ArticleBMC medical informatics and decision making2015
Using information theory to identify redundancy in common laboratory tests in the intensive care unit.
Article in BMC medical informatics and decision making, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Assigning credit where it is due: an information content score to capture the clinical value of multiplexed assays of variant effect.BMC bioinformatics · 2024Article
- Interventions to improve appropriateness of laboratory testing in the intensive care unit: a narrative review.Annals of intensive care · 2024Review
- Predicting Abnormal Laboratory Blood Test Results in the Intensive Care Unit Using Novel Features Based on Information Theory and Historical Conditional Probability: Observational Study.JMIR medical informatics · 2022Article
- Iatrogenic anaemia and transfusion thresholds in ICU patients with COVID-19 disease at a tertiary care hospital.Transfusion and apheresis science : official journal of the World Apheresis Association : official journal of the European Society for Haemapheresis · 2021Article
- A New Insight Into Missing Data in Intensive Care Unit Patient Profiles: Observational Study.JMIR medical informatics · 2019Article
- An Optimal Policy for Patient Laboratory Tests in Intensive Care Units.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2019Article
- Article
- Measuring agreement between healthcare survey instruments using mutual information.BMC medical informatics and decision making · 2016Article
- An unsupervised learning method to identify reference intervals from a clinical database.Journal of biomedical informatics · 2016Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClinical workflow is infused with large quantities of data, particularly in areas with enhanced monitoring such as the Intensive Care Unit (ICU). Information theory can quantify the expected amounts of total and redundant information contained in a given clinical data type, and as such has the potential to inform clinicians on how to manage the vast volumes of data they are required to analyze in their daily practice. The objective of this proof-of-concept study was to quantify the amounts of redundant information associated with common ICU lab tests.
methodsWe analyzed the information content of 11 laboratory test results from 29,149 adult ICU admissions in the MIMIC II database. Information theory was applied to quantify the expected amount of redundant information both between lab values from the same ICU day, and between consecutive ICU days.
resultsMost lab values showed a decreasing trend over time in the expected amount of novel information they contained. Platelet, blood urea nitrogen (BUN), and creatinine measurements exhibited the most amount of redundant information on days 2 and 3 compared to the previous day. The creatinine-BUN and sodium-chloride pairs had the most redundancy.
conclusionsInformation theory can help identify and discourage unnecessary testing and bloodwork, and can in general be a useful data analytic technique for many medical specialties that deal with information overload.
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