A clinical decision web to predict ICU admission or death for patients hospitalised with Covid-19 using machine learning algorithms

The purpose of the study was to build a predictive model for estimating the risk of ICU admission or mortality among patients hospitalized with COVID-19 and provide a user-friendly tool to assist clinicians in the decision-making process. The study cohort comprised 3623 patients with confirmed COVID-19 who were hospitalized in the SALUD hospital network of Aragon (Spain), which includes 23 hospitals, between February 2020 and January 2021, a period that includes several pandemic waves. Up to 165 variables were analysed, including demographics, comorbidity, chronic drugs, vital signs, and laboratory data. To build the predictive models, different techniques and machine learning (ML) algorithms were explored: multilayer perceptron, random forest, and extreme gradient boosting (XGBoost). A reduction dimensionality procedure was used to minimize the features to 20, ensuring feasible use of the tool in practice. Our model was validated both internally and externally. We also assessed its calibration and provide an analysis of the optimal cut-off points depending on the metric to be optimized. The best performing algorithm was XGBoost. The final model achieved good discrimination for the external validation set (AUC = 0.821, 95% CI 0.787–0.854) and accurate calibration (slope = 1, intercept = −0.12). A cut-off of 0.4 provides a sensitivity and specificity of 0.71 and 0.78, respectively. In conclusion, we built a risk prediction model from a large amount of data from several pandemic waves, which had good calibration and discrimination ability. We also created a user-friendly web application that can aid rapid decision-making in clinical practice

Deep neural network for complex open-water wetland mapping using high-resolution WorldView-3 and airborne LiDAR data

Wetland inventory maps are essential information for the conservation and management of natural wetland areas. The classification framework is crucial for successful mapping of complex wetlands, including the model selection, input variables and training procedures. In this context, deep neural network (DNN) is a powerful technique for remote sensing image classification, but this model application for wetland mapping has not been discussed in the previous literature, especially using commercial WorldView-3 data. This study developed a new framework for wetland mapping using DNN algorithm and WorldView-3 image in the Millrace Flats Wildlife Management Area, Iowa, USA. The study area has several wetlands with a variety of shapes and sizes, and the minimum mapping unit was defined as 20 m2 (0.002 ha). A set of potential variables was derived from WorldView-3 and auxiliary LiDAR data, and a feature selection procedure using principal components analysis (PCA) was used to identify the most important variables for wetland classification. Furthermore, traditional machine learning methods (support vector machine, random forest and k-nearest neighbor) were also implemented for the comparison of results. In general, the results show that DNN achieved satisfactory results in the study area (overall accuracy = 93.33 %), and we observed a high spatial overlap between reference and classified wetland polygons (Jaccard index ∼0.8). Our results confirm that PCA-based feature selection was effective in the optimization of DNN performance, and vegetation and textural indices were the most informative variables. In addition, the comparison of results indicated that DNN classification achieved relatively similar accuracies to other methods. The total classification errors vary from 0.104 to 0.111 among the methods, and the overlapped areas between reference and classified polygons range between 87.93 and 93.33 %. Finally, the findings of this study have three main implications. First, the integration of DNN model and WorldView-3 image is useful for wetland mapping at 1.2-m, but DNN results did not outperform other methods in this study area. Second, the feature selection was important for model performance, and the combination of most relevant input parameters contributes to the success of all tested models. Third, the spatial resolution of WorldView-3 is appropriate to preserve the shape and extent of small wetlands, while the application of medium resolution image (30-m) has a negative impact on the accurate delineation of these areas. Since commercial satellite data are becoming more affordable for remote sensing users, this study provides a framework that can be utilized to integrate very high-resolution imagery and deep learning in the classification of complex wetland areas

Exploring open-source multispectral satellite remote sensing as a tool to map long-term evolution of salt marsh shorelines

