scholarly journals A Highly Generalizable Natural Language Processing Algorithm for the Diagnosis of Pulmonary Embolism from Radiology Reports

2020 ◽  
Author(s):  
Jacob Johnson ◽  
Grace Qiu ◽  
Christine Lamoureux ◽  
Jennifer Ngo ◽  
Lawrence Ngo
Keyword(s):  
Pulmonary Embolism ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Sample Size ◽  
Language Processing ◽  
High Accuracy ◽  
Free Text ◽  
Radiology Reports ◽  
Natural Language Processing Algorithm

AbstractThough sophisticated algorithms have been developed for the classification of free-text radiology reports for pulmonary embolism (PE), their overall generalizability remains unvalidated given limitations in sample size and data homogeneity. We developed and validated a highly generalizable deep-learning based NLP algorithm for this purpose with data sourced from over 2,000 hospital sites and 500 radiologists. The algorithm achieved an AUCROC of 0.995 on chest angiography studies and 0.994 on non-angiography studies for the presence or absence of PE. The high accuracy achieved on this large and heterogeneous dataset allows for the possibility of application in large multi-center radiology practices as well as for deployment at novel sites without significant degradation in performance.

scholarly journals Development of a Natural Language Processing Algorithm for the Classification of Suspicious Liver Lesions from Radiology Reports

2021 ◽  
Author(s):  
Jacob Johnson ◽  
Kaneel Senevirathne ◽  
Lawrence Ngo
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Processing Algorithm ◽  
Liver Lesions ◽  
Radiology Reports ◽  
Natural Language Processing Algorithm

Here, we developed and validated a highly generalizable natural language processing algorithm based on deep-learning. Our algorithm was trained and tested on a highly diverse dataset from over 2,000 hospital sites and 500 radiologists. The resulting algorithm achieved an AUROC of 0.96 for the presence or absence of liver lesions while achieving a specificity of 0.99 and a sensitivity of 0.6.

scholarly journals Automate incidental findings in radiology reports using natural language processing and machine learning to identify and classify lung nodules.

2019 ◽  
Vol 5 (suppl) ◽  
pp. 49-49
Author(s):  
Christi French ◽  
Dax Kurbegov ◽  
David R. Spigel ◽  
Maciek Makowski ◽  
Samantha Terker ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Pulmonary Nodule ◽  
Incidental Findings ◽  
Free Text ◽  
Radiology Reports

49 Background: Pulmonary nodule incidental findings challenge providers to balance resource efficiency and high clinical quality. Incidental findings tend to be under evaluated with studies reporting appropriate follow-up rates as low as 29%. The efficient identification of patients with high risk nodules is foundational to ensuring appropriate follow-up and requires the clinical reading and classification of radiology reports. We tested the feasibility of automating this process with natural language processing (NLP) and machine learning (ML). Methods: In cooperation with Sarah Cannon, the Cancer Institute of HCA Healthcare, we conducted a series of experiments on 8,879 free-text, narrative CT radiology reports. A representative sample of health system ED, IP, and OP reports dated from Dec 2015 - April 2017 were divided into a development set for model training and validation, and a test set to evaluate model performance. A “Nodule Model” was trained to detect the reported presence of a pulmonary nodule and a rules-based “Size Model” was developed to extract the size of the nodule in mms. Reports were bucketed into three prediction groups: ≥ 6 mm, <6 mm, and no size indicated. Nodules were placed in a queue for follow-up if the nodule was predicted ≥ 6 mm, or if the nodule had no size indicated and the report contained the word “mass.” The Fleischner Society Guidelines and clinical review informed these definitions. Results: Precision and recall metrics were calculated for multiple model thresholds. A threshold was selected based on the validation set calculations and a success criterion of 90% queue precision was selected to minimize false positives. On the test dataset, the F1 measure of the entire pipeline was 72.9%, recall was 60.3%, and queue precision was 90.2%, exceeding success criteria. Conclusions: The experiments demonstrate the feasibility of technology to automate the detection and classification of pulmonary nodule incidental findings in radiology reports. This approach promises to improve healthcare quality by increasing the rate of appropriate lung nodule incidental finding follow-up and treatment without excessive labor or risking overutilization.

