ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2019
A Machine Learning Approach to Predicting the Stability of Inpatient Lab Test Results.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- DEPLOYR: a technical framework for deploying custom real-time machine learning models into the electronic medical record.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Confidence-based laboratory test reduction recommendation algorithm.BMC medical informatics and decision making · 2023Article
- Targeting repetitive laboratory testing with electronic health records-embedded predictive decision support: A pre-implementation study.Clinical biochemistry · 2023Article
- Probabilistic Prediction of Laboratory Test Information Yield.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Detection of factors affecting kidney function using machine learning methods.Scientific reports · 2022Article
- External Validation of a Laboratory Prediction Algorithm for the Reduction of Unnecessary Labs in the Critical Care Setting.The American journal of medicine · 2022Article
- A deep learning solution to recommend laboratory reduction strategies in ICU.International journal of medical informatics · 2020Article
- Modeling workflows: Identifying the most predictive features in healthcare operational processes.PloS one · 2020Article
- Prevalence and Predictability of Low-Yield Inpatient Laboratory Diagnostic Tests.JAMA network open · 2019Article
- Use of Artificial Intelligence in the Search for New Information Through Routine Laboratory Tests: Systematic Review.JMIR bioinformatics and biotechnologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
A primary focus for reducing waste in healthcare expenditure is identifying and discouraging unnecessary repeat lab tests. A machine learning model which could reliably predict low information lab tests could provide personalized, real-time predictions to discourage over-testing. To this end, we apply six standard machine learning algorithms to six years (2008-2014) of inpatient data from a tertiary academic center, to predict when the next measurement of a lab test is likely to be the "same" as the previous one. Out of 13 common inpatient lab tests selected for this analysis, several are predictably stable in many cases. This points to potential areas where machine learning approaches may identify and prevent unneeded testing before it occurs, and a methodological framework for how these tasks can be accomplished.
Identifiers
31259006PMC6568078What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.