From an ecological and socio-economic perspective, salt marshes are one of the most valuable natural assets on Earth. As external pressures are causing their extensive degradation and loss globally, the ability to monitor salt marshes on a long-term scale and identify drivers of change is essential for their conservation. Remote sensing has been demonstrated to be one of the most adept methods for this purpose and open-source multispectral satellite remote sensing missions have the potential to provide worldwide long-term time-series coverage that is non-cost-prohibitive. This study derives the long-term lateral evolution of four salt marsh patches in the Ria Formosa coastal lagoon (Portugal) using data from the Sentinel-2 and Landsat missions as well as from aerial photography surveys to quantitatively examine the accuracy and associated uncertainty in using open-source multispectral satellite remote sensing for this purpose. The results show that these open-source satellite archives can be a useful tool for tracking long-term salt marsh extent dynamics. During 1976-2020, there was a net loss of salt marsh in the study area, with erosion rates reaching an average of-3.3 m/yr opposite a tidal inlet. The main source of error in the satellite results was the dataset spatial resolution limits, but the specific salt marsh shoreline environment contributed to the relative magnitude of that error. The study notes the influence of eco-geomorphological dynamics on the mapping of sedimentary environments, so far not extensively discussed in scientific literature, highlighting the difference between mapping a morphological process and a sedimentary environment.info:eu-repo/semantics/publishedVersio

Comparing Deep Learning and Shallow Learning for Large-Scale Wetland Classification in Alberta, Canada

Advances in machine learning have changed many fields of study and it has also drawn attention in a variety of remote sensing applications. In particular, deep convolutional neural networks (CNNs) have proven very useful in fields such as image recognition; however, the use of CNNs in large-scale remote sensing landcover classifications still needs further investigation. We set out to test CNN-based landcover classification against a more conventional XGBoost shallow learning algorithm for mapping a notoriously difficult group of landcover classes, wetland class as defined by the Canadian Wetland Classification System. We developed two wetland inventory style products for a large (397,958 km2) area in the Boreal Forest region of Alberta, Canada, using Sentinel-1, Sentinel-2, and ALOS DEM data acquired in Google Earth Engine. We then tested the accuracy of these two products against three validation data sets (two photo-interpreted and one field). The CNN-generated wetland product proved to be more accurate than the shallow learning XGBoost wetland product by 5%. The overall accuracy of the CNN product was 80.2% with a mean F1-score of 0.58. We believe that CNNs are better able to capture natural complexities within wetland classes, and thus may be very useful for complex landcover classifications. Overall, this CNN framework shows great promise for generating large-scale wetland inventory data and may prove useful for other landcover mapping applications

Satellite Observations and Spatiotemporal Assessment of Salt Marsh /Dieback Along Coastal South Carolina (1990-2019)

Coastal wetland mapping is often difficult because of the heterogeneous vegetation compositions and associated tidal effects. Past studies in the Gulf/Atlantic coast states have reported acute marsh dieback events in which marsh rapidly browned and thinned, leaving stubble of dead stems or mudflad with damaged ecosystem services. Reported marsh dieback in South Carolina (SC), USA, however, have been limited. Previous studies have suggested a suite of possibly abiotic and biotic attributes responsible for salt marsh dieback. However, there are no consensus answers in current literature explaining what led to marsh dieback in past decades, especially from the spatiotemporal perspective. In this study, the U-Net was employed, and an adaptive deep learning approach was developed to map statewide salt marshes in estuarine emergent wetlands of SC from 20 Sentinel-2A&B images. Then all marsh dieback events were identified in the North Inlet-Winyah Bay (NIWB) estuary, SC, from 1990 to 2019. With 30 annually collected Landsat images, the Normalized Difference Vegetation Index (NDVI) series was extracted. A Stacked Denoising Autoencoder neural network was developed to identify the NDVI anomalies on the trajectories. All marsh dieback patches were extracted, and their inter-annual changes were examined. Among these were the five most severe marsh dieback events (1991, 1999, 2000, 2002, and 2013). The spatiotemporal relationships between the dieback series and the associated environmental variables in an intertidal marsh in the estuary were investigated. Daily Evaporative Demand Drought Index (EDDI), daily precipitation data from Parameter Elevation Regressions on Independent Slopes Model (PRISM), and station-based water quality observations (dissolved oxygen, specific conductivity, salinity, turbidity, pH, and temperature) in the estuary were retrieved. This study cogitates the environmental influence on coastal marsh from a spatiotemporal perspective using a long-term satellite time series analysis. The findings could provide insights into marsh ecological resilience and facilitate coastal ecosystem management

Using Machine Learning to Understand the Hydrologic Impacts of Permafrost Thaw-Driven Land Cover Change

