This is a preprocessed dataset derived from patient records in MIMIC-III and eICU, two large-scale electronic health record (EHR) databases. It contains features and labels for 5 prediction tasks involving 3 adverse outcomes (prediction times listed in parentheses): in-hospital mortality (48h), acute respiratory failure (4h and 12h), and shock (4h and 12h). We extracted comprehensive, high-dimensional feature representations (up to ~8,000 features) using FIDDLE (FlexIble Data-Driven pipeLinE), an open-source preprocessing pipeline for structured clinical data. These 5 prediction tasks were designed in consultation with a critical care physician for their clinical importance, and were used as part of the proof-of-concept experiments in the original paper to demonstrate FIDDLE's utility in aiding the feature engineering step of machine learning model development. The intent of this release is to share preprocessed MIMIC-III and eICU datasets used in the experiments to support and enable reproducible machine learning research on EHR data.

The eICU Collaborative Research Database is a large multi-center critical care database made available by Philips Healthcare in partnership with the MIT Laboratory for Computational Physiology.

The global Electronic Intensive Care Unit (eICU) market is experiencing robust growth, driven by the increasing prevalence of chronic diseases, the rising demand for improved patient care, and the escalating need for efficient resource allocation in healthcare settings. The market's expansion is further fueled by technological advancements in remote monitoring capabilities, data analytics, and artificial intelligence (AI) integration within eICU systems. These innovations enable proactive identification of patient deterioration, facilitating timely interventions and potentially improving patient outcomes. The adoption of eICU solutions is particularly strong in developed regions like North America and Europe, where advanced healthcare infrastructure and a higher concentration of specialized medical facilities are prevalent. However, growth is also anticipated in emerging markets as healthcare systems modernize and seek cost-effective solutions to enhance the quality of intensive care. While high initial investment costs and the need for specialized training can act as potential restraints, the long-term benefits in terms of reduced hospital readmissions, improved mortality rates, and enhanced operational efficiency are proving compelling for healthcare providers. This is leading to significant investments in eICU infrastructure and service expansion. The competitive landscape is characterized by a mix of established players and emerging companies. Major vendors like GE Healthcare and Philips leverage their extensive experience in medical devices and technology to offer comprehensive eICU solutions. Meanwhile, smaller, specialized companies focus on providing innovative software and AI-powered analytics platforms to enhance the functionality and efficiency of eICU systems. Strategic partnerships and mergers and acquisitions are becoming increasingly common, as companies seek to expand their market reach and integrate complementary technologies. The future trajectory of the eICU market points towards greater integration of telehealth capabilities, advanced analytics, and AI-driven decision support systems, further transforming intensive care delivery and pushing the boundaries of remote patient monitoring. This will ultimately lead to improved patient care, reduced healthcare costs, and more efficient utilization of resources within the healthcare system.

IntroductionSepsis associated thrombocytopenia (SAT) is a common complication of sepsis. We designed this study to investigate factors influencing SAT.MethodsPatients with sepsis (2984 in Peking union medical college hospital [PUMCH] database, 13165 in eICU Collaborative Research [eICU] database, 11101 in Medical Information Mart for Intensive Care IV [MIMIC-IV] database) were enrolled. Variables included basic information, comorbidities, and organ functions. Multi-variable logistic regression models and artificial neural network model were applied to determine the factors related to SAT.Main resultsAge and body mass index (BMI) were inversely correlated with the incidence of SAT (p-value 0.175 and 0.049 [PUMCH], p-value 0.000 and 0.000 [eICU], p-value 0.000 and 0.000 [MIMIC-IV]). Hematologic malignancies and other malignancies were positively correlated with the incidence of SAT (p-value 0.000 and 0.000 [PUMCH], p-value 0.000 and 0.000 [eICU], p-value 0.000 and 0.020 [MIMIC-IV]) except other malignancies was inversely correlated with the incidence of SAT in PUMCH database. Norepinephrine (NE) equivalents, total bilirubin (TBIL) and creatinine were positively correlated with the incidence of SAT (p-value 0.000, 0.000 and 0.011 [PUMCH], p-value 0.028, 0.000 and 0.013 [eICU], p-value 0.028, 0.000 and 0.027 [MIMIC-IV]). PaO2 / FiO2 was inversely correlated with the incidence of SAT in PUMCH database (p-value 0.021 [PUMCH]), while it was positively correlated with the incidence of SAT (p-value 0.000 [MIMIC-IV]). PaO2 / FiO2 and SAT was not related (p-value 0.111 [eICU]). TBIL, hematologic malignancies, PaO2 / FiO2 and NE equivalents ranked in the top five significant variables in all three datasets.ConclusionsHematologic malignancies and other malignancies were positively correlated with the incidence of SAT. NE equivalents, TBIL and creatinine were positively correlated with the incidence of SAT. TBIL, hematologic malignancies, PaO2 / FiO2 and NE equivalents ranked in the top significant variables in factors influencing SAT.

