dataesr/scientific-paragraphs-categorization huggingface dataset:
Comorbidities: cardiac comorbidities, hypertension, liver comorbidities, hypothyroidism, rheumatological comorbidities, neurological comorbidities, diabetes (type 1 or 2), renal comorbidities, pulmonary comorbidities, cancer, and immunode ciency; these were condensed into a single variable which is the sum of the total number of comorbidities. Paramedical exams at admission: Lung X-ray, C-Reactive Protein (CRP), haemoglobin, white cell count, lymphocyte count, creatinine, natraemia, serum potassium, prothrombin time, aspartate transaminase (AST), alanine transaminase (ALT), and lactate dehydrogenase (LDH). For Bayesian modelling, continuous variables were divided into classes of clinical relevance (age, BMI, temperature) or into ve classes (best tested discretisation). The database was separated in two separate datasets at random; the learning dataset that contained 80% of the data was used for the development of the models (Bayesian network and logistic regression) and the validation dataset that contained 20% of the data was used for the internal validation of the model.
The next step consists in estimating conditional probability tables, using the expectation maximisation algorithm. The expectation maximisation algorithm is an iterative procedure to compute the maximum likelihood estimation in the presence of missing or hidden data. In the maximum likelihood estimation, the aim was to estimate the model parameters for which the observed data were the most likely. This step is the so-called "learning step" that allows to develop the Bayesian network using the learning PREDICT-COVID dataset. The software used to create the model was RapidMiner Studio ® version 9. 7 with the W-BayesNet (TAN) Weka 3 ® Machine Learning Group extension.
The software used to create the logistic regression model was RapidMiner Studio ® version 9. 7 Weka 3 ® Machine Learning Group extension. The performance of the optimised developed prediction tool used a 10-fold cross validation to determine mean ± standard deviation (SD) area under the receiver-operating-characteristics (AUC-ROC) curve and accuracy. Performance of the optimised developed model to predict the clinical course of the patients of the validation dataset was investigated using the AUC-ROC curve, sensitivity, speci city, positive predictive value (PPV), and negative predictive value (NPV). The software used was RapidMiner Studio ® version 9. 7 with the W-BayesNet (TAN) Weka 3 ® Machine Learning Group extension. The prediction tool was published online using Netica ® version 5. 19 (Norsys corporation ® ).
Study population A total of 1100 patients on May 20, 2020 were selected. Wrongly selected patients (nosocomial infection, n = 266; under 18 years old, n = 3), and patients whose admission date was unknown (n = 8) were excluded from this analysis; thus, data from 823 patients were analysed. The mean ± SD age of the patients was 70. 6 ± 16. 9 years (range: 19 to 102 years), BMI was 26. 7 ± 5. 4 kg/m² (range: 11 to 46 kg/m²), and the median total number of comorbidities was 2 (range: 2 to 8). The most frequent comorbidity was hypertension (45. 1%), and 23. 4% of the patients were diabetics. The most frequent clinical signs reported at admission were abnormal lung auscultation (68. 0%), fatigue (67. 0%), and cough (66. 7%; Supplementary Table  1 ). There was no signi cant difference between patients with an unfavourable and those with a favourable outcome with regards to sex, age, smoking status, temperature, renal comorbidity, diabetes, lung auscultation, fatigue, cough, shortness of breath, diarrhoea, pain, headache, runny nose, myalgia, coma at admission, lung X-Ray, creatininaemia, C-Reactive-Protein, prothrombin time ratio, white blood cells count, neutrophils to lymphocytes ratio and AST were signi cantly different (Supplementary Table  2 ).
Models to predict the primary composite outcome: need for mechanical ventilation; admission into an ICU because of multiple organ dysfunction syndrome or death within 21 days of admission Using a Bayesian network, variables were rst classi ed according to the variance reduction, (Table  1 ). The 11 retained variables that were the most linked to the primary outcome criteria are listed in Table  2. The 5 most informative variables to predict the primary outcome, in descending order, were: CRP, neutrophil to lymphocyte ratio, AST, shortness of breath, and prothrombin time (Table  1 ). The ten-fold cross validation of the optimised Bayesian model developed using the learning dataset (80% of the patients, n = 658) and the 11 analysed variables provided a mean ± SD AUC-ROC of 0. 76 ± 0. 06 and a mean ± SD error rate of 29.
