ArticleBMC bioinformatics2018
Automatic infection detection based on electronic medical records.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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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.
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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, 1 synthesis or guideline pooled it.
- Strategies for improving physician documentation in the emergency department: a systematic review.BMC emergency medicine · 2018Pooled it
- Feasibility of Using Digital Clinical Records to Identify Factors Associated with Nursing-Sensitive Outcomes During Hospitalization: A Pilot Study.Nursing reports (Pavia, Italy) · 2026Article
- Psychosis Relapse Prediction Leveraging Electronic Health Records Data and Natural Language Processing Enrichment Methods.Frontiers in psychiatry · 2022Article
- Application of Artificial Intelligence in Community-Based Primary Health Care: Systematic Scoping Review and Critical Appraisal.Journal of medical Internet research · 2021Article
- Prediction of 30-Day Readmission After Stroke Using Machine Learning and Natural Language Processing.Frontiers in neurology · 2021Article
- Consolidation of Clinical Microbiology Laboratories and Introduction of Transformative Technologies.Clinical microbiology reviews · 2020Review
- Acceptance Prediction for Answers on Online Health-care Community.BMC bioinformatics · 2019Article
- A Hybrid Approach for Modeling Type 2 Diabetes Mellitus Progression.Frontiers in genetics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMaking accurate patient care decision, as early as possible, is a constant challenge, especially for physicians in the emergency department. The increasing volumes of electronic medical records (EMRs) open new horizons for automatic diagnosis. In this paper, we propose to use machine learning approaches for automatic infection detection based on EMRs. Five categories of information are utilized for prediction, including personal information, admission note, vital signs, diagnose test results and medical image diagnose.
resultsExperimental results on a newly constructed EMRs dataset from emergency department show that machine learning models can achieve a decent performance for infection detection with area under the receiver operator characteristic curve (AUC) of 0.88. Out of all the five types of information, admission note in text form makes the most contribution with the AUC of 0.87.
conclusionsThis study provides a state-of-the-art EMRs processing system to automatically make medical decisions. It extracts five types of features associated with infection and achieves a decent performance on automatic infection detection based on machine learning models.
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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 Socratic graph.