Natural Language Processing to Identify Pulmonary Nodules and Extract Nodule Characteristics From Radiology Reports

Zheng, Chengyi; Huang, Brian; Agazaryan, Andranik A.; Creekmur, Beth; Osuj, Thearis A.; Gould, Michael K.

doi:10.1016/j.chest.2021.05.048

Cited by 28 publications

(17 citation statements)

References 36 publications

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“…However, deep learning models using the NLP technique are scarce and mostly applied to radiological reports. Zheng et al developed an NLP algorithm to identify pulmonary nodules and the associated characteristics with high accuracy ( 18 ). Furthermore, a recent study compared different machine learning NLP methods to classify radiology reports in orthopedic trauma for injuries and found that BERT NLP outperformed traditional machine learning models and rule-based classifiers for Dutch radiology reports in orthopedic trauma ( 19 ).…”

Section: Discussionmentioning

confidence: 99%

Using Text Content From Coronary Catheterization Reports to Predict 5-Year Mortality Among Patients Undergoing Coronary Angiography: A Deep Learning Approach

Lee

Chen

et al. 2022

Front. Cardiovasc. Med.

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BackgroundCurrent predictive models for patients undergoing coronary angiography have complex parameters which limit their clinical application. Coronary catheterization reports that describe coronary lesions and the corresponding interventions provide information of the severity of the coronary artery disease and the completeness of the revascularization. This information is relevant for predicting patient prognosis. However, no predictive model has been constructed using the text content from coronary catheterization reports before.ObjectiveTo develop a deep learning model using text content from coronary catheterization reports to predict 5-year all-cause mortality and 5-year cardiovascular mortality for patients undergoing coronary angiography and to compare the performance of the model to the established clinical scores.MethodThis retrospective cohort study was conducted between January 1, 2006, and December 31, 2015. Patients admitted for coronary angiography were enrolled and followed up until August 2019. The main outcomes were 5-year all-cause mortality and 5-year cardiovascular mortality. In total, 11,576 coronary catheterization reports were collected. BioBERT (bidirectional encoder representations from transformers for biomedical text mining), which is a BERT-based model in the biomedical domain, was utilized to construct the model. The area under the receiver operating characteristic curve (AUC) was used to assess model performance. We also compared our results to the residual SYNTAX (SYNergy between PCI with TAXUS and Cardiac Surgery) score.ResultsThe dataset was divided into the training (60%), validation (20%), and test (20%) sets. The mean age of the patients in each dataset was 65.5 ± 12.1, 65.4 ± 11.2, and 65.6 ± 11.2 years, respectively. A total of 1,411 (12.2%) patients died, and 664 (5.8%) patients died of cardiovascular causes within 5 years after coronary angiography. The best of our models had an AUC of 0.822 (95% CI, 0.790–0.855) for 5-year all-cause mortality, and an AUC of 0.858 (95% CI, 0.816–0.900) for 5-year cardiovascular mortality. We randomly selected 300 patients who underwent percutaneous coronary intervention (PCI), and our model outperformed the residual SYNTAX score in predicting 5-year all-cause mortality (AUC, 0.867 [95% CI, 0.813–0.921] vs. 0.590 [95% CI, 0.503–0.684]) and 5-year cardiovascular mortality (AUC, 0.880 [95% CI, 0.873–0.925] vs. 0.649 [95% CI, 0.535–0.764]), respectively, after PCI among these patients.ConclusionsWe developed a predictive model using text content from coronary catheterization reports to predict the 5-year mortality in patients undergoing coronary angiography. Since interventional cardiologists routinely write reports after procedures, our model can be easily implemented into the clinical setting.

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Section: Discussionmentioning

confidence: 99%

Using Text Content From Coronary Catheterization Reports to Predict 5-Year Mortality Among Patients Undergoing Coronary Angiography: A Deep Learning Approach

Lee

Chen

et al. 2022

Front. Cardiovasc. Med.

