A Retrospective Analysis of the Clinical Impact of 939 Chest Radiographs Using the Medical Records

Geijer, Mats; Ivarsson, Liz; Göthlin, Jan

doi:10.1155/2012/862198

Cited by 1 publication

(1 citation statement)

References 18 publications

(23 reference statements)

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“…The chest x-ray (CXR) is the most commonly requested imaging test and is critical for the diagnosis of several conditions, including pulmonary infections [1]. The CXR has been found to provide valuable clinical data, often supporting the clinically suspected diagnosis, identifying previously unknown pathology, and influencing the course of clinical management [2]. Given its clinical utility and the insight it provides into the patient's medical state, the CXR report is of value to researchers performing retrospective studies.…”

Section: Introductionmentioning

confidence: 99%

Labelling chest x-ray reports using an open-source NLP and ML tool for text data binary classification

Towfighi

Agarwal

Dyf

et al. 2019

Preprint

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The chest x-ray is a commonly requested diagnostic test on internal medicine wards which can diagnose many acute pathologies needing intervention. We developed a natural language processing (NLP) and machine learning (ML) model to identify the presence of opacities or endotracheal intubation on chest x-rays using only the radiology report. This a preliminary report of our work and findings. Using the General Medicine Inpatient Initiative (GEMINI) dataset, housing inpatient clinical and administrative data from 7 major hospitals, we retrieved 1000 plain film radiology reports which were classified according to 4 labels by an internal medicine resident. NLP/ML models were developed to identify the following on the radiograph reports: the report is that of a chest x-ray, there is definite absence of an opacity, there is definite presence of an opacity, the report is a follow-up report with minimal details in its text, and there is an endotracheal tube in place. Our NLP/ML model development methodology included a random search of either TF-IDF or bag-of-words for vectorization along with random search of various ML models. Our Python programming scripts were made publicly available on GitHub to allow other parties to train models using their own text data. 100 randomly generated ML pipelines were compared using 10-fold cross validation on 75% of the data, while 25% of the data was left out for generalizability testing. With respect to the question of whether a chest x-ray definitely lacks an opacity, the model's performance metrics were accuracy of 0.84, precision of 0.94, recall of 0.81, and receiver operating characteristic area under curve of 0.86. Model performance was worse when trained against a highly imbalanced dataset despite the use of an advanced oversampling technique.

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Section: Introductionmentioning

confidence: 99%