and the eICU

Pulmonary embolism (PE) is associated with acute kidney injury (AKI). This study aimed to develop a nomogram to predict AKI in PE patients admitted to the intensive care unit. The data of patients with PE were obtained from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) and the eICU Collaborative Research Database, with AKI as the primary outcome. Patients from MIMIC-IV were divided into training (80%) and internal validation (20%) cohorts, and external validation was performed using the eICU. Independent risk factors for AKI were identified using univariable logistic regression and stepwise logistic regression. A nomogram was constructed based on the stepwise analysis. Its performance was evaluated using the receiver operating characteristic (ROC) area under the curve (AUC), calibration plots, decision curve analysis (DCA), and sensitivity analysis, and compared to the simplified acute physiology score (SAPS) II score. Six independent risk factors for AKI were identified. The nomogram’s AUC was 0.717 in the training cohort, 0.758 in the internal validation cohort, and 0.889 in the external validation cohort. The AUC of the nomogram was higher than the SAPS II score (p 

Objective: Congestive heart failure (CHF) is a clinical syndrome in which heart disease progresses to a severe stage. Risk assessment and early diagnosis of death in patients with CHF are critical to patient prognosis and treatment. The purpose of this study was to establish a nomogram predicting in-hospital death for CHF patients in the ICU. Design: A retrospective observational cohort study. Setting and participants: The data of study from 30,411 CHF patients in the Medical Information Mart for Intensive Care (MIMIC-IV) database and the eICU Collaborative Research Database (eICU-CRD). Primary outcome: In-hospital mortality. Results: The inclusion criteria were met by 15983 subjects, whose in-hospital mortality rate was 12.4%. Multivariate analysis determined that the independent risk factors were age, race, norepinephrine, dopamine, phenylephrine, vasopressin, mechanical ventilation, intubation, HepF, heart rate, respiratory rate, temperature, SBP, AG, BUN, creatinine, chloride, MCV, ...

The Electronic Intensive Care Unit (eICU) market has gained remarkable traction in the healthcare sector, owing to the increasing demand for advanced monitoring solutions that ensure patient safety and enhance care delivery in intensive care units (ICUs). eICUs leverage telemedicine technology to provide remote surv

Number of patients and records in four tasks.

Decompensation risk prediction in eICU.

Background: It remains unclear whether the mean vancomycin trough concentration (VTC) derived from the entire course of therapy is of potential benefit for critically ill patients. This study was conducted to explore the association between mean serum VTC and mortality in intensive care units (ICUs).Methods: 3,603 adult patients with two or more VTC records after receiving vancomycin treatment in the eICU Collaborative Research Database were included in this multicenter retrospective cohort study. Mean VTC was estimated using all measured VTCs and investigated as a continuous and categorical variable. Patients were categorised into four groups according to mean VTC: 20 mg/L. Multivariable logistic regression and subgroup analyses were performed to investigate the relationship of mean VTC with mortality.Results: After adjusting for a series of covariates, logistic regression analyses indicated that mean VTC, as a continuous variable, was positively correlated with ICU (odds ratio, 1.038, 95% confidence interval, [1.014–1.063]) and hospital (1.025 [1.005–1.046]) mortalities. As a categorical variable, mean VTC of 10–15 mg/L was not associated with reduced ICU (1.705 [0.975–2.981]) and hospital (1.235 [0.829–1.841]) mortalities. Mean VTC of 15–20 mg/L was not correlated with a lower risk of hospital mortality (1.370 [0.924–2.029]). Moreover, mean VTCs of 15–20 and >20 mg/L were significantly associated with higher ICU mortality (1.924 [1.111–3.332]; 2.428 [1.385–4.258]), and mean VTC of >20 mg/L with higher hospital mortality (1.585 [1.053–2.387]) than mean VTC of

Summary of the eICU dataset.

