Jaro distance score to detect duplicated features. Data imputation was applied only to features with less than 30% missing values as in  [47]  upon approval from the clinical experts. Upon the completion of the cohort data curation process, ontologies were constructed for each curated cohort database based on the extracted metadata. Semantic mapping rules were defined between the individual ontologies and the pSS reference ontology. As shown on Supplementary Table  1 , the cohort data harmonization process resulted in 48 common concepts (or terminological concepts) which constitute the pSS minimal criteria (minimal common data elements) across the 21 federated cohort databases.
Federated AI models for lymphoma classification and biomarker extraction According to Table  2 , the lymphoma over non-lymphoma ratio was 5. 2% which implies a significant population imbalance. To deal with this, random downsampling with replacement  [15]  was applied on each individual training cohort database among the lymphoma (target group) and the non-lymphoma (control group) patients. The process was repeated ten times to avoid biases during the downsampling process. On each iteration, the downsampled control group was matched with the target group according to the age, gender, and disease duration using a ratio 1:1 to yield equally balanced populations. The Wilcoxon Mann-Whitney rank-sum test was used to evaluate whether the distributions of  the age and disease duration did not significantly deviate between the target group and the downsampled control group whereas the chi-square test was used for gender matching.
The classification performance of the federated AI models was assessed based on the accuracy, sensitivity, specificity, and area under the ROC curve (AUC). Four large scale federated lymphoma classification scenarios were conducted; three scenarios including a common set of training harmonized cohort databases and three different testing databases, as well as, one scenario with a different set of training databases and a single testing database. The training set in federated scenarios 1-3 is {UOA, UNIPI, UNEW, UNIPG, PARIS, UoB, UNI-VAQ, HUA, UOI, UU, UNIRO, UMCU, MHH, UBO} and the testing set is {AOUD (scenario 1), UNIPG (scenario 2), HUA (scenario 3)} whereas the training set in federated scenario 4 is {AOUD, UOA, UNIPI, UNIPG, UNEW, PARIS, UoB, UNIVAQ, UOI, UU, UNIRO, UMCU, MHH, UBO, UMCU} and the testing set is HUA.
8, specificity 0. 73). In federated scenario 4, the FDART with dropout rates 0. 1 and 0. 2 achieved the best performance (accuracy 0. 78, sensitivity1, specificity 0. 76) like the FGBT (accuracy 0. 75, sensitivity 1, specificity 0. 74). In the final scenario, the FDART with dropout rate 0. 3 achieved the best performance (accuracy 0. 8, sensitivity 0. 87, specificity 0. 79) yielding better sensitivity than the FGBTs, where the average execution time was 30 s for data access and training/testing on each harmonized cohort database.
Discussion The HarmonicSS cloud computing services delineated the clinical picture and unmet needs of pSS through: (i) the utilization of a unique data governance framework that enables the extensive evaluation of the DPA and DPIA documents by the Data Controller's Committee (DCC) and the secure upload of the GDPR compliant cohort data in federated databases, (ii) the application of cohort data curation and harmonization workflows on 21 regional, national and international European cohorts on pSS yielding 7,156 high quality patient records, and (iii) the utilization of high-performance federated AI workflows towards the development of explainable and trustworthy federated AI models for lymphoma classification and biomarker extraction. The data sharing assessment module can also support cross border data sharing since it ensures: (i) the secure upload of the legally and ethically compliant data on the federated databases of the platform using secure data encryption protocols, and (ii) the secure access of the data analytics services on the data through the handshaking protocol towards the development of trustworthy AI models.
The existing platforms and tools that have been developed for data curation, harmonization and federated or distributed data analysis are presented in Supplementary Table  2  and compared against the core services of the HarmonicSS platform. The existing studies for data curation focus on the development of software tools, such as, the ExeTera software  [16] , where the absence of quantitative methods for data curation along with the lack of reusable quality reports hampers the generalizability of the software. In addition, the DPUK  [17]  adopts a qualitative approach based on quality criteria that are manually defined for each individual data source. In HarmonicSS, quantitative data curation tools have been developed to enhance the quality of the data and provide re-usable reports to the clinicians.
Regarding data harmonization, the Data-SHaPER  [18]  utilizes ontologies based on the definition of a DataSchema which is not widely used as a semantic data model. The BiobankConnect software  [19]  focuses on lexical matching which can lead to information loss when the terminologies are conceptually similar. The SORTA tool  [20]  focuses on the alignment of heterogeneous ontologies through manual semantic interlinking methods. Contrary to these, the HarmonicSS platform offers a cohort data harmonization service which uses lexical and semantic matching to identify terminologies with common lexical and conceptual basis, where the pSS reference model is expressed into a. RDF/. OWL format. As far as federated/distributed learning is concerned, the euroCAT platform  [21, 22]  requires the installation of local servers on each hospital's premises, where the distributed learning algorithms include Bayesian networks and Support Vector Machines which were trained across 3 centers to predict dyspnea yielding modest prediction performance.
In the PHT platform  [23] , a distributed logistic regression model was trained across 8 sites to predict post-treatment with adequate performance. In other studies, lymphoma classification models were trained across 4 pSS cohorts for federated lymphoma classification  [15]  and single cohorts were used to develop lymphoma classification models with reduced statistical power  [11] [12] [13]. The HarmonicSS platform removes the need for the installation of local servers or any type of software on premises through the development of a federated data management system that supports a large family of federated AI algorithms with small execution time complexity yielding robust and explainable AI models for lymphoma classification. In addition, the HarmonicSS platform is cloud agnostic and thus can be adapted to any cloud infrastructure.
