Showing 7 of total 7 results (show query)
cu-dbmi-peds
phoenix:The Phoenix Pediatric Sepsis and Septic Shock Criteria
Implementation of the Phoenix and Phoenix-8 Sepsis Criteria as described in "Development and Validation of the Phoenix Criteria for Pediatric Sepsis and Septic Shock" by Sanchez-Pinto, Bennett, DeWitt, Russell et al. (2024) <doi:10.1001/jama.2024.0196> (Drs. Sanchez-Pinto and Bennett contributed equally to this manuscript; Dr. DeWitt and Mr. Russell contributed equally to the manuscript), "International Consensus Criteria for Pediatric Sepsis and Septic Shock" by Schlapbach, Watson, Sorce, Argent, et al. (2024) <doi:10.1001/jama.2024.0179> (Drs Schlapbach, Watson, Sorce, and Argent contributed equally) and the application note "phoenix: an R package and Python module for calculating the Phoenix pediatric sepsis score and criteria" by DeWitt, Russell, Rebull, Sanchez-Pinto, and Bennett (2024) <doi:10.1093/jamiaopen/ooae066>.
Maintained by Peter DeWitt. Last updated 13 days ago.
pediatricphoenixpythonsepsisseptic-shocksql
31.7 match 3 stars 5.78 score 20 scripts
marksendak
constellation:Identify Event Sequences Using Time Series Joins
Examine any number of time series data frames to identify instances in which various criteria are met within specified time frames. In clinical medicine, these types of events are often called "constellations of signs and symptoms", because a single condition depends on a series of events occurring within a certain amount of time of each other. This package was written to work with any number of time series data frames and is optimized for speed to work well with data frames with millions of rows.
Maintained by Mark Sendak. Last updated 6 years ago.
electronic-health-recordelectronic-health-recordshealthcarepatientstimeseries
7.4 match 6 stars 4.76 score 19 scripts
bupaverse
eventdataR:Event Data Repository
Event dataset repository including both real-life and artificial event logs. They can be used in combination with functionalities provided by the 'bupaR' packages 'edeaR', 'processmapR', etc.
Maintained by Gert Janssenswillen. Last updated 3 years ago.
3.8 match 2 stars 6.62 score 352 scripts 13 dependents
prise6
aVirtualTwins:Adaptation of Virtual Twins Method from Jared Foster
Research of subgroups in random clinical trials with binary outcome and two treatments groups. This is an adaptation of the Jared Foster method (<https://www.ncbi.nlm.nih.gov/pubmed/21815180>).
Maintained by Francois Vieille. Last updated 7 years ago.
5.3 match 4 stars 4.51 score 16 scripts
josie-athens
pubh:A Toolbox for Public Health and Epidemiology
A toolbox for making R functions and capabilities more accessible to students and professionals from Epidemiology and Public Health related disciplines. Includes a function to report coefficients and confidence intervals from models using robust standard errors (when available), functions that expand 'ggplot2' plots and functions relevant for introductory papers in Epidemiology or Public Health. Please note that use of the provided data sets is for educational purposes only.
Maintained by Josie Athens. Last updated 5 months ago.
4.0 match 5 stars 5.73 score 72 scripts
eth-mds
ricu:Intensive Care Unit Data with R
Focused on (but not exclusive to) data sets hosted on PhysioNet (<https://physionet.org>), 'ricu' provides utilities for download, setup and access of intensive care unit (ICU) data sets. In addition to functions for running arbitrary queries against available data sets, a system for defining clinical concepts and encoding their representations in tabular ICU data is presented.
Maintained by Nicolas Bennett. Last updated 10 months ago.
2.0 match 39 stars 5.65 score 77 scripts
cran
SeqDetect:Sequence and Latent Process Detector
Sequence detector in this package contains a specific automaton model that can be used to learn and detect data and process sequences. Automaton model in this package is capable of learning and tracing sequences. Automaton model can be found in Krleža, Vrdoljak, Brčić (2019) <doi:10.1109/ACCESS.2019.2955245>. This research has been partly supported under Competitiveness and Cohesion Operational Programme from the European Regional and Development Fund, as part of the Integrated Anti-Fraud System project no. KK.01.2.1.01.0041. This research has also been partly supported by the European Regional Development Fund under the grant KK.01.1.1.01.0009.
Maintained by Dalibor Krleža. Last updated 5 years ago.
2.0 match 2.00 score 2 scripts