dataesr/scientific-paragraphs-categorization huggingface dataset:
Performance of the SOSIC scores We developed three models using XGBoost algorithms that accurately predicted 90-day mortality using data from ICU D1 and the prediction day. Apparent-, bootstrap-corrected-, and test-dataset-validation metrics are reported in the Additional file 3. Based on the test dataset the AUC was slightly higher for SOSIC-7 (0. 80 [0. 74-0. 86]) than for SOSIC-1 (0. 76 [0. 71-0. 81]) and SOSIC-14 (0. 76 [0. 68-0. 83]). Similarly, SOSIC-1 and SOSIC-7 calibration curves were excellent (Fig. 3 ). Those findings indicate fair agreement between predicted and observed risks in those two scores. Although calibration of SOSIC-14 was lower (slope 0. 83 [0. 49-1. 22]) compared to the SOSIC-1 and SOSIC-7, the Brier scores, which assess both calibration and discrimination of the predictive models, were similar for all three models.
Indeed, the AUC was not better at day-7 or 14 compared to day-1 and its better performance compared to the SOFA-score was reduced over time. A greater number of variables inducing a higher heterogeneity associated with a reduction of the sample size of the development and the test datasets at day-7 and 14 could explain this finding. Because no models predicting COVID-19 outcomes focused on patients already in the ICU  [5, 6, 26] , the SOSIC scores have the potential for clinical usefulness and generalizability. Internal validations of the SOSIC scores showed consistent discrimination and calibration, which obviously deserves further external validation. With an AUC around 0. 80, external validation is desirable to assess the mortality prediction beyond population levels and to fully assess the mortality risk of the individual being admitted and cared for up to 2 weeks in-ICU.
Caregiver reluctance to provide noninvasive oxygen strategies has been overcome  [3] , leading to higher percentages of patients on high-flow oxygen and noninvasive ventilation, and lower rates of intubation on ICU D1  [3, 28]. Debates are still ongoing as to the best timing of intubation in that population, as recent data have suggested poorer outcomes associated with an early intubation strategy  [29] [30] [31]. Thus, the very high percentages of our patients intubated on ICU D1 will probably differ during subsequent COVID-19 outbreaks, in countries with different public healthcare organizations or ICU admission policies. Indeed, SOSIC predictions should be interpreted as reflecting a profile of critically ill patients with COVID-19 not routinely treated with corticosteroids and outside vaccination campaigns, which may have changed since May 2020.
Another limitation is that we only included predictors that were routinely collected in the COVID-ICU database during the study period. Thus, we cannot rule out that some additional laboratory or ventilatory parameters reflecting respiratory mechanics (especially measured on ICU D7 or D14) would have improved SOSIC-score performances. We were also unable to integrate d-dimer concentrations as initially planned  [33] , because of their inconsistent collection at ICU admission. Although the XGBoost algorithm incorporates missing data in its split finding algorithm, we cannot guarantee that this method can handle any pattern of missing data effectively  [34]. As this algorithm potentially exploits the data missingness patterns for prediction, a major shift in the missingness mechanism in an external independent sample may affect SOSIC scores performance.
75 95%CI [0. 69;0. 81] vs 0. 76 95%CI [0. 67;0. 85]). Similarly, calibration slope close to one whatever the region (Parisgreater area and Grand Est AUC: 0. 92 95%CI [0. 65;1. 21] versus other regions AUC: 0. 90 95%CI [0. 53;1. 33]). Similar results were observed for SOSIC-7 and SOSIC-14 models and are reported in Additional file 5. The internal validation of SOSIC-1 on the test dataset exhibited fair performance (AUC: 0. 76 [95%CI 0. 71;0. 81]) in contrast to much poorer discrimination of the SAPS II (AUC:0. 64 [95%CI 0. 58;0. 70]) and SOFA scores (AUC:0. 62 [95%CI 0. 56; 0. 69]). Graphic representation of the SOSIC-1, SAPS II, and SOFA discrimination performances is shown in Fig. 4. Similarly, performances of the SOSIC-7.