2 ± 6. 5%. The ten-fold cross-validation of the logistic regression model developed using the learning dataset (80% of the patients, n = 658) and the 11 analysed variables for the primary composite outcome had a mean ± SD AUC-ROC of 0. 76 ± 0. 08 and a mean ± SD error rate of 27. 4 ± 11. 0%. The performance of the developed optimised Bayesian model to predict the primary outcome in the validation dataset (the remaining 20% of the patients, n = 165) had an AUC-ROC of 0. 78, an accuracy of 77. 6%, a sensitivity of 67. 9%, a speci city of 76. 2%, a PPV of 59. 4%, and NPV of 82. 5% (Table  3 ). The structure of the optimised Bayesian network using 11 variables to assess the primary outcome criteria is presented in Fig. 1. Similarly, elevated BMI has also been reported to be a risk factor  [22, 23] , but this was not the case herein.
total mortality captures indirect deaths, such as those related to a healthcare system under crisis, yielding a more complete picture of the pandemic's consequences. The total number of explanatory variables was limited due to the increased mathematical combinatory; and was related to the number of subjects included in the learning dataset. After a classi cation based on variance reduction, somewhat arbitrarily, only the 11 most relevant variables were selected. Thus, using 11 variables, the prediction model offers a good compromise between performance and ergonomics. As Lindsell et al. [24]  point out, our model, which can be used as both a prognostic and predictive model, does not guide the health care team on the effectiveness of treatment, which is more informative than a simple prognosis.
In agreement, 7 of the 11 selected prognostic variables included in the model proposed herein are included among those cited in the other proposed models. Although increasing age is reported as a risk for poor outcomes [8, 9, 20-23], herein patients with an unfavourable outcome were slightly but signi cantly older, but this was not retained in the prediction model presented herein. Performance of the developed optimised Bayesian model (11 variables) to predict the primary outcome of the validation dataset (remaining 20% of the patients, n = 165) Parameters used to evaluate the performance AUC-ROC curve 0. 78 Accuracy 77. 6% Sensitivity 67. 9% Speci city 76. 2% Positive predictive value 59. 4% Negative predictive value 82. 5% AUC-ROC curve:.
Satellite images are becoming increasingly available with a resolution allowing users to explore smaller riverine systems globally. However, with the exception of Landsat, the temporal window covered by satellite data is still too short for historical analysis. Satellite imagery is therefore accurate to characterize processes at an inter-and intra-annual scale, but not yet for detecting channel changes over decades beyond last 30-40 years. For longer channel temporal trajectories, or smaller rivers, satellite records are insufficient. Data can be supplemented by historical map data to extend data records, as used by  Ricaurte et al. (2012)  to compare the contemporary and historical distribution of vegetated islands in sections of the Danube, Rhine and Olt rivers. Complementary field data RS data can be complemented with more traditional field approaches to increase the set of convergent evidence confirming changes in channel morphology and their drivers.
Reach-scale changes Classical approach from airborne images A classic approach to analyse reach-scale channel adjustments over multiple kilometres is to compile historical aerial photographs. Series of photographs are selected at least every 10 years, depending on the availability of archived photos and flood dates, and integrated in a geographic information system (GIS) environment to extract geomorphic variables, e. g. active channel width or sinuosity, gravel bar area  (Gilvear et al. , 2000; Ollero, 2010; Michalková et al. , 2011; Rollet et al. , 2013; Toone et al. , 2014; Arnaud et al. , 2015; Lallias-Tacon et al. , 2017; Scorpio et al. , 2018)  and landscape unit characteristics (e. g. Dufour et al. , 2015; Solins et al. , 2018). Image georeferencing and vectorization of river features from historical datasets are still mostly manual and time-consuming tasks which require real expertise.
