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4,954 results for “hospitals”
Dataset: The ONE Group Hospitality, Inc. (STKS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Selina Hospitality PLC (SLNA) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Selina Hospitality PLC (SLNAW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: RCI Hospitality Holdings, Inc. (RICK) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Analysing the intra and interregional components of spatial accessibility gravity model to capture the level of equity in the distribution of hospital services: does they influence patient mobility?
<p>aggregated_data_age55+.csv and distance_matrix_age55+.csv have been included in the second version of the dataset as the reference population is limited to resident with 55 years old or more.</p>
Sempa et al. 2019 - A Retrospective audit of treatment outcomes, side-effect profiles and default and mortality rates of HIV/AIDS patients at the antiretroviral clinic at Pretoria Academic Hospital.
<p>The comprehensive demographic and long-term treatment data of the first, consecutive, 963 patients older than 18 years of age who presented for ART at the Tshwane District Hospital in Gauteng, South Africa during 2004 and 2005 were selected for analysis. All patients started ART after 2004 as part of the South African national HIV treatment plan and were treated according to the National Department of Health HIV guidelines (2004) operative at the time, i.e. eligibility for ART was CD4 <200 cells/µL or WHO stage 4 disease regardless of CD4 count. Treatment was initiated using a standardized triple-drug regimen consisting of two nucleoside reverse transcriptase inhibitors, mostly d4T and 3TC, and one non-nucleoside reverse transcriptase inhibitor, either NVP or EFV. CD4 and HIV-1 VL monitoring was performed at treatment initiation ‘baseline’ and then 6-monthly, according to the national protocol. Demographic, anthropometric, clinical, ART and 5-year longitudinal treatment response data were collected. Excluded all second-line ART visits for all patients who were switched to second-line therapy.</p>
Daños en Hospital de Licantén
<p><span>Fotografías con la afectación del Hospital de Licantén tras la inundación. Fotografías tomadas por Gabriela Cortés, Simón Inzunza, Nikole Guerrero, Yvonne Merino y Nicolás Pérez. Fecha de captura: 13 y 14 de julio de 2023 </span></p>
Hospital de campaña Licantén
<p>Fotografías del Hospital de Campaña instalado tras la inundación del hospital de campaña en Licatén, Región del Maule. Fotografías tomadas por Gabriela Cortés, Simón Inzunza, Nikole Guerrero, Yvonee Merino y Nicolás Pérez. </p> <p> </p> <p> </p>
ANALYSIS OF QUALITY OF LIFE AND COMPLIANCE LEVEL OF PULMONARY TB PATIENTS AND BODY MASS INDEX IN POST-TRANSFERENCE TREATMENT ANTI TUBERCULOSIS IN HOSPITAL dr. M HAULUSSY AMBON
<p>Tuberculosis is the most common multi-systemic infection, with various types of manifestation and clinical description, lung is the most common location for developing tuberculosis disease (WHO, 2018). As many as 58% of cases of pulmonary TB occur in 3 (three) countries such as Southeast Asia. Based on profile Service Health Maluku Province, in 2021 number of invention Pulmonary TB cases Service Health (Health Office) of Maluku Province to end in 2019 it reached 6,379 people or by 0.35 percent of total population in the province the largest as 1.8 million people.This research uses a quantitative observational design with a cross sectional approach. With sample use systematic type Purposive sampling of 83 respondents. Data analysis using linear regression. Results test statistics show the most influential variable to level pulmonary tuberculosis treatment is a level indicator compliance with level significance sig 0.000. Based on the results of the Multiple Linear Regression analysis, it shows that with a p-value of 0.00 0 < 0.05, H 1 is accepted, so it is concluded that there is a simultaneous influence quality of life as well as the level of compliance of TB lung sufferers And body mass index on post treatment tb lung at DR M Haulussy Ambon Regional Hospital.</p>
Human contact network analytics and COVID-19 hospital incidence in France