Characteristics on ICU day-7 or day-14 of the development and test datasets of the SOSIC-7 and SOSIC-14 scores according to their 90-day survival status Results are expressed as n (%) or median (25th-75th percentiles). ICU intensive care unit, SOFA Sequential Organ-Failure Assessment, MV mechanical ventilation, VAP ventilator-associated pneumonia a Only the most intensive ventilation modality (i. e. , invasive MV > noninvasive ventilation > high-flow oxygen > standard oxygen) on day-7 or day-14 is reported Death (740) P value Alive (217) Death (75) P value Alive (1251) Death (413) P value Alive (142) Death (43) P value On day-7 or day-14 in.
Funding This study was funded by the  Fondation APHP  and its donators though the program " Alliance Tous Unis Contre le Virus ", the  Direction de la Recherche Clinique et du Développement , and the  French Ministry of Health. The  Reseau European de recherche en Ventilation Artificielle (REVA)  network received a 75,000 € research grant from  Air Liquide Healthcare. The funders had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The sponsor was  Assistance Publique Hôpitaux de Paris (APHP).
Wikidata recently supported entity schemas based on shape expressions (ShEx). They play an important role in the validation of items belonging to a multitude of domains on Wikidata. However, the number of entity schemas created by the contributors is relatively low compared to the number of WikiProjects. The past couple of years have seen attempts at simplifying the shape expressions and building tools for creating them. In this article, ShExStatements is presented with the goal of simplifying writing the shape expressions for Wikidata.
INTRODUCTION Entity schemas based on ShEx (Shape Expressions)  [10, 13, 15]  were recently introduced on Wikidata  [17]. One of the main advantages of Shape Expressions is that they can be used for RDF validation  [7, 14, 16]. Several tools and scripts currently exist that can be used to visualize and validate a subset of data on Wikidata using ShEx. One such tool is shex. js [4] , which let the Wikidata contributors easily check entities against any particular schema. A SPARQL query is used to select a subset of relevant data from Wikidata and the validation is run on this prefetched data. Thus users can both test and explore the current state of the data related to the SPARQL query. They may propose new modifications to the entity schema or even correct the data items. In the case of Wikidata, WikiProjects are used to identify and discuss relevant properties for the items to a particular domain.
For example, WikiProject Informatics 1  identifies properties for software, hardware, programming languages, file sytems, algorithms, etc. The number of Wikiprojects is an interesting indicator for measuring the use of entity schemas since WikiProjects are managed by dedicated contributors interested in a particular domain. At the time of writing, there are only less than 300 shape expressions 2  on Wikidata. This number is quite low compared to the number of WikiProjects on Wikipedia  [9]  and Wikidata  [6]. Therefore any tool for shape expressions must take into consideration such WikiProjects and propose ways to integrate the information present in these tools to build shape expressions. One possible approach is to propose a smaller subset of shape expressions that can be used to build simple shape expressions in a manner that closely resembles some of the existing templates  3.
These simple expressions can take into account the WikiProjects for validating whether the items belonging to a given domain have all the necessary statements. Considering the multilingual nature, another important aspect is to let the communities describe relevant domains in their local languages. ShExStatements 4   [12]  was developed to answer these requirements. It was developed in a manner similar to the QuickStatements 5  and OpenRefine 6   [3]  to ensure a simpler interface using tabular formats or CSV files. In this article ShExStatements  [12]  is presented, explaining how a tabular format or a CSV file format was developed for simplifying writing shape expressions, especially for the new comers. In section 2, state of the art is presented. Taking an example, the grammar of ShExStatements is described in section 3.
RELATED WORKS Several WikiProjects 7  are currently available on Wikidata related to open government data, culture, history, sports, birds, agriculture, tourism, etc. Some of these WikiProjects take into consideration the infoboxes of Wikipedia  [8]  belonging to different languages to identify the different properties used to describe the objects belonging to a certain class. These infobox properties are then mapped to appropriate Wikidata properties. WikiProjects, therefore, play an important role in identifying the key Wikidata properties. However, WikiProjects alone cannot be used to automatically validate the existing Wikidata items. Though Wikidata supports property constraints  8  , their usage is limited to specifying how properties can be used. Wikidata items use multiple properties and schemas are needed to describe and validate the items belonging to different classes.