, 2011; Gilvear and Bryant, 2016; Serlet et al. , 2018). However, extracting channel change information from archived data (e. g. old aerial photographs) is not straightforward and requires an assessment of error production and propagation to allow its application for quantitative geomorphic analysis  (James et al. , 2012; Bakker and Lane, 2017). For example, it has been demonstrated that structure-from-motion (SfM) data processing of historical aerial photos of braided channels can produce a quality of information equivalent to classical photogrammetric approaches, provided that image texture and overlap are sufficiently high for tie-point detection and matching  (Bakker and Lane, 2017). However, the persistence of systematic centimetre-to decimetre-scale elevation errors after coregistration of point clouds indicates that topographic differencing using SfM processing of archival imagery is still limited for the quantitative analysis of sediment budgets.
The integration of large-scale historical data (beyond RS) is often used to better contextualize reach-scale changes within a catchment and landscape context. For example,  Ziliani and Surian (2016)  combine catchment-scale datasets on river pressures (e. g. bank protection, sediment mining, chronology and location of torrential control works), RS-derived information (land use changes), historical maps and aerial photos to disentangle the contribution of local versus large-scale drivers in the evolutionary trajectory of channel morphology along the nearly-natural Tagliamento River (northwestern Italy).
Observing continuous network changes This second approach has become possible in the last 10 years thanks to a better temporal and spatial resolution in RS data. It relies on the integration of optical, multi-spectral (orthophotos or satellite images) and topographic (LiDAR) data. Macfarlane et al. (2017)  combined Landsat imagery and a modelled estimate of pre-European settlement land cover, and showed, over 50 000 km of rivers, that 62% of Utah rivers and 48% of the Columbia River Basin network exhibited significant differences in riparian vegetation compared to historic conditions due to land-use impacts and flow and disturbance regime changes. Bizzi et al. (2019)  derived in the Piedmont river network (Italy) historical and current hydraulic scaling laws by integrating a recent regional geomorphic database based on remotely sensed datasets  (Demarchi et al.
Modern softcopy SfM photogrammetry packages will often deliver visually stunning results and extremely high data volumes irrespective of the quality of the input data. Since it is increasingly difficult to validate a significant percentage of these outputs with field data, they are too often accepted as good without detailed examination. After the appearance of the first papers on the topic of SfM in 2012/2013, it has taken several years and multiple contributions to recognize that SfM photogrammetry, while still strongly rooted in photogrammetry, requires its own expertise. The best   2011 ) further demonstrated that this doming deformation was due to poor camera calibration due to the exclusive use of nadir imagery. It is now well recognized that for SfM photogrammetry with low-cost cameras the acquisition of off-nadir imagery with convergent views is critical.
g. Spada et al. , 2018 , who combine data from the CORONA, Landsat and Sentinel 2 missions), which are publicly accessible and provide high spatial resolution (10-60 m) images in Europe every 5 days (if no cloud), or weekly or sub-monthly, at the global scale. Over the past few decades, geomorphologists have advocated for an increase in spatial resolution, whereas now some of the geomorphic questions are solved when resolution is reduced (e. g. channel bathymetry from radiometric information). An issue is then to determine the optimal resolution and level of change detection for solving geomorphic questions. In recent years, satellites have increased in spatial resolution (reaching sub-meter scales) and frequency of acquisition (subweekly acquisition), collecting multispectral and radar information and in some cases (such as Pleiades) stereoscopic datasets for topographic/DEM reconstruction.
, 2010; Clerici et al. , 2014; Cunningham et al. , 2018). Fine-scale approaches now extend to the network scale. Michez et al. (2017)  compared rivers of different regions in Belgium based on the ratios of channel width and depth to the basin area. Comprehensive, systematic analyses of the different predictors of fluvial patterns, as well as predictions of future channel evolution (if any of these predictors are altered), may now be achieved at a global level, at least for medium-size rivers, using existing pre-processed, remotely sensed archives and platforms. For instance, the Global Width Database for Large Rivers (GWD-LR) contains channel widths between 60°S and 60°N extracted using the SRTM Water Body Database  (Yamazaki et al. , 2014). Considerable advances may be achieved by using global archives to interrogate or predict.
