2000 10 20655 https://socictopen.socict.org/files/original/2798b6101653bd6e04e2534b1a9f7d09.pdf 0d888143e3687583971768da450b5c0f Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Using Early Data to Estimate the Actual Infection Fatality Ratio from COVID-19 in France Creator An entity primarily responsible for making the resource Samuel Soubeyrand, Lionel Roques, Julien Papaïx, Etienne K Klein, Antoine Sar Description An account of the resource The number of screening tests carried out in France and the methodology used to target the patients tested do not allow for a direct computation of the actual number of cases and the infection fatality ratio (IFR). The main objective of this work is to estimate the actual number of people infected with COVID-19 and to deduce the IFR during the observation window in France. We develop a `mechanistic-statistical’ approach coupling a SIR epidemiological model describing the unobserved epidemiological dynamics, a probabilistic model describing the data acquisition process and a statistical inference method. The actual number of infected cases in France is probably higher than the observations: we find here a factor ×8 (95%-CI: 5–12) which leads to an IFR in France of 0.5% (95%-CI: 0.3–0.8) based on hospital death counting data. Adjusting for the number of deaths in nursing homes, we obtain an IFR of 0.8% (95%-CI: 0.45–1.25). This IFR is consistent with previous findings in China (0.66%) and in the UK (0.9%) and lower than the value previously computed on the Diamond Princess cruse ship data (1.3%). Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource Bayesian inference, Case-fatality rate, SIR model, COVID-19, infection fatality ratio, mechanistic-statistical model Identifier An unambiguous reference to the resource within a given context DOI: 10.3390/biology9050097 Source A related resource from which the described resource is derived Biology Publisher An entity responsible for making the resource available MDPI AG Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Biology (General) https://socictopen.socict.org/files/original/50d89fa3a1c87449524bc4980d4ba6cd.pdf cb7292ef5a63ea1ff0203a7ad931b1f2 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Using echocardiography to guide the treatment of novel coronavirus pneumonia Creator An entity primarily responsible for making the resource Qian-Yi Peng, Xiaoting Wang, Li-na Zhang, Chinese Critical Care Ultrasound Study Group (CCUSG) Date A point or period of time associated with an event in the lifecycle of the resource 2020 Identifier An unambiguous reference to the resource within a given context DOI: 10.1186/s13054-020-02856-z Source A related resource from which the described resource is derived Critical Care Publisher An entity responsible for making the resource available BMC Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Medical emergencies. Critical care. Intensive care. First aid Language A language of the resource EN https://socictopen.socict.org/files/original/8b621074546dd894afba05c44da6a335.pdf 77965a330ddc83cfc7629987c96c5758 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Using Geotagged Social Media Data to Explore Sentiment Changes in Tourist Flow: A Spatiotemporal Analytical Framework Creator An entity primarily responsible for making the resource Wei Jiang, Zhengan Xiong, Qin Su, Yi Long, Xiaoqing Song, Peng Sun Description An account of the resource Understanding sentiment changes in tourist flow is critical in designing exciting experiences for tourists and promoting sustainable tourism development. This paper proposes a novel analytical framework to investigate the tourist sentiment changes between different attractions based on geotagged social media data. Our framework mainly focuses on visualizing the detailed sentiment changes of tourists and exploring the valuable spatiotemporal pattern of the sentiment changes in tourist flow. The tourists were first identified from social media users. Then, we accurately evaluated the tourist sentiment by constructing a Chinese sentiment dictionary, grammatical rule, and sentiment score. Based on the location information of social media data, we built and visualized the tourist flow network. Last, to further reveal the impact of attractions on the sentiment of tourist flow, the positive and negative sentiment profiles were generated by mining social media texts. We took Beijing, a famous tourist destination in China, as a case study. Our results revealed the following: (1) the temporal trend of tourist sentiment has seasonal characteristics and is significantly influenced by government control policies against COVID-19; (2) due to the impact of the attraction’s historical background, some tourist flows with highly decreased sentiment strength are linked to attractions; (3) on the long journey to the attraction, the sentiment strength of tourists decreases; and (4) bad traffic conditions can significantly decrease tourist sentiment. This study highlights the methodological implications of visualizing sentiment changes during collective tourist movement and provides comprehensive insight into the spatiotemporal pattern of tourist sentiment. Date A point or period of time associated with an event in the lifecycle of the resource 2021 Subject The topic of the resource Spatiotemporal analysis, geotagged social media data, tourist flow, sentiment change Identifier An unambiguous reference to the resource within a given context 10.3390/ijgi10030135 Source A related resource from which the described resource is derived Epidemiology and Health Publisher An entity responsible for making the resource available Korean Society of Epidemiology Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Geography (General) https://socictopen.socict.org/files/original/14ae35c320817be1d95d815a160b24c4.pdf ae34ff0c28cae37f568b2b95b602d7dd Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images. Creator An entity primarily responsible for making the resource Sheetal Rajpal, Navin Lakhyani, Ayush Kumar Singh, Rishav Kohli, Naveen Kumar Description An account of the resource Coronaviruses are a family of viruses that majorly cause respiratory disorders in humans. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new strain of coronavirus that causes the coronavirus disease 2019 (COVID-19). WHO has identified COVID-19 as a pandemic as it has spread across the globe due to its highly contagious nature. For early diagnosis of COVID-19, the reverse transcription-polymerase chain reaction (RT-PCR) test is commonly done. However, it suffers from a high false-negative rate of up to 67% if the test is done during the first five days of exposure. As an alternative, research on the efficacy of deep learning techniques employed in the identification of COVID-19 disease using chest X-ray images is intensely pursued. As pneumonia and COVID-19 exhibit similar/ overlapping symptoms and affect the human lungs, a distinction between the chest X-ray images of pneumonia patients and COVID-19 patients becomes challenging. In this work, we have modeled the COVID-19 classification problem as a multiclass classification problem involving three classes, namely COVID-19, pneumonia, and normal. We have proposed a novel classification framework which combines a set of handpicked features with those obtained from a deep convolutional neural network. The proposed framework comprises of three modules. In the first module, we exploit the strength of transfer learning using ResNet-50 for training the network on a set of preprocessed images and obtain a vector of 2048 features. In the second module, we construct a pool of frequency and texture based 252 handpicked features that are further reduced to a set of 64 features using PCA. Subsequently, these are passed to a feed forward neural network to obtain a set of 16 features. The third module concatenates the features obtained from first and second modules, and passes them to a dense layer followed by the softmax layer to yield the desired classification model. We have used chest X-ray images of COVID-19 patients from four independent publicly available repositories, in addition to images from the Mendeley and Kaggle Chest X-Ray Datasets for pneumonia and normal cases. To establish the efficacy of the proposed model, 10-fold cross-validation is carried out. The model generated an overall classification accuracy of 0.974 ± 0.02 and a sensitivity of 0.987 ± 0.05, 0.963 ± 0.05, and 0.973 ± 0.04 at 95% confidence interval for COVID-19, normal, and pneumonia classes, respectively. To ensure the effectiveness of the proposed model, it was validated using an independent Chest X-ray cohort and an overall classification accuracy of 0.979 was achieved. Comparison of the proposed framework with state-of-the-art methods reveal that the proposed framework outperforms others in terms of accuracy and sensitivity. Since interpretability of results is crucial in the medical domain, the gradient-based localizations are captured using Gradient-weighted Class Activation Mapping (Grad-CAM). In summary, the results obtained are stable over independent cohorts and interpretable using Grad-CAM localizations that serve as clinical evidence. Date A point or period of time associated with an event in the lifecycle of the resource 2021 Subject The topic of the resource covid-19, machine learning, deep learning, Classification, chest x-rays, Grad-CAM Identifier An unambiguous reference to the resource within a given context 10.1016/j.chaos.2021.110749 Source A related resource from which the described resource is derived Chaos, solitons, and fractals https://socictopen.socict.org/files/original/0b6347f43d3e67e61df1f1acbaa6a369.pdf a1046ac428cbdf3c0d18bf57cb6d83eb Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Using Health Insurance Network Provider Data and Public Data Sets to Identify SARS-CoV-2 Vaccinators in the USA Creator An entity primarily responsible for making the resource John R. Litaker, Naomi Tamez, Wesley Durkalski, Richard Taylor Description An account of the resource Objective: Mass vaccination planning is occurring at all levels of government in advance of regulatory approval and manufacture of a SARS-CoV-2 vaccine for distribution sometime in 2021. We outline a methodology in which both health insurance provider network data and publicly available data sources can be used to identify and plan for SARS-CoV-2 vaccinator capacity at the county level.Methods: Sendero Health Plans, Inc. provider network data, Texas State Board of Pharmacy data, US Census Bureau data, and H1N1 monovalent vaccine data were utilized to identify providers with demonstrated capacity to vaccinate the population in Travis County, Texas to achieve an estimated SARS-CoV-2 herd immunity target of 67%.Results: Within the Sendero network, 2,356 non-pharmacy providers were identified with 788 (33.4%) practicing in primary care and 1,569 (66.6%) practicing as specialists. Of the total, 686 (29.1%) provided at least one immunization