This is very important in multilingual and multi-domain context. Therefore, validation of RDF  [7, 14, 16]  is important to ensure data present in a semantic knowledge base is following the proposed ontology or schema. Several tools have been proposed that take into consideration the expressivity  [15]  of shape expressions. These tools can be classified in the way shape expressions can be created. The first approach is to automatically generate schema expressions from existing RDF data. Designer  [1] , Wikidata Shape Expressions Inference  9  , and sheXer  10 are some examples. Visual interfaces have also been suggested to understand, modify and create new shape expressions. YASHE  11 and ShExAuthor  12  are examples of some visual tools for creating Shape Expressions. Finally, there are approaches that propose a smaller subset of the ShEx language.
Shex-Lite 13   [2] , ShExML  [5] , and ShExStatements belong to this category. ShExML is a language developed to integrate multiple heterogeneous data sources. Shex-Lite is meant to be an independent language, maintaining compatibility with ShEx, and can be used to generate object models in object-oriented programming languages. ShExStatements, on the other hand, is a language developed to generate ShEx from CSV files and tabular formats.
SHEXSTATEMENTS To explain the grammar of ShExStatements, an example is given below in Figure  6. It describes the ShExStatements of a human language on Wikidata. wd,<http://www. wikidata. org/entity/>,,, wdt,<http://www. wikidata. org/prop/direct/>,,, @language,wdt:P31,wd:Q34770,,# instance of a language @language,wdt:P1705,LITERAL,,# native name @language,wdt:P17,. ,+,# spoken in country @language,wdt:P2989,. ,+,# grammatical cases @language,wdt:P282,. ,+,# writing system @language,wdt:P1098,. ,+,# speakers @language,wdt:P1999,. ,*,# UNESCO language status @language,wdt:P2341,. ,+,# indigenous to.
Cardinality can be any one of the following values (1) * : zero or more values (2) ? : zero or one (3) + : one or more values (4) m : m number of values (5) m,n : any number of values between m and n (including m and n). Take the fifth statement that states that a language can have one or more writing systems, hence the use of + in the fourth column. But the third column can also be another node. A ShExStatements file can also use delimiters like vertical bar (|) or semicolons (;). The following example in Figure  3  shows these two cases. This example is a ShExStatements of a TV series. The first statement describes that a TV series is an instance of wd:Q5398426 (television series). The second statement states that a TV series has zero or more genres wdt:P136. However, to describe a genre, we need additional statements.
ShExStatements consists of one or more statements, often preceded by prefix statements. There may exist blank lines (NEWLINE) between the statements. Listing 1: ShExStatements: statements s t a t e m e n t s : NEWLINE | s t a t e m e n t | s t a t e m e n t s t a t e m e n t s | p r e f i x e s s t a t e m e n t s t a t e m e n t s A statement may be one of the following use cases: (1) a node with a property and an associated property value. For example, a language has a property value of wd:Q34770 for the property wdt:P31. (2) a node with a property and multiple associated values separated by a comma. For example, a genre can have any one value from wd:Q201658, wd:Q15961987. for the property wdt:P31. (3) a node with a property and an associated property value, along with the cardinality. For example, a language has a property value of.
DEVELOPMENT ShExStatements is developed in Python and has multiple interfaces. It can be executed from the command line. There is also a web interface as shown in Figure  5  and an API that allows users to generate shape expressions from CSV files. It uses the library ply 14  for writing the grammar as described above and the parser for parsing CSV files or input. The web interface is built using Flask 15  and pyshex 16  is used to generate ShExj 17  from ShExStatements.
