Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Data Reporting and Recording01:24

Data Reporting and Recording

5.5K
Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
5.5K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

45.1K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
45.1K
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

38.4K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
38.4K
Data Validation01:15

Data Validation

2.1K
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
2.1K
Data Validation01:03

Data Validation

6.9K
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
6.9K
Data Collection II01:29

Data Collection II

10.1K
The nursing history captures and records the patient's health status, so that a care plan evolves to meet the patient's individual needs. The nursing health history is a part of the initial assessment. A comprehensive history covers all health dimensions and plays a significant role in the assessment process. A comprehensive history includes the patient's biographical information, reasons for seeking health care, expectations, present and past health history, medications, and...
10.1K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Early Nephrology Consultation and Acute Kidney Injury in Hospitalized Patients: A Randomized Clinical Trial.

JAMA network open·2026
Same author

The Phoenix criteria for paediatric sepsis: 2 years on.

The Lancet. Child & adolescent health·2026
Same author

Predicting Intensive Care Readmission Among Hospitalized Children.

medRxiv : the preprint server for health sciences·2026
Same author

Identifying a Refractory Shock Phenotype in Pediatric Sepsis Using a Vasoactive-Adjusted Shock Index.

Shock (Augusta, Ga.)·2026
Same author

External Validation, Recalibration, and Extension of a Prediction Model of Early Acute Kidney Injury in Critically Ill Children Using Multicenter Data.

Critical care explorations·2026
Same author

External Validation, Molecular Signatures, and Therapeutic Relevance of Pediatric Sepsis-Associated Acute Kidney Injury Subphenotypes.

Critical care medicine·2026

Related Experiment Video

Updated: Feb 10, 2026

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
13:44

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

4.5K

Big Data and Data Science in Critical Care.

L Nelson Sanchez-Pinto1, Yuan Luo2, Matthew M Churpek3

  • 1Department of Pediatrics (Critical Care), Northwestern University Feinberg School of Medicine, Chicago, IL; Department of Preventive Medicine (Health and Biomedical Informatics), Northwestern University Feinberg School of Medicine, Chicago, IL.

Chest
|May 13, 2018
PubMed
Summary
This summary is machine-generated.

Data science in critical care utilizes complex data for better patient care. While promising, successful implementation of data-driven systems in the ICU remains a challenge.

Keywords:
big datacritical caredata sciencemachine learningprediction models

More Related Videos

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.3K
Analysis of SEC-SAXS data via EFA deconvolution and Scatter
10:59

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Published on: January 28, 2021

9.9K

Related Experiment Videos

Last Updated: Feb 10, 2026

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
13:44

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

4.5K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.3K
Analysis of SEC-SAXS data via EFA deconvolution and Scatter
10:59

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Published on: January 28, 2021

9.9K

Area of Science:

  • Critical care medicine
  • Data science
  • Health informatics

Background:

  • Digitalization has led to vast amounts of clinical big data.
  • Data science offers principled knowledge extraction from complex data.
  • The intensive care unit (ICU) is a prime area for data science due to data volume and complexity.

Purpose of the Study:

  • To review the definitions, algorithms, applications, challenges, and future of big data and data science in critical care.
  • To familiarize intensivists with the opportunities and challenges in this rapidly growing field.

Main Methods:

  • Literature review of data science in critical care.
  • Analysis of existing studies and publications.
  • Discussion of data science definitions, algorithms, applications, and challenges.

Main Results:

  • Data science is highly relevant to critical care due to large datasets and complex patient needs.
  • Few data science projects have successfully translated into implemented ICU systems to date.
  • Intensivists need to understand the potential and hurdles of big data and data science.

Conclusions:

  • Data science holds significant promise for advancing critical care.
  • Overcoming implementation challenges is crucial for realizing the benefits of data-driven systems in the ICU.
  • Continued exploration and understanding of data science are essential for the future of critical care.