The automated data curation workflow enhanced the quality of the raw cohort data at a great extent and the cohort data harmonization module enabled the application of semantic interlinking mechanisms on each curated cohort database yielding harmonized cohort databases with 7,156 patients. The federated AI services provided trustworthy and explainable AI models for lymphoma classification and biomarker(s) detection, where the federated AI modeling process is orchestrated by the central computing engine   (CCE). The access of the CCE to each individual harmonized cohort database is monitored by the data sharing management module to enhance the trustworthiness of the AI models. So far, the platform supports six algorithms for federated learning, including the federated logistic regression, the federated SVM, the federated MLP, the federated Multinomial Naïve Bayes (FMNB), the federated gradient boosting trees (FGBT), and the FGBTs with and without dropout elimination (FDART).
The FGBT and the FDART achieved the best performance for lymphoma classification due to the robustness of the boosting stages which reduce the classification error on each stage. The FDART outperformed the rest of the algorithms yielding lymphoma classification models with average AUC 0. 87 across the scenarios. The dropout rates introduced by the FDART yielded slightly better performance than the FGBT which confirms that the dropout elimination can enhance the decision-making process. The execution time of the federated AI workflows was 30 s (in average) per database which confirms the small execution time complexity. The biomarkers for lymphoma development include parotid or submandibular swelling, cryoglobulinemia, rheumatoid factor, and low C4 levels, among others, which have been validated in previous studies  [48] [49] [50]  highlighting the significance of parotid or submandibular gland swelling, low C4, rheumatoid factor and cryoglobulinemia for lymphoma development.
The federated AI workflows which are offered by the platform are built on top of a federated repository on autoimmune disease data along with the data curation and harmonization services for enhancing the quality of the cohort data. These services are executed under a PaaS (Platform as a Service) cloud computing model, which enhances the sustainability of the platform based on three trajectories: (i) the maintenance of the PaaS operations from a legal and ethical point of view, (ii) the collaboration among multidisciplinary and international partners able to attract funds and investments from sponsors, and (iii) the implementation of a business model. The fact that the HarmonicSS platform is compliant with HL7 standards enhances its applicability to other clinical domains. The federated data analytics services of the platform can be applied only on harmonized databases and thus emphasis shall be given towards the definition of an ontology for the domain of interest.
Apart from the core modules though, the HarmonicSS platform provides services for health policies impact assessment, association rule mining, and query-based knowledge discovery, as well as, tools for salivary gland ultrasonography image segmentation and patient selection for multinational clinical trials, and training material for both clinicians and patient organizations. The platform can also offer, upon request to the DCC, the option for the extraction of the anonymized harmonized data to provide the clinicians with the opportunity to apply statistical analysis and related approaches. The federated AI model can be used for the accurate risk prediction of lymphoma and thus contribute to the early lymphoma diagnosis in patients who have been diagnosed with pSS avoiding additional costs for biopsies.
For this purpose, the CCE was built on top of virtual machines (VMs) which were utilized in the GRNET cloud infrastructure to enable the secure access of the AI model's weights on each cohort database. For demonstration purposes and according to Fig. 3, which depicts the federated AI model training and testing work-flow, the set of training cohorts was defined as fA; B; C; D; E; Fg and the testing cohort was set to fGg. The model M is first loaded into the Central Computing Engine (CCE) along with the set of training and testing cohorts. According to the workflow (Algorithm 1, Fig. 3), the model is trained on the dataset in location P A yielding the model M A. The model's weights are incrementally updated on the training cohorts which are stored in the private locations P B ; P C ; P D ; P E ; P F yielding the intermediate AI models M B ; M C ; M D ; M E ; M F.
Acknowledgement This work has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  731944  and from the  Swiss State Secretariat for Education, Research and Innovation SERI  under grant agreement  16. 0210  ( HarmonicSS -HARMONIzation  and integrative analysis of regional, national and international Cohorts on primary Sjögren's Syndrome (pSS) towards improved stratification, treatment and health policy making). SJ Bowman's salary is part funded by the  Birmingham NIHR Biomedical Research Centre.
Algorithm 1. A pseudocode implementation of the federated AI modeling process. Input parameters train = a set of training cohorts which are stored in federated databases (and private cloud spaces) test = a set of testing cohort(s) which are stored in federated databases (and private cloud spaces) M = an initial supervised machine learning model 1 def federatedAImodeling (train ¼ A; B; C; D; E; F f g ; test ¼ G f g; M): 2 train the initial model M on the dataset in location P A and receive the model M A 3 store the weights of M A in the Central Compute Engine (CCE) 4 for iintrain do: 5 retrieve weights and send them to location P i 6 update the weights of M i on dataset in location P iþ1 through (5) 7 store the weights of the model M iþ1 in the CCE for the update process in the next location 8 retrieve the final federated model M G from the training stage 9 evaluate the performance of M G on the dataset in location P G (test) 10 return M G ;.
Search and eligibility The search strategy was exhaustive, not restricted to a specific language or year of publication. Databases included Ovid, PubMed, CINAHL, ISRCTN and the Cochrane database of systematic reviews, from inception to August 12, 2021. We searched for RCT's and observational studies that investigated the association between OC screening and OC mortality, as well as downstaging (Appendix 1). We further hand-searched the citations of the retrieved eligible papers to identify additional publications that might have been missed during the initial search. We also searched clinicaltrials. gov for non-published studies.
Data analysis Review Manager software was applied to synthesise the outcomes. Dichotomous outcomes, such as OC mortality, OC detection, advanced stage OC detection and OPMD detection were used to estimate the effect of screening as expressed by risk ratios with a confidence interval (95 %). We used the I 2 statistic to assess a level of heterogeneity and so to define a possibility for quantitative synthesis. A meta-analysis, with a weighted random effects model was performed to report on the effect of OC screening on mortality and advanced OC cases. Only studies reporting similar outcomes were included in the meta-analysis.