ShEx generation ShExStatements is also available on Python package index 18  and therefore can be installed using pip. Once ShExStatements is installed, run the following command with the above example written in a file (for example, language. csv). This file contains an example description of a language on Wikidata and uses comma as a delimiter to separate the values. $. / s h e x s t a t e m e n t s. sh l a n g u a g e. c s v ShExStatements will generate the following Shape Expression (ShEx). It is also possible to use shexstatements in Python programs. The method _ℎ_ _ takes as input a CSV file containing shexstatements and a delimiter. In this example, we use ", " as a delimiter. Listing 17: ShExStatements: Using Python library from s h e x s t a t e m e n t s. s h e x f r o m c s v import CSV s h e x = CSV.
RESULTS ShExStatements is also available on Toolforge 19  along with a detailed documentation 20. A number of shape expressions were created during COVID-19 Biohackathon April 5-11 2020 21  using ShExStatements. For example, pandemic (EntitySchema:E184 22  ), hospital, preprint, lockdown, etc. The primary goal was to identify the key properties for these entities, which could later be improved and extended. During this hackathon, the possibility of using such simple shape expressions for selecting data from Wikidata was also discussed. Wikidata is a multilingual knowledge base. One of the main objectives of ShExStatements is to ensure its use by multilingual users. ShExStatements mainly makes use of symbols and positions for specifying prefixes or even imports. EntitySchema:E210 23  is one such example which was generated from examples/hospital.
Limitations and Future Works Currently, ShExStatements only works with CSV files or tabular formats. Future works include supporting formats like Office Open XML format and Excel files. User evaluation tests are required to understand the challenges associated with writing shape expressions. Even though ShExStatements was tested mainly for data on Wikidata and for creating new entity schemas, it can also be used for generating ShEx for other RDF data sources. This needs to be further explored. Another possible major work is to integrate ShExStatements in such a manner that users can directly create new entity schemas from the ShExStatements application. Currently, users need to manually copy the generated shape expression from ShExStatements and then create a new entity schema on Wikidata.
CONCLUSION Validation of data is important, especially for Wikidata considering the multilingual and multi-domain nature of the knowledge base. The recently introduced shape expressions (ShEx) is a major step in this direction. To promote its use, more tools may be required. Tabular formats, especially CSV files are commonly used file formats by Wikidata contributors while using tools like OpenRefine and QuickStatements. In this article, ShExStatements tool was presented to simplify writing shape expressions using CSV files. A subset of ShEx was used for building ShExStatements. With a command-line interface, a Python library, and a web interface, ShExStatements provide a wide variety of ways to generate shape expressions using this simpler subset.
This partition is then interpolated to the blood cavity using the Laplace equation, which allows to compute regional volumes. For obtaining an end-systole (ES) partition, the endocardial partition is transported from ED to ES using a commercial imagebased tracking software, and then the interpolation process is repeated. We assessed the intraand inter-observer reproducibility using a 10-subjects dataset containing repeated quantifications of the same images, obtaining intra-and inter-observer errors (7 -12% and 10 -23% respectively). Finally, we propose a novel synthetic mesh generation algorithm that deforms a template mesh imposing a user-defined strain to a template mesh. We used this method to create a new.
Complementarily, in order to assess the method's potential to identify clinically relevant and physiologically plausible regional remodelling, we propose a method to generate synthetic datasets through regional induction of circumferential and longitudinal elongation and analyse the sensitivity of the parcellation to both global and regional remodelling. To assess the performance, and robustness to noise, in a real setting, we also do an inter-and intra-observer reproducibility analysis, as well as a test/retest comparison of two sequential acquisitions, on the same patient. This technique enables both a regional analysis of anatomy, using the end-diastole (ED) volumes, as well as function, via regional ejection fraction (EF).