Both solutions offer comparable precision, accuracy and density of information for unvegetated and exposed terrains (a compilation of precision and accuracy values for airborne LiDAR datasets in gravel-bed rivers is available in  Lallias-Tacon et al. , 2014) , but with LiDAR it is possible to capture the topography of vegetated surfaces, provided that the density of the vegetation cover is not too high (e. g. Charlton et al. , 2003). The most recent advances in LiDAR technology also offer the possibility to combine different LiDAR wavelengths to capture during the same flight the topography of exposed and submerged surfaces of river channels  (Mandlburger et al. , 2015) , which can be a decisive advantage for large river channels. Case studies making use of sequential and distributed high-resolution RS data to reconstruct short-term channel changes are now common in the literature (see recent review from  Vericat et al.
, 2017). Differential topography based on sequential LiDAR or SfM datasets is used to produce distributed maps of erosion and deposition of channel reaches, to use this information to reconstruct sediment budgets, and also to backcalculate bedload transport using the morphological approach  (Passalacqua et al. , 2015; Vericat et al. , 2017; Antoniazza et al. , 2019). The order of magnitude of detectable elevation changes with those data is generally around 10-20 cm, but this depends on the sensor accuracy or flight height as well as the properties of the investigated surfaces. Several studies document the negative effect of vegetation, local slope and surface roughness on the level of detection of topographic change in river channels (e. g. Wheaton et al. , 2010; Milan et al. , 2011; Lallias-Tacon et al.
, 2014). It is also recognized that these data need a careful inspection and correction of systematic errors in spatial positioning or elevation before computing a sediment budget, as this error may have a strong impact on the integrated volumes of sediment erosion and deposition  (Anderson, 2019). Stable areas may be used to evaluate the systematic error, and to coregister the sequential datasets before computing the sediment budget (e. g. Lallias-Tacon et al. , 2014; Passalacqua et al. , 2015; Anderson, 2019). Topographic differencing using high-resolution datasets have been successfully used to investigate a large range of fluvial processes, such as bank erosion  (Thoma et al. , 2005; Jugie et al. , 2018) , braided channel responses to flow events  (Lane et al. , 2003; Milan et al. , 2007; Hicks et al.
MacVicar and  Piégay (2012)  pioneered installing a video camera on the Ain River in France to describe the relation between wood transport and water discharge, and to construct and validate a wood budget for the reach upstream of the camera (Figures  10A  and 10B ). Boivin et al. (2017)  used two video cameras to monitor the passage of wood during floods and icebreakup events in the Saint-Jean River in Canada. As for flood discharge data (Le  Coz et al. , 2016) , web-crowdsourced home movies have been recently used to define and characterize wood-laden flows  (Ravazzolo et al. , 2017; Ruiz-Villanueva et al. , 2019)  (Figure  10C ). Automatic and semi-automatic wood detection procedures have been developed to track and quantify the wood discharge in the images  (Benacchio et al. , 2017) , but the systematic application still requires further research  (Piégay et al.
Catchment-scale models Until a few years ago, catchment-scale models were limited by the lack of suitable datasets, but they are now a flourishing research area that is providing valuable evidence to support the management and planning of river systems. Catchment-scale models have become feasible owing to the availability of DEMs with a high enough resolution to represent river features (e. g. Passalacqua et al. , 2015). The coupling of DEMs with largescale distributed hydrological models  (Van Der Knijff et al. , 2010)  can now be used to characterize sediment and nutrient transport across entire networks  (Jain et al. , 2006; Barker et al. , 2009; Bizzi and Lerner, 2015). This context has fostered the development of sediment models to assess how sediment is routed through a network and how the various sediment sources within the basin generate different sediment connectivity patterns  (Cavalli et al.