Discontinuous permafrost regions are experiencing a change in land cover distribution as a result of permafrost thaw. In wetlands interspersed with discontinuous permafrost, climate change is particularly problematic because temperature increases can result in significant permafrost thaw, thaw-driven landscape changes, and resultant changes in watershed hydrologic responses. The influence of land cover change on the short- and long-term hydrological responses of wetland-peatland complexes is poorly understood. A better understanding of the impacts of climate-related land cover evolution on the hydrology of wetland-covered watersheds requires information about the distribution of hydrologically important lands, their pattern, and the rate at which they change over time. Here, we first developed a machine learning-based land cover evolution model (TSLCM) to estimate the long-term evolution of dominant land covers for application to the discontinuous permafrost regions of Northern Canada. This model is applied to replicate historical land cover and estimate future land cover scenarios at the Scotty Creek Research Basin in the Northwest Territories, Canada. A significant challenge when analyzing land cover change effects on hydrological properties is generating time-dependent classified maps of the region of interest, and the challenges associated with preprocessing remotely sensed data for discriminating between wetlands and forest-covered regions. In this work, we focus on two important objectives supporting the improved classification of wetlands in discontinuous permafrost regions: the exclusive use of only RGB imagery, and the use of an image segmentation method to accelerate the automatic classification of land cover. A semantic segmentation neural network, a multi-layer perceptron (MLP), and watershed function algorithms are applied to develop the taiga wetland identification neural network (TWINN) for the hydrological classification of wetlands. TWINN is here demonstrated to accurately classify high-resolution imagery of discontinuous permafrost regions within the Northwest Territories into the water, forest, and wetlands, and also able to delineate the runoff area of wetlands. To study the effect of land cover evolution on runoff generation in the Scotty Creek basin, the products of TWINN and TSLCM are used to inform a process-based hydrological model where land cover change is represented explicitly. According to simulation results, land cover transitions can modify annual mean streamflow by as much as 7%, in addition to influences due to changing precipitation regimes alone

sUAS and Deep Learning for High-Resolution Monitoring of Tidal Marshes in Coastal South Carolina

Tidal marshes are dynamic environments, now more than ever threatened by both natural and anthropogenic forces. Best practices for monitoring tidal marshes, as well as the environmental factors that affect them, have been studied for more than 40 years. With recent technological advances in remote sensing, new capabilities for monitoring tidal marshes have emerged. One of these new opportunities and challenges is hyper-spatial resolution imagery (\u3c10 \u3ecm) that can be captured by small unmanned aerial systems (sUAS). Aside from enhanced visualization, structure-from-motion (SfM) technology can derive dense point clouds from overlapped sUAS images for high resolution digital elevation models (DEMs). Furthermore, Deep Learning (DL) algorithms, patterned after the brain’s neural networks, provide effective and efficient analysis of mass amounts of pixels in high-resolution images. In this dissertation, I seek to apply these developing geospatial technologies—sUAS and DL—to map, monitor, and model marsh vegetation. First, sUAS and coastal vegetation related literature was extensively reviewed to provide a secure foundation to build upon. Second, an above ground biomass (AGB) model of the tidal marsh vegetation Spartina Alterniflora was developed using high resolution sUAS imagery to assess marsh distribution and healthiness in the estuary. We determined that the best RGB-based index for mapping S. Alterniflora biomass was the Excess Green Index (ExG), and using a quadratic relationship we achieved an R2 of 0.376. Third, with a time series of sUAS missions, tidal marsh wrack was monitored before and after a hurricane event to map and monitor its short- and long-term effects of tidal wrack deposition on vegetation. sUAS proved to be an exceptionally capable tool for this study, revealing that 55% of wrack stayed within 10 m of a water body and wrack may persist for only 3-4 months over the same location after a hurricane event. Finally, deep learning remote sensing techniques were applied to county-wide NAIP aerial imagery to map Land Use/ Land Cover (LULC) changes of Beaufort County, South Carolina from 2009 to 2019, and to assess if and why marsh losses or gains may have occurred around the county from coastal development. We discovered that the DL U-net classifier performed the best (92.4% overall accuracy) and the largest changes in the county have come by way of forest loss for urban growth, which will impact the marshes over time. This dissertation advances the theoretical and application-based use of sUAS and DL to benefit application driven GIScientists and coastal managers in the coastal marsh realm to mitigate future negative impacts and expand our understanding of how we can protect such majestic environments