ArticleJournal of healthcare informatics research2019
Detection of Surgical Site Infection Utilizing Automated Feature Generation in Clinical Notes.
Article in Journal of healthcare informatics research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Machine Learning-Enhanced Surveillance for Surgical Site Infections in Patients Undergoing Colon Surgery: Model Development and Evaluation Study.JMIR formative research · 2025Article
- Automating surgical procedure extraction for society of surgeons adult cardiac surgery registry using pretrained language models.JAMIA open · 2024Article
- Data-Driven Technologies as Enablers for Value Creation in the Prevention of Surgical Site Infections: a Systematic Review.Journal of healthcare informatics research · 2023Review
- Performance of machine learning algorithms for surgical site infection case detection and prediction: A systematic review and meta-analysis.Annals of medicine and surgery (2012) · 2022Review
- Automated Detection of Periprosthetic Joint Infections and Data Elements Using Natural Language Processing.The Journal of arthroplasty · 2021Article
- Developing a Natural Language Processing tool to identify perinatal self-harm in electronic healthcare records.PloS one · 2021Article
- Effective Analysis of Inpatient Satisfaction: The Random Forest Algorithm.Patient preference and adherence · 2021Article
- Clinical concept extraction: A methodology review.Journal of biomedical informatics · 2020Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Postsurgical complications (PSCs) are known as a deviation from the normal postsurgical course and categorized by severity and treatment requirements. Surgical site infection (SSI) is one of major PSCs and the most common healthcare-associated infection, resulting in increased length of hospital stay and cost. In this work, we proposed an automated way to generate keyword features using sublanguage analysis with heuristics to detect SSI from cohort in clinical notes and evaluated these keywords with medical experts. To further valid our approach, we also applied different machine learning algorithms on cohort using automatically generated keywords. The results showed that our approach was able to identify SSI keywords from clinical narratives and can be used as a foundation to develop an information extraction system or support search-based natural language processing (NLP) approaches by augmenting search queries.
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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.