Transfer learning with chest X-rays for ER patient classification
One of the challenges with urgent evaluation of patients with acute respiratory distress syndrome (ARDS) in the emergency room (ER) is distinguishing between cardiac vs infectious etiologies for their pulmonary findings. We conducted a retrospective study with the collected data of 171 ER patients....
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| Veröffentlicht in: | Scientific reports 2020-12, Vol.10 (1), p.20900-20900, Article 20900 |
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| creator | Stubblefield, Jonathan Hervert, Mitchell Causey, Jason L. Qualls, Jake A. Dong, Wei Cai, Lingrui Fowler, Jennifer Bellis, Emily Walker, Karl Moore, Jason H. Nehring, Sara Huang, Xiuzhen |
| description | One of the challenges with urgent evaluation of patients with acute respiratory distress syndrome (ARDS) in the emergency room (ER) is distinguishing between cardiac vs infectious etiologies for their pulmonary findings. We conducted a retrospective study with the collected data of 171 ER patients. ER patient classification for cardiac and infection causes was evaluated with clinical data and chest X-ray image data. We show that a deep-learning model trained with an external image data set can be used to extract image features and improve the classification accuracy of a data set that does not contain enough image data to train a deep-learning model. An analysis of clinical feature importance was performed to identify the most important clinical features for ER patient classification. The current model is publicly available with an interface at the web link:
http://nbttranslationalresearch.org/
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| doi_str_mv | 10.1038/s41598-020-78060-4 |
| format | Article |
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http://nbttranslationalresearch.org/
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http://nbttranslationalresearch.org/
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We conducted a retrospective study with the collected data of 171 ER patients. ER patient classification for cardiac and infection causes was evaluated with clinical data and chest X-ray image data. We show that a deep-learning model trained with an external image data set can be used to extract image features and improve the classification accuracy of a data set that does not contain enough image data to train a deep-learning model. An analysis of clinical feature importance was performed to identify the most important clinical features for ER patient classification. The current model is publicly available with an interface at the web link:
http://nbttranslationalresearch.org/
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| subjects | 631/114 639/705 692/308 692/699 Chest Classification Deep Learning Disease - classification Emergency medical care Emergency Service, Hospital Heart Humanities and Social Sciences Humans multidisciplinary Multidisciplinary Sciences Patients - classification Radiography, Thoracic Respiratory distress syndrome Respiratory Distress Syndrome - diagnostic imaging Respiratory Distress Syndrome - etiology Retrospective Studies Science Science & Technology Science & Technology - Other Topics Science (multidisciplinary) Transfer learning X-rays |
| title | Transfer learning with chest X-rays for ER patient classification |
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