Diagnostic accuracy of adenosine deaminase for pleural tuberculosis in a low prevalence setting: A machine learning approach within a 7-year prospective multi-center study.
Alberto Garcia-ZamalloaDiego VicenteRafael ArnayArantzazu ArrospideJorge TaboadaIván Castilla RodríguezUrko Aguirre LarracoecheaNekane MúgicaLadislao AldamaBorja AguinagaldeMontserrat JimenezEdurne BikuñaMiren Begoña BasauriMarta AlonsoEmilio Perez-Tralleronull nullPublished in: PloS one (2021)
The level of adenosine deaminase in pleural fluid together with cell count, other routine biochemical parameters and age, combined with a machine-learning approach, is suitable for the diagnosis of pleural tuberculosis in a low prevalence scenario. Secondly, non-tuberculous effusions that are suspected to be malignant may also be identified with adequate accuracy.
Keyphrases
- machine learning
- mycobacterium tuberculosis
- risk factors
- artificial intelligence
- pulmonary tuberculosis
- hiv aids
- big data
- single cell
- protein kinase
- cell therapy
- pulmonary embolism
- clinical practice
- emergency department
- mesenchymal stem cells
- adverse drug
- human immunodeficiency virus
- hiv infected
- antiretroviral therapy