Evidence map›Paper›PMID 37695448›Full record

ArticleJournal of clinical monitoring and computing2024

Improving case duration accuracy of orthopedic surgery using bidirectional encoder representations from Transformers (BERT) on Radiology Reports.

William Zhong, Phil Y Yao, Sri Harsha Boppana, Fernanda V Pacheco, Brenton S Alexander, Sierra Simpson, Rodney A Gabriel

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

Article in Journal of clinical monitoring and computing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
2.9field-weighted citation impact, top 8% 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.

  1. Pooled it
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  3. Article
  4. Review
  5. Review
  6. Article
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

7 authors at 1 institution in 1 country.

William ZhongDivision of Perioperative Informatics, Department of Anesthesiology, University of California, La Jolla, San Diego, CA, USA.
Phil Y YaoDivision of Perioperative Informatics, Department of Anesthesiology, University of California, La Jolla, San Diego, CA, USA.
Sri Harsha BoppanaDivision of Perioperative Informatics, Department of Anesthesiology, University of California, La Jolla, San Diego, CA, USA.
Fernanda V PachecoSchool of Medicine, University of California, La Jolla, San Diego, CA, USA.
Brenton S AlexanderDivision of Perioperative Informatics, Department of Anesthesiology, University of California, La Jolla, San Diego, CA, USA.
Sierra SimpsonDivision of Perioperative Informatics, Department of Anesthesiology, University of California, La Jolla, San Diego, CA, USA.
Rodney A GabrielDivision of Perioperative Informatics, Department of Anesthesiology, University of California, La Jolla, San Diego, CA, USA. ragabriel@health.ucsd.edu.
University of California, San Diego · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeA major source of inefficiency in the operating room is the mismatch between scheduled versus actual surgical time. The purpose of this study was to demonstrate a proof-of-concept study for predicting case duration by applying natural language processing (NLP) and machine learning that interpret radiology reports for patients undergoing radius fracture repair.

methodsLogistic regression, random forest, and feedforward neural networks were tested without NLP and with bag-of-words. Another NLP method tested used feedforward neural networks and Bidirectional Encoder Representations from Transformers specifically pre-trained on clinical notes (ClinicalBERT). A total of 201 cases were included. The data were split into 70% training and 30% test sets. The average root mean squared error (RMSE) were calculated (and 95% confidence interval [CI]) from 10-fold cross-validation on the training set. The models were then tested on the test set to determine proportion of times surgical cases would have scheduled accurately if ClinicalBERT was implemented versus historic averages.

resultsThe average RMSE was lowest using feedforward neural networks using outputs from ClinicalBERT (25.6 min, 95% CI: 21.5-29.7), which was significantly (P < 0.001) lower than the baseline model (39.3 min, 95% CI: 30.9-47.7). Using the feedforward neural network and ClinicalBERT on the test set, the percentage of accurately predicted cases, which was defined by the actual surgical duration within 15% of the predicted surgical duration, increased from 26.8 to 58.9% (P < 0.001).

conclusionThis proof-of-concept study demonstrated the successful application of NLP and machine leaning to extract features from unstructured clinical data resulting in improved prediction accuracy for surgical case duration.

Indexed as

Orthopedic ProceduresRadiologyHumansMachine LearningNeural Networks, ComputerOperating RoomsMachine learningNatural language processingOperating roomOrthopedic surgery

Identifiers

PMID37695448
PMCPMC10879219
OpenAlexW4386592384

What OpenQuestion holds

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LicenceCC BY
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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.