Pulse oximeters measure peripheral arterial oxygen saturation (SpO2) noninvasively, while the gold standard involves arterial blood gas measurement (SaO2). There are known racial and ethnic disparities in their performance. BOLD is a new comprehensive dataset that aims to underscore the importance of addressing biases in pulse oximetry accuracy, particularly affecting people with darker skin tones. The dataset was created by harmonizing three Electronic Health Record databases (MIMIC-III, MIMIC-IV, eICU-CRD) comprising Intensive Care Unit stays. Paired SpO2 and SaO2 measurements were time-aligned and combined with various other clinical parameters to provide a detailed picture of each patient. It includes 49,099 such paired measurements within a 5-minute window, with oxygen saturation levels between 70-100%. Significantly, about 25% of the data represents minority racial and ethnic groups, a proportion seldom achieved in previous studies. The code scripts have been made publicly available to facilitate replication.We hope that BOLD is poised to be a valuable resource for health equity studies, that can be used to develop pulse oximetry debiasing algorithms.

The Tele Intensive Care Unit (tICU) market is experiencing robust growth, projected to reach a market size of $4.18 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 15.03% from 2025 to 2033. This expansion is driven by several key factors. The increasing prevalence of chronic diseases requiring intensive care, coupled with the rising demand for cost-effective and accessible healthcare solutions, is significantly fueling market growth. Technological advancements in remote monitoring devices, high-speed internet connectivity, and sophisticated data analytics capabilities are enabling efficient and high-quality tICU services, further boosting market adoption. The shift towards value-based care models, emphasizing improved patient outcomes at reduced costs, also plays a crucial role, as tICU offers a viable solution for optimizing resource utilization and enhancing patient care across geographical locations. Furthermore, the growing preference for remote patient monitoring, particularly among aging populations, and the increasing adoption of telehealth solutions across healthcare systems are contributing to the market's upward trajectory. Segmentation analysis reveals that the hardware component, encompassing devices, communication lines, and computer systems, constitutes a major share of the market. The intensivist-managed tICU segment currently dominates in terms of type of management, driven by the need for specialized expertise in critical care. However, the co-managed and open models are gaining traction due to their cost-effectiveness and flexibility. Geographically, North America currently holds a significant market share owing to advanced healthcare infrastructure and early adoption of telemedicine technologies. However, Asia-Pacific is expected to witness substantial growth in the coming years due to increasing healthcare investments and rising awareness regarding the benefits of tICU. Competition in the market is intense, with key players including Hicuity Health, iMDsoft, and Teladoc Health continuously innovating and expanding their service offerings to cater to the growing demand. Recent developments include: February 2023: CLEW, a provider of real-time AI analytics platforms for healthcare providers, launched the CLEW ICU Conversion and Accelerator program. The program comprises packaged integrations to EMR, monitoring, AV equipment, CLEW's FDA-cleared AI predictive models, and the CLEW ICU workflow platform. Additionally, within 12 weeks, the program's consulting and implementation resources enable US health systems to successfully transition away from the Philips eICU software.January 2023: Google Cloud, eGovernment Foundation, and the Directorate of Health and Family Welfare Government of Nagaland launched the TeleICU hub at Naga Hospital Authority.. Key drivers for this market are: Increase in Volume of Surgical Procedures Worldwide, Increasing Demand for Remote Patient Monitoring. Potential restraints include: Increase in Volume of Surgical Procedures Worldwide, Increasing Demand for Remote Patient Monitoring. Notable trends are: Software and Services Segment is Expected to Witness Growth Over the Forecast Period.

Components:Hardware: Telemedicine devices, cameras, sensorsSoftware: Telemonitoring platforms, communication toolsType of Management:IntensivistOpenCo-managedOpen with ConsultantOther Types of Management Recent developments include: In the year 2019, one of the leading market players, Royal Philips introduced a telemedicine intensive care unit that is designed to enhance the medical treatments offered to high-risk patients. The qualities of the services and the real-time connection have enabled the caretakers to monitor real-time and enhance the medical services very effectively., In the year 2019, one of the leading market players, Philips had launched an eICU program that operates on the tele-ICU software and posses e manager facility as well..

