Evidence map›Paper›PMID 31834905›Full record

ArticlePloS one2019

Predicting the occurrence of surgical site infections using text mining and machine learning.

Daniel A da Silva, Carla S Ten Caten, Rodrigo P Dos Santos, Flavio S Fogliatto, Juliana Hsuan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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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

5 authors.

Daniel A da SilvaIndustrial Engineering Department, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
Carla S Ten CatenIndustrial Engineering Department, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
Rodrigo P Dos SantosHospital de Clinicas de Porto Alegre, Porto Alegre, Brazil.
Flavio S FogliattoIndustrial Engineering Department, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.ORCID 0000-0002-0323-8060
Juliana HsuanCopenhagen Business School, Copenhagen, Denmark.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study we propose the use of text mining and machine learning methods to predict and detect Surgical Site Infections (SSIs) using textual descriptions of surgeries and post-operative patients' records, mined from the database of a high complexity University hospital. SSIs are among the most common adverse events experienced by hospitalized patients; preventing such events is fundamental to ensure patients' safety. Knowledge on SSI occurrence rates may also be useful in preventing future episodes. We analyzed 15,479 surgery descriptions and post-operative records testing different preprocessing strategies and the following machine learning algorithms: Linear SVC, Logistic Regression, Multinomial Naive Bayes, Nearest Centroid, Random Forest, Stochastic Gradient Descent, and Support Vector Classification (SVC). For prediction purposes, the best result was obtained using the Stochastic Gradient Descent method (79.7% ROC-AUC); for detection, Logistic Regression yielded the best performance (80.6% ROC-AUC).

Indexed as

AlgorithmsBayes TheoremMachine LearningDatabases, FactualData MiningFemaleHumansMaleMedical RecordsMiddle AgedPredictive Value of TestsSurgical Wound Infection

Identifiers

PMID31834905
PMCPMC6910696

What OpenQuestion holds

Textmetadata
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.