between January 1, 2019 and September 30, 2020. There are 300 pharmacies with active licenses in Travis County with 161 (53.7%) classified as community pharmacies. We estimate that 1,707,098 doses of a 2-dose SARS-CoV-2 vaccine series will need to be administered within Travis County, Texas to achieve the estimated 67% herd immunity threshold to disrupt person-to-person transmission of the SARS-CoV-2 virus based on 2020 census data.Conclusion: A community-based health insurance plan can use data from its provider network and public data sources to support the CDC call to action to identify SARS-CoV-2 vaccinators in the community, including physicians, nurse practitioners, physician assistants, and pharmacies in order to provide macro level estimates of SARS-CoV-2 administration and throughput. Date A point or period of time associated with an event in the lifecycle of the resource 2021 Subject The topic of the resource covid-19, SARS-CoV-2, Vaccination, mass vaccination, vaccinators, Sendero Health Plans Identifier An unambiguous reference to the resource within a given context 10.3389/fpubh.2020.616140 Source A related resource from which the described resource is derived Epidemiology and Health Publisher An entity responsible for making the resource available Korean Society of Epidemiology Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Public aspects of medicine https://socictopen.socict.org/files/original/5267ab8fe56e6d446b5ba7e76beea87f.pdf 33a4bc21798de0fc7f66b42dcf0ca9ae Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Agricultura sostenible Description An account of the resource Dominio científico: Agricultura sostenible Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Using High-Resolution Hyperspectral and Thermal Airborne Imagery to Assess Physiological Condition in the Context of Wheat Phenotyping Creator An entity primarily responsible for making the resource Pablo J. Zarco-Tejada, Victoria Gonzalez-Dugo, Pilar Hernandez, Ignacio Solis Description An account of the resource There is a growing need for developing high-throughput tools for crop phenotyping that would increase the rate of genetic improvement. In most cases, the indicators used for this purpose are related with canopy structure (often acquired with RGB cameras and multispectral sensors allowing the calculation of NDVI), but using approaches related with the crop physiology are rare. High-resolution hyperspectral remote sensing imagery provides optical indices related to physiological condition through the quantification of photosynthetic pigment and chlorophyll fluorescence emission. This study demonstrates the use of narrow-band indicators of stress as a potential tool for phenotyping under rainfed conditions using two airborne datasets acquired over a wheat experiment with 150 plots comprising two species and 50 varieties (bread and durum wheat). The flights were performed at the early stem elongation stage and during the milking stage. Physiological measurements made at the time of flights demonstrated that the second flight was made during the terminal stress, known to largely determine final yield under rainfed conditions. The hyperspectral imagery enabled the extraction of thermal, radiance, and reflectance spectra from 260 spectral bands from each plot for the calculation of indices related to photosynthetic pigment absorption in the visible and red-edge regions, the quantification of chlorophyll fluorescence emission, as well as structural indices related to canopy structure. Under the conditions of this study, the structural indices (i.e., NDVI) did not show a good performance at predicting yield, probably because of the large effects of terminal water stress. Thermal indices, indices related to chlorophyll fluorescence (calculated using the FLD method), and carotenoids pigment indices (PRI and CAR) demonstrated to be better suited for screening complex traits such as crop yield. The study concludes that the indicators derived from high-resolution thermal and hyperspectral airborne imagery are efficient tools for field-based phenotyping providing additional information to standard NDVI imagery currently used. Date A point or period of time associated with an event in the lifecycle of the resource 2015 Subject The topic of the resource chlorophyll fluorescence, field-based phenotyping, hyperspectral, thermal imaging, water stress Identifier An unambiguous reference to the resource within a given context 10.3390/rs71013586 Source A related resource from which the described resource is derived Remote Sensing Publisher An entity responsible for making the resource available MDPI AG Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Science Relation A related resource <a href="http://www.mdpi.com/2072-4292/7/10/13586" target="_blank" rel="noreferrer noopener">http://www.mdpi.com/2072-4292/7/10/13586</a> https://socictopen.socict.org/files/original/459849f7db4740b85b420419e497a7a6.pdf 7c034ff12d2f2da782ff8e18ab71f5ef Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Using interactive technologies and distance learning in sustainable education Creator An entity primarily responsible for making the resource Rybakova Anna, Shcheglova Aleksandra, Bogatov Denis, Alieva Liudmila Description An account of the resource This paper focuses on the use of interactive technologies and distance learning in sustainable education. It discusses how remote learning technologies can positively influence students’ learning and entry in sustainable