BackgroundIn the ICU, patients with acute myocardial infarction and cardiogenic shock (AMI-CS) often face high mortality rates, making timely and precise mortality risk prediction crucial for clinical decision-making. Despite existing models, machine learning algorithms hold the potential for improved predictive accuracy.MethodsIn this study, a predictive model was developed using the MIMIC-IV database, with external validation performed on the eICU-CRD database. We included ICU patients diagnosed with AMI-CS. Feature selection was conducted using the Boruta algorithm, followed by the construction and comparison of four machine learning models: Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Gaussian Naive Bayes (GNB). Model performance was evaluated based on metrics such as AUC (Area Under the Curve), accuracy, sensitivity, specificity, and so on. The SHAP method was employed to visualize and interpret the importance of model features. Finally, we constructed an online prediction model and conducted external validation in the eICU-CRD database.ResultsIn this study, a total of 570 and 391 patients with AMI-CS were included from the MIMIC-IV and eICU-CRD databases, respectively. Among all machine learning algorithms evaluated, LR exhibited the best performance with a validation set AUC of 0.841(XGBoost: 0.835, AdaBoost: 0.839, GNB: 0.826). The model incorporated five variables: prothrombin time, blood urea nitrogen, age, beta-blockers and Angiotensin-Converting Enzyme Inhibitors or Angiotensin II Receptor Blockers. SHAP plots are employed to visualize the importance of model features and to interpret the results. An online prediction tool was developed, externally validated with the eICU-CRD database, achieving an AUC of 0.755.ConclusionEmploying the LR algorithm, we developed a predictive model for assessing the mortality risk among AMI-CS patients in the ICU setting. Through model predictions, this facilitates early detection of high-risk individuals, ensures judicious allocation of healthcare resources.

(Num. and Cat. indicate presence of numerical and categorical variables respectively. Repn. indicates representation of categorical variables, either One Hot Encoding (OHE) or embedding (EMB)).

The use of the Sequential Organ Failure Assessment (SOFA) score, originally developed to describe disease morbidity, is commonly used to predict in-hospital mortality. During the COVID-19 pandemic, many protocols for crisis standards of care used the SOFA score to select patients to be deprioritized due to a low likelihood of survival. A prior study found that age outperformed the SOFA score for mortality prediction in patients with COVID-19, but was limited to a small cohort of intensive care unit (ICU) patients and did not address whether their findings were unique to patients with COVID-19. Moreover, it is not known how well these measures perform across races. In this retrospective study, we compare the performance of age and SOFA score in predicting in-hospital mortality across two cohorts: a cohort of 2,648 consecutive adult patients diagnosed with COVID-19 who were admitted to a large academic health system in the northeastern United States over a 4-month period in 2020 and a cohort of 75,601 patients admitted to one of 335 ICUs in the eICU database between 2014 and 2015. We used age and the maximum SOFA score as predictor variables in separate univariate logistic regression models for in-hospital mortality and calculated area under the receiver operator characteristic curves (AU-ROCs) and area under precision-recall curves (AU-PRCs) for each predictor in both cohorts. Among the COVID-19 cohort, age (AU-ROC 0.795, 95% CI 0.762, 0.828) had a significantly better discrimination than SOFA score (AU-ROC 0.679, 95% CI 0.638, 0.721) for mortality prediction. Conversely, age (AU-ROC 0.628 95% CI 0.608, 0.628) underperformed compared to SOFA score (AU-ROC 0.735, 95% CI 0.726, 0.745) in non-COVID-19 ICU patients in the eICU database. There was no difference between Black and White COVID-19 patients in performance of either age or SOFA Score. Our findings bring into question the utility of SOFA score-based resource allocation in COVID-19 crisis standards of care.

NSW Rural Fire Service spatial data supply (periodic ETL) to Emergency Service Agencies. Contains fire history and hazard reduction advice plus updates to administrative RFS Boundaries and stations. Suitable for use by Emergency Information Coordination Unit - EICU.

RFS Stations

RFS Brigades

RFS Districts and Zones

BFMC Boundaries

RFS Area

Fire Area Districts

NSW Fire History

Bush Fire Prone Land

Contains archived (fire set to out) current fire year data. With increasing Guardian application use check with RFS that all HR across all agencies are captured

Metadata

Additional file 2.