<p>This data set contains COVID-19 hospital incidence, temperature and human mobility and contact data recorded between 2020-03-24 and 2021-03-30 used in the paper:</p> <p>Selinger et al. 2021: Predicting COVID-19 incidence in French hospitals using human contact network analytics. 10.1016/j.ijid.2021.08.029</p> <p>See methods in the article for detailed descriptions and the data curation process.</p> <p> </p> <p><strong>1) cov_mob_tst_national.csv contains national-level data</strong><br> </p> <p>The columns comprise:</p> <p>incid_hosp: hospital admission incidence </p> <p>incid_rea: ICU admission incidence</p> <p>incid_dc: hospital death incidence </p> <p>incid_rad: incidence of those returned home</p> <p>within_departement_colocation_X%: X%-quantile of colocation probabilities with départements</p> <p>between_departement_colocation_X%: X%-quantile of colocation probabilities between départements</p> <p>fb_population_coverage_X%: X%-quantile of ratio of fb_population over census population in département</p> <p>null_links_X%: X%-quantile of null links across départements</p> <p>clustering_X%: X%-quantile of clustering coefficients across départements</p> <p>ricci_X%: X%-quantile of curvature across départements</p> <p>ricci_min_X%: X%-quantile of minimum curvature across départements</p> <p>ricci_mean_X%: X%-quantile of average curvature across départements</p> <p>ricci_max_X%: X%-quantile of maximum curvature across départements</p> <p>strength_X%: X%-quantile of network strengths across départements</p> <p>betweenness_centrality_X%: X%-quantile of betweenness_centrality scores across départements</p> <p>positive_test_ratio_weekly: ratio of weekly cumulated positive tested over weekly cumulated tests</p> <p>retail_and_recreation_percent_change_from_baseline: Google Mobility Reports</p> <p>grocery_and_pharmacy_percent_change_from_baseline: Google Mobility Reports</p> <p>parks_percent_change_from_baseline: Google Mobility Reports </p> <p>transit_stations_percent_change_from_baseline: Google Mobility Reports</p> <p>workplaces_percent_change_from_baseline: Google Mobility Reports</p> <p>residential_percent_change_from_baseline: Google Mobility Reports</p> <p>mean_temperature_X%: X% quantile of mean daily temperatures averaged over the week across départements</p> <p>min_temperature_X%: X% quantile of minimum daily temperatures averaged over the week across départements</p> <p>max_temperature_X%: X% quantile of maximum daily temperatures averaged over the week across départements</p> <p> </p> <p> </p> <p><strong>2) cov_mob_dep.csv contains département-level data</strong></p> <p>The columns comprise:</p> <p>dep: département code</p> <p>incid_hosp: hospital admission incidence </p> <p>incid_rea: ICU admission incidence</p> <p>incid_dc: hospital death incidence </p> <p>incid_rad: incidence of those returned home</p> <p>week: week (matched to colocation data recording usually on Tuesdays)</p> <p>dep_name: name of the département</p> <p>null_links: number of null links</p> <p>betweenness_centrality: betweenness centrality</p> <p>clustering: clustering coefficient</p> <p>strength: network strength</p> <p>ricci_mean: minimum curvature among all edges incident to a département</p> <p>ricci_min: mean curvature across all edges incident to a département </p> <p>ricci_X%: X%-quantile curvature among all edges incident to a département</p> <p>fb_population: number of facebook users </p> <p>facebook_colocation_within_dep: colocation probability within département</p> <p>fb_population_coverage: ratio of fb_population over census population in département</p> <p>facebook_colocation_between_dep_X%: X%-quantile of facebook colocation among all edges incident to the département</p> <p>min_temperature: minimum daily temperature averaged over the week</p> <p>max_temperature: maximum daily temperature averaged over the week</p> <p>mean_temperature: mean daily temperature averaged over the week</p> <p>incid_hosp_Y: incidence of hospital admission from Ynd most colocated département</p> <p>incid_rea_Y: incidence of ICU admission from Ynd most colocated département</p> <p>incid_dc_Y: incidence of hospital deaths from Ynd most colocated département</p> <p>incid_rad_Y: incidence of returned home from Ynd most colocated département</p> <p> </p>
Fig. 3 in Explorations in anatomy: the remains from Royal London Hospital
Fig. 3. – Mona monkey (Cercopithecus mona) skeleton from grave [272]. Photo by Andy Chopping, copyright MOLA.
Fig. 1 in Explorations in anatomy: the remains from Royal London Hospital
Fig. 1. – Map showing the areas of archaeological excavation at the Royal London Hospital. Drawn by Tracy Wellman, copyright MOLA.
Fig. 4 in Explorations in anatomy: the remains from Royal London Hospital
Fig. 4.– Partial Hermann's tortoise (Testudo hermanni) skeleton from grave [231]. Photo by Andy Chopping, copyright MOLA.
