ArticleScientific reports2021
Machine learning applied to near-infrared spectra for clinical pleural effusion classification.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 19 citations in OpenAlex.
- Hospital Outcome of Host Heterogeneity, Organ dysfunction and Trajectory in sepsis (HOHHOT): A cohort study in the critical care unit.BMJ open · 2025Article
- Machine learning-based Diagnostic model for determining the etiology of pleural effusion using Age, ADA and LDH.Respiratory research · 2025Article
- Article
- Optimal Modeling of Anti-Breast Cancer Candidate Drugs Based on Graph Model Feature Selection.Computational and mathematical methods in medicine · 2022Article
- Biological and cytological-morphological assessment of tuberculous pleural effusions.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologieArticle
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer patients with malignant pleural effusions (MPE) have a particular poor prognosis. It is crucial to distinguish MPE from benign pleural effusion (BPE). The present study aims to develop a rapid, convenient and economical diagnostic method based on FTIR near-infrared spectroscopy (NIRS) combined with machine learning strategy for clinical pleural effusion classification. NIRS spectra were recorded for 47 MPE samples and 35 BPE samples. The sample data were randomly divided into train set (n = 62) and test set (n = 20). Partial least squares, random forest, support vector machine (SVM), and gradient boosting machine models were trained, and subsequent predictive performance were predicted on the test set. Besides the whole spectra used in modeling, selected features using SVM recursive feature elimination algorithm were also investigated in modeling. Among those models, NIRS combined with SVM showed the best predictive performance (accuracy: 1.0, kappa: 1.0, and AUC
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