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“…Patients aged 18 years of age or older were identified by diagnostic codes from radiology reports and healthcare encounters [21]. The specific diagnostic codes used were International Classification of Diseases, Ninth Revision, Clinical Modification codes 793.11 (solitary pulmonary nodule) and 793.19 ("other non-specific abnormal finding of lung field") [22,23]. Patients with calcified nodules, a diagnosis of cancer within the prior 5 years (excluding non-melanoma skin cancer or prostate cancer), previously identified nodules, had less than 12 months life expectancy (as indicated by chart notes documenting recognition of, but no intention to evaluate, the incidental nodule), a high suspicion for lung metastasis (as indicated in the radiology report), or those in whom long-term follow-up could not be measured (e.g., patient elected to have the nodule managed by a physician outside of the health system) were excluded.…”

Section: Cohort Identification and Patient Selectionmentioning

confidence: 99%

Prevalence and consequences of non-adherence to an evidence-based approach for incidental pulmonary nodules

Wayne

Prescott

2022

PLoS ONE

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Importance Distinguishing benign from malignant pulmonary nodules is challenging. Evidence-based guidelines exist, but their impact on patient-centered outcomes is unknown. Objective To understand if the evaluation of incidental pulmonary nodules that follows an evidence-based management strategy is associated with fewer invasive procedures for benign lesions and/or fewer delays in cancer diagnosis. Design Retrospective cohort study. Setting Large academic medical center. Participants Adults (≥18 years age) with an incidental pulmonary nodule discovered between January 2012 and December 2014. Patients with calcified nodules, prior nodules, prior diagnosis of cancer, high suspicion for pulmonary metastasis, or limited life expectancy were excluded. Exposure Nodule management strategy (pre-specified based on evidence-based practices). Outcome Composite of any invasive procedure for a benign nodule or delay in diagnosis in patients with cancer (>3 month delay once probability of cancer was >15%). Results Of 314 patients that met inclusion criteria, median age was 61, 46.5% were men, and 66.5% had current or former tobacco use. The mean nodule size was 10.3 mm, mean probability of cancer was 11.8%, and 14.3% of nodules were malignant. Evaluation followed an evidence-based strategy in 245 patients (78.0%), and deviated in 69 patients (22%). The composite outcome occurred in 26 (8.3%) patients. Among patients whose nodule evaluation was concordant with an evidence-based evaluation, 6.1% (15/245) experienced the composite outcome versus 15.9% (11/69) of patients with an evaluation that deviated from evidence-based recommendations (P<0.01). Conclusions and relevance At a large academic medical center, more than 1 in 5 patients with an incidental pulmonary nodule underwent evaluation that deviated from evidence-based practice recommendations. Nodule evaluation that deviated from an evidence-based strategy was associated with biopsy of benign lesions and delays in cancer diagnosis, suggesting a need to improve guideline uptake.

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“…Sehingga memiliki data pekerjaan yang akuntabilitas dan transparan. [8][9][10] [11][12][13][14] [15]…”

Section: Kesimpulanunclassified

Implementasi Chatbot Untuk Laporan Bug / Error Reporting Dengan Natural Languange Processing (Studi Kasus : CV. Solusi Prima)

Hendri

2021

processor

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Research at CV. Solusi Prima was done to help implement chatbot on bug report, in order to solve some problems in interaction with client. We use some research method such as observation, interview, and library research. From this research's result there are some problem with working system that is client and staff in terms of report bug, make report which is done separately and manually (via whatsapp). This matter make staff become confused between chat to see report bug and for staff to make report bug for their head manager, and for head manager it's cost a lot of time to recap all of his staff's works in patch bug. Especially if their customer and the staff keep report error numerously, can cause older report bugs not being recorded in system, or some report being recorded but not tidy enough, so the staff will get confused to input report bug one by one, because of that we use this chatbot system based on natural language processing method with messenger application (telegram).Aim to make it easier for staff and client in making bug reports, so no need for staff to make report manually without having to lose previously datas, other than that the data that is display base on realtime so whatever report that is display will automatically broadcasted by system to all company's staffs from the results of the testing, system worked is compatible with what we are planning from the start. With the use of chatbot that have been equipped with natural language processing, make user easier to do reporting without moving their hands from keyboard to mouse too often. All the informations in chatbot will be saved and can be used for doing data analythic, so making chatbot easier to use and making it easy for staff and client to report a bug.

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Natural Language Processing to Identify Pulmonary Nodules and Extract Nodule Characteristics From Radiology Reports

Cited by 28 publications

References 36 publications

Using Text Content From Coronary Catheterization Reports to Predict 5-Year Mortality Among Patients Undergoing Coronary Angiography: A Deep Learning Approach

Using Text Content From Coronary Catheterization Reports to Predict 5-Year Mortality Among Patients Undergoing Coronary Angiography: A Deep Learning Approach

Prevalence and consequences of non-adherence to an evidence-based approach for incidental pulmonary nodules

Implementasi Chatbot Untuk Laporan Bug / Error Reporting Dengan Natural Languange Processing (Studi Kasus : CV. Solusi Prima)

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