The benefit of augmenting open data with clinical data-warehouse EHR for forecasting SARS-CoV-2 hospitalizations in Bordeaux area, France
<p><strong>Objective</strong></p> <p>The aim of this study was to develop an accurate regional forecast algorithm to predict the number of hospitalized patients and to assess the benefit of the Electronic Health Records (EHR) information to perform those predictions. Materials and Methods Aggregated data from SARS-CoV-2 and weather public database and data warehouse of the Bordeaux hospital were extracted from May 16, 2020, to January 17, 2022. The outcomes were the number of hospitalized patients in the Bordeaux Hospital at 7 and 14 days. We compared the performance of different data sources, feature engineering, and machine learning models.</p> <p><strong>Results </strong></p> <p>During the period of 88 weeks, 2561 hospitalizations due to COVID-19 were recorded at the Bordeaux Hospital. The model achieving the best performance was an elastic-net penalized linear regression using all available data with a median relative error at 7 and 14 days of 0.136 [0.063; 0.223] and 0.198 [0.105; 0.302] hospitalizations, respectively. Electronic health records (EHRs) from the hospital data warehouse improved median relative error at 7 and 14 days by 10.9% and 19.8%, respectively. Graphical evaluation showed remaining forecast error was mainly due to delay in slope shift detection.</p> <p><strong>Discussion </strong></p> <p>Forecast models showed overall good performance both at 7 and 14 days which was improved by the addition of the data from Bordeaux Hospital data warehouse.</p> <p><strong>Conclusions </strong></p> <p>The development of hospital data warehouses might help to get more specific and faster information than traditional surveillance systems, which in turn will help to improve epidemic forecasting at a larger and finer scale.</p>
The effect of Vitamin D levels on the course of COVID-19 in hospitalized patients – a 1-year prospective cohort study
<p>Background: The aim of the current study was to assess the patients with COVID-19 and the impact of vitamin D supplementation on the course of COVID-19.<br> Methods: This prospective cohort study included patients hospitalized due to COVID-19 between December 2020 and December 2021. Patients' demographic, clinical, and laboratory parameters were analysed. <br> Results: 301 participants were enrolled in the study. 46 (15,3%) had moderate, and 162 (53,8%) had severe COVID-19. 14 (4,7%) patients died, and 30 (10,0%) were admitted to the ICU due to disease worsening. The majority needed oxygen therapy (n=224; 74,4%). Average vitamin 25(OH)D3 levels were below optimal at the admittance, and vitamin D deficiency was detected in 205 individuals. More male patients were suffering from vitamin D deficiency. Patients with the more severe disease showed lower levels of vitamin 25(OH)D3 in their blood. The most severe group of patients had more symptoms that lasted significantly longer with progressing disease severity. This group of patients also suffered from more deaths, ICU admissions, and treatments with dexamethasone, remdesivir, and oxygen.<br> Conclusion: Patients with the severe course of COVID-19 were shown to have increased inflammatory parameters, increased mortality, and higher incidence of vitamin D deficiency. The results suggest that the vitamin D deficiency might represent a significant risk factor for a severe course of COVID-19.</p> <p> </p>
Python codes for deconstructing the effects of stochasticity on transmission of hospital-acquired infections in ICUs
<p>The inherent stochasticity in transmission of hospital-acquired infections (HAIs) has complicated our understanding of transmission pathways. It is particularly difficult to detect the impact of changes in the environment on the acquisition rate due to stochasticity. In this study, we investigated the impact of uncertainty (epistemic and aleatory) on nosocomial transmission of HAIs by evaluating the effects of stochasticity on the detectability of seasonality on admission. For doing so, we developed an agent-based model of an ICU and simulated the acquisition of HAIs considering the uncertainties in the behavior of the healthcare workers (HCWs) and transmission of pathogens between patients, HCWs, and the environment. Our results show that stochasticity in HAI transmission weakens our ability to detect the effects of a change, such as seasonality, on the acquisition rate, particularly when transmission is a low-probability event. In addition, our findings demonstrate that data compilation can address this issue, while the amount of required data depends on the size of the said change and the amount of stochasticity. Our methodology can be used as a framework to assess the impact of interventions and provide decision-makers with insight about the minimum required size and target of interventions in a healthcare facility.</p>
