Corpus ID: 236772317

ricu: R's Interface to Intensive Care Data

  title={ricu: R's Interface to Intensive Care Data},
  author={Nicola Bennett and Drago Plevcko and Ida-Fong Ukor and Nicolai Meinshausen and Peter Buhlmann},
Providing computational infrastructure for handling diverse intensive care unit (ICU) datasets, the R package ricu enables writing dataset-agnostic analysis code, thereby facilitating multi-center training and validation of machine learning models. The package is designed with an emphasis on extensibility both to new datasets as well as clinical data concepts, and currently supports the loading of around 100 patient variables corresponding to a total of 319,402 ICU admissions from 4 data… Expand

Tables from this paper


Open-access MIMIC-II database for intensive care research
A publicly available ICU database, namely Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II), which can support a wide variety of research studies, ranging from the development of clinical decision support algorithms to retrospective clinical studies, is built. Expand
MIMIC-III, a freely accessible critical care database
MIMIC-III (‘Medical Information Mart for Intensive Care’) is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary careExpand
Closing the data loop: An integrated open access analysis platform for the MIMIC database
A new model for collaborative access, exploration, and analyses of the Medical Information Mart for Intensive Care — III (MIMIC III) database for translational clinical research is described, which addresses problems of data integration, preprocessing, normalization, analyses, and visualization. Expand
MIMIC-Extract: a data extraction, preprocessing, and representation pipeline for MIMIC-III
MIMIC-Extract, an open source pipeline for transforming the raw electronic health record data of critical care patients from the publicly-available MIMic-III database into data structures that are directly usable in common time-series prediction pipelines, is presented. Expand
The eICU Collaborative Research Database, a freely available multi-center database for critical care research
The e ICU Collaborative Research Database is described, a multi-center intensive care unit (ICU) database with high granularity data for over 200,000 admissions to ICUs monitored by eICU Programs across the United States. Expand
Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach
InSight, a machine learning classification system that uses multivariable combinations of easily obtained patient data, is an effective tool for predicting sepsis onset and performs well even with randomly missing data. Expand
An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU
Using data available in the ICU in real-time, Artificial Intelligence Sepsis Expert can accurately predict the onset of sepsis in an ICU patient 4–12 hours prior to clinical recognition. Expand
An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection
This work develops a flexible classifier that leverages streaming lab results, vitals, and medications to predict sepsis before it occurs, and substantially outperforms clinical baselines, and improves on a previous related model for detectingsepsis. Expand
Sharing ICU Patient Data Responsibly Under the Society of Critical Care Medicine/European Society of Intensive Care Medicine Joint Data Science Collaboration: The Amsterdam University Medical Centers Database (AmsterdamUMCdb) Example*
The development and release of Amsterdam University Medical Centers Database (AmsterdamUMCdb), the first freely available critical care database in full compliance with privacy laws from both the United States and Europe, is described as an example of the feasibility of sharing complex critical care data. Expand
Early prediction of circulatory failure in the intensive care unit using machine learning
An early-warning system that integrates measurements from multiple organ systems using a high-resolution database with 240 patient-years of data that predicts 90% of circulatory-failure events and provides early identification of patients at risk for circulatory failure with a much lower false-alarm rate than conventional threshold-based systems. Expand