education. The paper looks at the use of distance learning in higher education as a means to help students in the built environment and its use within the education system. It studies and expands the theoretical research on the benefits of distance learning, where the study is remote and there is no personal contact with staff or students, and examines the impact of distance learning on the student’s learning experience. It also proposes and evaluates potential solutions to overcome the barriers to learning in the built environment and create successful virtual learning communities, recognising that such improvements must be reconciled with the primary benefits identified. The paper provides an overview of sustainable distance learning within higher education and discusses the differences between learning outside the structural environment of a profession, what it means for the student’s learning experience and the potential to overcome barriers to distance learning. This is a very timely topic in the times of COVID-10 pandemic. Lockdowns of the economy and social life impacted all spheres of education with schools and universities closed for long periods of time and all teaching moved to online and distance mode. However, coronavirus pandemic also brought the digital surge in the system of education, including the sustainable education. All these innovations might stay after the pandemic and help the education to evolve and to embrace more novel trends and technologies. Date A point or period of time associated with an event in the lifecycle of the resource 2021 Identifier An unambiguous reference to the resource within a given context 10.1051/e3sconf/202125007003 Source A related resource from which the described resource is derived Epidemiology and Health Publisher An entity responsible for making the resource available Korean Society of Epidemiology Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Environmental sciences https://socictopen.socict.org/files/original/37e2bd4d41f75aa4fe0cb3087fb24a18.pdf e2ecda76ad30312686d1464c3d72b143 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Agricultura sostenible Description An account of the resource Dominio científico: Agricultura sostenible Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Using Irrigation Scheduling to Increase Water Productivity of Wheat-Maize Rotation under Climate Change Conditions Calendario de Riego para Aumentar la Productividad del Agua en Rotación Trigo-Maíz en Condiciones de Cambio Climático Creator An entity primarily responsible for making the resource Gamal El Afandi, Fouad A Khalil, Samiha A Ouda Description An account of the resource Irrigation scheduling was used to increase water productivity of wheat (Triticum aestivum L.)-maize (Zea mays L.) rotation under two climate change scenarios. Three wheat varieties and two maize hybrids were planted at in a 2-yr field experiment. CropSyst model was calibrated and validated for the collected field data, then was used to assess the impact of two climate change scenarios (A2 and B2) and three adaptation strategies (early sowing changing, irrigation schedule and the interaction between them) in the year of 2038s. The results revealed that A2 reduced yield more than B2 scenario for both crops. High yield reduction in wheat-maize rotation could be expected under climate change conditions, where wheat and maize yield will be reduced by an average of 41 and 56%, respectively. The most effective adaptation strategy for wheat was sowing 3 wk earlier and irrigation every 21 d, with irrigation water saving and no yield improvement under A2 scenario in both growing seasons. Whereas under B2 scenario yield improvement by 2% occurred with 3% saving in the applied irrigation water in the 1st growing season and in the 2nd growing season yield could improved by 8% with less than 1% increase in the applied irrigation water and higher water productivity. Changing irrigation schedule was an effective adaptation option for maize, where yield improvement could occur under both climate change scenarios in both growing seasons by up to 9% with less than 3% increase in the applied irrigation water and higher water productivity. Date A point or period of time associated with an event in the lifecycle of the resource 2010 Subject The topic of the resource CropSyst, Estrategias de Adaptación, HadCM3, Pan evaporation technique, adaptation strategies, climate change scenarios, escenarios de cambio climático, evaporación de bandeja Source A related resource from which the described resource is derived Chilean Journal of Agricultural Research Publisher An entity responsible for making the resource available Instituto de Investigaciones Agropecuarias, INIA Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Agriculture, Environmental sciences Relation A related resource <a href="http://www.scielo.cl/scielo.php?script=sci_arttext&amp;pid=S0718-58392010000300015" target="_blank" rel="noreferrer noopener">http://www.scielo.cl/scielo.php?script=sci_arttext&amp;pid=S0718-58392010000300015</a> https://socictopen.socict.org/files/original/d18af1caed46ca97051251ea99cd02db.pdf 66b70a3b57a34479bb2cabcfb2c4641e Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Using Machine Learning to Predict ICU Transfer in Hospitalized COVID-19 Patients Creator An entity primarily responsible for making the resource Roopa Kohli-Seth, Robert Freeman, Fu-Yuan Cheng, Himanshu Joshi, Madhu Mazumdar, David L. Reich, Arash Kia, Prem Timsina, Matthew