Coursera - Cybersecurity in Healthcare (Hospitals & Care Centres)
<p><strong>Coursera - Cybersecurity in Healthcare (Hospitals & Care Centres)</strong></p> <p>M. Jofre</p> <p>The course "Cybersecurity in Healthcare" has been developed to raise awareness and understanding the role of cybersecurity in healthcare (e.g., hospitals, care centres, clinics, other medical or social care institutions and service organisations) and the challenges that surround it. In this course, we will cover both theoretical and practical aspects of cybersecurity. We look at both social aspects as technical aspects that come into play. Furthermore, we offer helpful resources that cover different aspects of cybersecurity. Even if you are not active in the healthcare domain, you will find helpful tips and insights to deal with cybersecurity challenges within any other organisation or in personal contexts as well.</p> <p>This course begins by introducing the opportunities and challenges that digitalisation of healthcare services has created. It explains how the rise of technologies and proliferation of (medical) data has become an attractive target to cybercriminals, which is essential in understanding why adequate cybersecurity measures are critical within the healthcare environment. In later modules, course contents cover the threats, both inside and outside of healthcare organisations like e.g. social engineering and hacking. Module 4 on Cyber Hygiene describes how to improve cybersecurity within healthcare organisations in practical ways. Module 5 looks deeper into how organisational culture affects cybersecurity, the cybersecurity culture, focusing on the interaction between human behaviour and technology and how organisational factors can boost or diminish the level and attention to cybersecurity in healthcare.</p> <p>References:</p> <p>[1] M. Jofre, “Holistic View Of Healthcare Cybersecurity Ecosystem,” Zenodo, Jul. 2020. doi: 10.5281/zenodo.7999970.<br> [2] M. Jofre, “Minimum Quality Standard For Cybersecurity Training In Healthcare,” Zenodo, Jun. 2020. doi: 10.5281/zenodo.8000029.<br> [3] M. Jofre et al., “Cybersecurity and Privacy Risk Assessment of Point-of-Care Systems in Healthcare—A Use Case Approach,” Appl. Sci., vol. 11, no. 15, Art. no. 15, Jan. 2021, doi: 10.3390/app11156699.</p>
Data Hospital Beds, Physicians, Nurses and Expenditure for 20 Latin American Countries from 1960 to 2022
<p>Long-term quantitative series for 20 Latin American countries, spanning from 1960 to 2020, on the number of hospital beds, physicians, nurses and healthcare expenditure.</p> <p>Matus-Lopez, M. and Fernández Pérez, P. 2023. "Transformations in Latin American Healthcare: A Retrospective Analysis of Hospital Beds, Medical Doctors, and Nurses from 1960 to 2022". <em>Journal of Evolutionary Studies in Business</em>.</p> <p>The information was extracted from official reports and cross-country databases. Official reports were available in digital format in the Institutional Repository for Information Sharing (IRIS) of Pan American Health Organization (PAHO). They were summary of four-year reports on Health Conditions in the Americas (PAHO 1962, 1966, 1970, 1974, 1978, 1982, 1986, 1990, 1994, 1998, 2002a), annual reports of Basic Indicators (PAHO 2002b, 2007, 2008, 2010, 2013), Health in South America (PAHO 2012) and Core Indicators (PAHO 2016). Databases were Open Data Portal of the Pan American Health Organization (PLISA) (PAHO 2023), Core Indicator Database provided directly by PAHO (PAHO 2022), Data Portal of National Health Workforce Accounts of the World Health Organization (NHWA) (WHO 2022), and the Global Health Expenditure Database of the World Health Organization (GHED) (WHO 2023).</p> <p>Serie 1. Hospital Beds per 1,000 inhabitants</p> <p>Serie 2. Physicians per 10,000 inhabitants</p> <p>Serie 3. Nurses per 10,000 inhabitants</p> <p>Serie 4. Government spending on health, per capita. Constant US dollars of 2020</p> <p>Cite as:</p> <p> </p> <p> </p>
Dataset and R script: a longitudinal hospital study of febrile children in Ghana
<p>This is the dataset and R script accompanying the manuscript "Fever in focus: symptoms, diagnoses, and treatment of febrile children in Ghana, a longitudinal hospital study."</p>
Video Versus Conversational Contraceptive Counseling During Maternity Hospitalization
ClinicalTrials.gov study NCT03400449. IPD Sharing: NO. Countries: 1. Publications: 1.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.