Considerations for advancing nephrology research and practice through natural language processing.

Sharidan K Parr, Glenn T Gobbel

Abstract readComment

In one paragraph

Article in Kidney international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it

0cells of the map it votes in

2citing papers in PubMed

–field-weighted citation impact

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

2 citing papers in PubMed.

Using Natural Language Processing and Machine Learning to classify the status of kidney allograft in Electronic Medical Records written in Spanish.PloS one · 2025
Article
Performance and clinical utility of a new supervised machine-learning pipeline in detecting rare ciliopathy patients based on deep phenotyping from electronic health records and semantic similarity.Orphanet journal of rare diseases · 2024
Article

4 · The record

Corrections and comments

Comment on
Natural language processing of electronic health records is superior to billing codes to identify symptom burden in hemodialysis patients.2020

5 · Who and what money

Authors and funding

2 authors.

Sharidan K ParrDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA. Electronic address: sharidan.k.parr@vumc.org.

Glenn T GobbelDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA; Veteran Affairs Tennessee Valley Healthcare System, Nashville, Tennessee, USA; Division of General Internal Medicine and Public Health, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Much of medical data is buried in the free text of clinical notes and not captured by structured data, such as administrative codes. Natural language processing (NLP) can locate and use information that resides in unstructured free text. Chan et al. demonstrate that NLP is sensitive for identifying symptoms in hemodialysis patients. These findings highlight the benefit NLP may bring to nephrology and should prompt discussion of important considerations for NLP system design and implementation.

Indexed as

Natural Language ProcessingNephrologyElectronic Health RecordsHumansRenal Dialysis

Identifiers

PMID31980076

DOI10.1016/j.kint.2019.12.001

What OpenQuestion holds

Textmetadata

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.