Levin, Pranai Tandon Description An account of the resource Objectives: Approximately 20–30% of patients with COVID-19 require hospitalization, and 5–12% may require critical care in an intensive care unit (ICU). A rapid surge in cases of severe COVID-19 will lead to a corresponding surge in demand for ICU care. Because of constraints on resources, frontline healthcare workers may be unable to provide the frequent monitoring and assessment required for all patients at high risk of clinical deterioration. We developed a machine learning-based risk prioritization tool that predicts ICU transfer within 24 h, seeking to facilitate efficient use of care providers’ efforts and help hospitals plan their flow of operations. Methods: A retrospective cohort was comprised of non-ICU COVID-19 admissions at a large acute care health system between 26 February and 18 April 2020. Time series data, including vital signs, nursing assessments, laboratory data, and electrocardiograms, were used as input variables for training a random forest (RF) model. The cohort was randomly split (70:30) into training and test sets. The RF model was trained using 10-fold cross-validation on the training set, and its predictive performance on the test set was then evaluated. Results: The cohort consisted of 1987 unique patients diagnosed with COVID-19 and admitted to non-ICU units of the hospital. The median time to ICU transfer was 2.45 days from the time of admission. Compared to actual admissions, the tool had 72.8% (95% CI: 63.2–81.1%) sensitivity, 76.3% (95% CI: 74.7–77.9%) specificity, 76.2% (95% CI: 74.6–77.7%) accuracy, and 79.9% (95% CI: 75.2–84.6%) area under the receiver operating characteristics curve. Conclusions: A ML-based prediction model can be used as a screening tool to identify patients at risk of imminent ICU transfer within 24 h. This tool could improve the management of hospital resources and patient-throughput planning, thus delivering more effective care to patients hospitalized with COVID-19. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource random forest, Critical care, intensive care units, supervised machine learning, COVID-19 Identifier An unambiguous reference to the resource within a given context DOI: 10.3390/jcm9061668 Source A related resource from which the described resource is derived Journal of Clinical Medicine Publisher An entity responsible for making the resource available MDPI AG Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Medicine https://socictopen.socict.org/files/original/f41ec40b57b9b61b04012ead9acb54dc.pdf 66b70a3b57a34479bb2cabcfb2c4641e Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Using Machine Learning to Predict ICU Transfer in Hospitalized COVID-19 Patients Creator An entity primarily responsible for making the resource Roopa Kohli-Seth, Fu-Yuan Cheng, Himanshu Joshi, Pranai Tandon, Robert Freeman, David L Reich, Madhu Mazumdar, Matthew Levin, Prem Timsina, Arash Kia Description An account of the resource Objectives: Approximately 20–30% of patients with COVID-19 require hospitalization, and 5–12% may require critical care in an intensive care unit (ICU). A rapid surge in cases of severe COVID-19 will lead to a corresponding surge in demand for ICU care. Because of constraints on resources, frontline healthcare workers may be unable to provide the frequent monitoring and assessment required for all patients at high risk of clinical deterioration. We developed a machine learning-based risk prioritization tool that predicts ICU transfer within 24 h, seeking to facilitate efficient use of care providers’ efforts and help hospitals plan their flow of operations. Methods: A retrospective cohort was comprised of non-ICU COVID-19 admissions at a large acute care health system between 26 February and 18 April 2020. Time series data, including vital signs, nursing assessments, laboratory data, and electrocardiograms, were used as input variables for training a random forest (RF) model. The cohort was randomly split (70:30) into training and test sets. The RF model was trained using 10-fold cross-validation on the training set, and its predictive performance on the test set was then evaluated. Results: The cohort consisted of 1987 unique patients diagnosed with COVID-19 and admitted to non-ICU units of the hospital. The median time to ICU transfer was 2.45 days from the time of admission. Compared to actual admissions, the tool had 72.8% (95% CI: 63.2–81.1%) sensitivity, 76.3% (95% CI: 74.7–77.9%) specificity, 76.2% (95% CI: 74.6–77.7%) accuracy, and 79.9% (95% CI: 75.2–84.6%) area under the receiver operating characteristics curve. Conclusions: A ML-based prediction model can be used as a screening tool to identify patients at risk of imminent ICU transfer within 24 h. This tool could improve the management of hospital resources and patient-throughput planning, thus delivering more effective care to patients hospitalized with COVID-19. Date A point or period of time associated with an event in the lifecycle of the resource 2020 Subject The topic of the resource covid-19, Intensive care units, Critical care, random forest, supervised machine learning Identifier An unambiguous reference to the resource within a given context 10.3390/jcm9061668 Source A related resource from which the described resource is derived Epidemiology and Health Publisher An entity responsible for making the resource available Korean Society of Epidemiology Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Medicine