While this parcellation method has the advantage of being fully automatic and therefore completely reproducible under the same image and segmentation, the method still relies in the generated 3D model thus depending on the original segmentation quality and stability. To avoid errors due to a bad point-to-point correspondence, our parcellation does not use explicit registration and only uses the geodesic distances from anatomic landmarks that can be clearly identified in 3D echocardiography: the apex, tricuspid and pulmonary valve. The method is independent of the triangulation of the ventricular surface. We applied our method to the analysis of 3D models of the RV generated from 3D echocardiography images using Tomtec software (4D RV-FUNCTION ), but it can be easily adapted to other processing platforms and imaging modalities.
Tomtec software provides a semi-automatic model-based segmentation of the RV, and a tracking during the full cardiac cycle. Figure  1  shows the full process used to parcellate the RV, which is described below. This paper has been acccepted for publication in Medical Image Analysis. Please find the final version at: https://doi. org/10. 1016/j. media. 2021. 102044 2 METHODOLOGY The first step of our parcellation is the identification of the valves and apex using the pointto-point correspondence provided by the semiautomatic segmentation software in the endocardial surface mesh (given that these correspond to stable anatomical landmarks in the mesh that can be identified in the image). Next, for each node of the mesh, we compute the geodesic distances to the apex, pulmonary valve and tricuspid valve.
Data acquisition 3D echocardiographic images of 10 male healthy American football players without cardiovascular disease were acquired in a modified apical 4 chambers view using an EPIQ7 ultrasound system This paper has been acccepted for publication in Medical Image Analysis. Please find the final version at: https://doi. org/10. 1016/j. media. 2021. 102044 3 EXPERIMENTAL SETTING (Philips Medical Systems, Andover, MA, USA) equipped with a 1 to 5 MHz transthoracic matrix array transducer (X5-1). For each individual, 4-6 different ECG-gated subvolumes were acquired in a single breath-hold to be compounded into the full 3D+t images of 2 complete cardiac cycles. A written informed consent form was obtained from all study participants. The demographics of the population can be seen in the supplementary material S1.
Another male subject, a pure control, was imaged in two consecutive acquisitions by two different operators to obtain an estimate of the variability due to the imaging process. The image loops were processed using a clinically validated software (4D RV-FUNCTION Tomtec-Arena TTA2, Tomtec Imaging Systems GmbH, Unterschleissheim, Germany  (Niemann et al. , 2007; Muraru et al. , 2016) ) to segment and track the RV and obtain a 3D-model for each patient. These models were exportable in ucd, a standard file format. All 3D models had the same topology: a triangular watertight mesh with 938 nodes and 1872 faces. The points were in approximate point-to-point correspondence among different individuals. The segmentation pipeline consisted of the following steps: first the clinician segments the RV endocardium contour of the frame corresponding to the R peak using the semiautomatic tool, and the result is tracked during a full cardiac cycle.
Registration and harmonic maps We additionally assessed an alternative to parcellate the RV based on registration and harmonic maps. For the registration approach, we defined the partition in a template mesh, and used a state-of-the-art software (Deformetrica v4. 3 Bône et al. (  2018 )) to map a parcellation defined in a template mesh to the surface of the target mesh. Afterwards, the steps of tetrahedralisation and interpolation of the partition to the blood pool are computed as described in 2. 1. Previous to the non-rigid registration, we aligned the template and the target meshes using the apex and valves positions. For harmonic mapping approach, we used the heat maps with sources in each of the 3 landmarks instead of the geodesic distance. The results of the harmonic mapping experiments are found in supplementary material (S6), and were found to be worse than the geodesic approach.
Reproducibility of the parcellation method The RV parcellation computed by our method is dependent on the RV segmentation, whose variability was assessed in the previous section. Therefore, even if our method is automatic, we need to evaluate the robustness of the method to the segmentation variability that is present in a normal clinical setting. We use the re-requantified dataset to test the inter-and intra-observer reproducibility of the regional volumes and EFs. We report the mean and percent absolute error in volumes and EF. To assess the stability of the parcellations themselves, we compute the stability of the intersection point of the three segments. We compared the reproducibility of our original approach, in which the parcellation was independently computed for each mesh, and a registration-based approach in which the parcellation is first defined in a template mesh, and then transported to each individual using registration.