, 2013; Heckmann and Schwanghart, 2013; Czuba and Foufoula-Georgiou, 2014; Heckmann et al. , 2015 Heckmann et al. , , 2018;; Parker et al. , 2015; Czuba, 2018). For instance, the CAtchment Sediment Connectivity And DElivery (CASCADE) modelling framework enables a quantitative, spatially explicit analysis of network sediment connectivity with potential applications in both river science and management  (Schmitt et al. , 2016; Figure   12 ). In the Mekong delta, understanding the cumulative effects of constructed and planned dams helps identify new solutions addressing both economic and environmental objectives  (Schmitt et al. , 2018a (Schmitt et al. , , 2018b (Schmitt et al. , , 2019)). Similarly, in the case of instream large wood (i. e. fallen trees, trunks, rootwads and branches), models have been developed to assess wood supply and transfer through catchments using novel datasets  (Ruiz-Villanueva et al.
Second, this use of ground control, long held as an absolute requirement, is currently being challenged (e. g. Carbonneau and Dietrich, 2017; James et al. , 2017). If we look towards the near future, the resolution of Earth observation data from satellites is such that soon it should provide more information to characterize large to mid-sized river features and changes almost continuously in space and time. Mini-satellites provide almost daily images globally at 3-5 m resolution in the RGB and near-infrared bands (see https:// www. planet. com/), and the SWOT satellite will soon observe major lakes, rivers and wetlands with unprecedented resolution. In the next few years, two major programs will supply more frequent images with better quality: Landsat 9, which will be launched in 2020; and Pleiades Neo, which will be composed of four satellites that will revisit the same scene twice daily, producing panchromatic images at 30 cm resolutiona higher spatial resolution than for airborne campaigns done by many national institutions since 1940s.
The increasing global data availability High-resolution topographic and observed hydrological data have only been available for a few years at the global scale and are providing new ways to characterize river characteristics and trajectories. A better understanding of how fluvial systems vary globally will require close integration of geomorphic datasets with a range of hydrologic, climatic, topographic and biological data archives. Hydrologic data have become available for many countries via the Global Runoff Data Centre (GRDC) and the World Meteorological Organization's Hydrological Observing System (WHOS). Crochemore et al. (2019)  provide an analysis of the quality of 21 586 river flow time series from 13 openly accessible hydrological archives. Recent global datasets such as the Global Streamflow Indices and Metadata Archive  (Do et al.
Emerging geoprocessing tools Data are increasingly available from a number of freely and openly accessible repositories. However, to realize the full potential of big data, rapid access and efficient processing capabilities are required  (Giuliani et al. , 2017). With the development of new data and sensors we must also develop our collective ability to manage and analyse these data. The increasing development of 3D information provided by photogrammetry and LiDAR or infra-annual time series of VHR images, for instance, potentially opens many scientific and applied issues related to the interpretation and understanding of riverscape functioning, but also raises the question of the chain of actors involved in data acquisition, processing and utilization. Deriving insights on fluvial characteristics from very large datasets requires computational tools and automation.
There has been a rise in computational hydrology, ecology and geomorphology over the last decade thanks to the uptake of open-source programming languages such as R and Python. For example, hydrologists have developed many packages supporting the entire hydrological 'workflow', including meteorological and hydrological data retrieval via application programming interfaces; data extraction at catchment scales from global gridded data; many different catchment hydrological models; and packages specifically designed for statistical analyses and data visualization  (Slater et al. , 2019b). Many hydrological and ecological packages already exist for automated satellite image processing, handling and manipulation of RS data, correcting and rescaling satellite imagery, or for analysing remotely sensed vegetation data.
Computational fluvial geomorphologists are also increasingly using and developing toolboxes to understand and quantify river landscape change (Figure  14 ; for a recent review see  Fryirs et al. , 2019). For instance, the open-source LSDTopoTools software is used for topographic analysis, channel network extraction, chi analysis, calculation of erosion rates, hilltop flow routing and relief metrics, and/or topographic extraction of floodplains and terraces  (Mudd et al. , 2018). The RiVMAP MATLAB toolbox or the cmgo R package can be used to measure channel widths, the locations and rates of migration, accretion and erosion, and the space-time characteristics of cutoff dynamics  (Golly and Turowski, 2017; Schwenk et al. , 2017). The CASCADE toolbox  (Tangi et al. , 2019)  provides assessment of sediment connectivity at the network scale and enables screening impacts of many infrastructure portfolios.