Evidence map›Paper›PMID 36405250›Full record

ArticleInformatics in medicine unlocked2022

Medication based machine learning to identify subpopulations of pediatric hemodialysis patients in an electronic health record database.

Autumn M McKnite, Kathleen M Job, Raoul Nelson, Catherine M T Sherwin, Kevin M Watt, Simon C Brewer

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Informatics in medicine unlocked, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 45% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Autumn M McKniteDepartment of Pharmacology and Toxicology, College of Pharmacy, University of Utah, Salt Lake City, Ut, USA.
Kathleen M JobDivision of Clinical Pharmacology, Department of Pediatrics, The University of Utah, Salt Lake City, UT, USA.
Raoul NelsonDivision of Pediatric Nephrology, Department of Pediatrics, University of Utah, Salt Lake City, UT, USA.
Catherine M T SherwinDepartment of Pharmacotherapy, College of Pharmacy, University of Utah, Salt Lake City, UT, USA.
Kevin M WattDivision of Clinical Pharmacology, Department of Pediatrics, The University of Utah, Salt Lake City, UT, USA.
Simon C BrewerDepartment of Geography, University of Utah, Salt Lake City, UT, USA.
University of Utah · USWright State University · US

Funding

Physiologically-Based Pharmacokinetic Approach to Determine Dosing on Extracorporeal Life SupportR01HD097775 · NICHD · UNIVERSITY OF UTAH · PI WATT, KEVIN M · 2019 to 2023
$2.7M
Investigation of Drug-Drug and Drug-Circuit Interactions in Children on Continuous Renal Replacement TherapyF31DK130542 · NIDDK · UNIVERSITY OF UTAH · PI MCKNITE, AUTUMN · 2022 to 2023
$74k
NICHD NIH HHS R01 HD097775NIDDK NIH HHS F31 DK130542
6 · The paper itself

Abstract

Electronic health records (EHRs) have given rise to large and complex databases of medical information that have the potential to become powerful tools for clinical research. However, differences in coding systems and the detail and accuracy of the information within EHRs can vary across institutions. This makes it challenging to identify subpopulations of patients and limits the widespread use of multi-institutional databases. In this study, we leveraged machine learning to identify patterns in medication usage among hospitalized pediatric patients receiving renal replacement therapy and created a predictive model that successfully differentiated between intermittent (iHD) and continuous renal replacement therapy (CRRT) hemodialysis patients. We trained six machine learning algorithms (logistical regression, Naïve Bayes,

Indexed as

Electronic health recordsHemodialysisMachine learningMedicationsPediatrics

Identifiers

PMID36405250
PMCPMC9674326
OpenAlexW4302363550

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.