Senti-BAS: A BERT-based model with sentiment computing for happiness research (Preprint)

2021 ◽  
Author(s):  
Zeyuan Zeng ◽  
Yijia Zhang ◽  
Liang Yang ◽  
Hongfei Lin
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
High Accuracy ◽  
Language Models ◽  
Fine Grained ◽  
Label Information ◽  
Common Criterion ◽  
Text Content ◽  
Sentiment Computing

BACKGROUND Happiness becomes a rising topic that we all care about recently. It can be described in various forms. For the text content, it is an interesting subject that we can do research on happiness by utilizing natural language processing (NLP) methods. OBJECTIVE As an abstract and complicated emotion, there is no common criterion to measure and describe happiness. Therefore, researchers are creating different models to study and measure happiness. METHODS In this paper, we present a deep-learning based model called Senti-BAS (BERT embedded Bi-LSTM with self-Attention mechanism along with the Sentiment computing). RESULTS Given a sentence that describes how a person felt happiness recently, the model can classify the happiness scenario in the sentence with two topics: was it controlled by the author (label ‘agency’), and was it involving other people (label ‘social’). Besides language models, we employ the label information through sentiment computing based on lexicon. CONCLUSIONS The model performs with a high accuracy on both ‘agency’ and ‘social’ labels, and we also make comparisons with several popular embedding models like Elmo, GPT. Depending on our work, we can study the happiness at a more fine-grained level.

scholarly journals Automating Stroke Data Extraction From Free-Text Radiology Reports Using Natural Language Processing: Instrument Validation Study (Preprint)

2020 ◽  
Author(s):  
Amy Y X Yu ◽  
Zhongyu A Liu ◽  
Chloe Pou-Prom ◽  
Kaitlyn Lopes ◽  
Moira K Kapral ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Predictive Value ◽  
Large Vessel ◽  
Free Text ◽  
Imaging Data ◽  
Large Vessel Occlusion ◽  
Vessel Occlusion ◽  
Radiology Reports

BACKGROUND Diagnostic neurovascular imaging data are important in stroke research, but obtaining these data typically requires laborious manual chart reviews. OBJECTIVE We aimed to determine the accuracy of a natural language processing (NLP) approach to extract information on the presence and location of vascular occlusions as well as other stroke-related attributes based on free-text reports. METHODS From the full reports of 1320 consecutive computed tomography (CT), CT angiography, and CT perfusion scans of the head and neck performed at a tertiary stroke center between October 2017 and January 2019, we manually extracted data on the presence of proximal large vessel occlusion (primary outcome), as well as distal vessel occlusion, ischemia, hemorrhage, Alberta stroke program early CT score (ASPECTS), and collateral status (secondary outcomes). Reports were randomly split into training (n=921) and validation (n=399) sets, and attributes were extracted using rule-based NLP. We reported the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the overall accuracy of the NLP approach relative to the manually extracted data. RESULTS The overall prevalence of large vessel occlusion was 12.2%. In the training sample, the NLP approach identified this attribute with an overall accuracy of 97.3% (95.5% sensitivity, 98.1% specificity, 84.1% PPV, and 99.4% NPV). In the validation set, the overall accuracy was 95.2% (90.0% sensitivity, 97.4% specificity, 76.3% PPV, and 98.5% NPV). The accuracy of identifying distal or basilar occlusion as well as hemorrhage was also high, but there were limitations in identifying cerebral ischemia, ASPECTS, and collateral status. CONCLUSIONS NLP may improve the efficiency of large-scale imaging data collection for stroke surveillance and research.

scholarly journals Automating Stroke Data Extraction From Free-Text Radiology Reports Using Natural Language Processing: Instrument Validation Study

10.2196/24381 ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. e24381
Author(s):  
Amy Y X Yu ◽  
Zhongyu A Liu ◽  
Chloe Pou-Prom ◽  
Kaitlyn Lopes ◽  
Moira K Kapral ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Predictive Value ◽  
Large Vessel ◽  
Free Text ◽  
Imaging Data ◽  
Large Vessel Occlusion ◽  
Vessel Occlusion ◽  
Radiology Reports

Background Diagnostic neurovascular imaging data are important in stroke research, but obtaining these data typically requires laborious manual chart reviews. Objective We aimed to determine the accuracy of a natural language processing (NLP) approach to extract information on the presence and location of vascular occlusions as well as other stroke-related attributes based on free-text reports. Methods From the full reports of 1320 consecutive computed tomography (CT), CT angiography, and CT perfusion scans of the head and neck performed at a tertiary stroke center between October 2017 and January 2019, we manually extracted data on the presence of proximal large vessel occlusion (primary outcome), as well as distal vessel occlusion, ischemia, hemorrhage, Alberta stroke program early CT score (ASPECTS), and collateral status (secondary outcomes). Reports were randomly split into training (n=921) and validation (n=399) sets, and attributes were extracted using rule-based NLP. We reported the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the overall accuracy of the NLP approach relative to the manually extracted data. Results The overall prevalence of large vessel occlusion was 12.2%. In the training sample, the NLP approach identified this attribute with an overall accuracy of 97.3% (95.5% sensitivity, 98.1% specificity, 84.1% PPV, and 99.4% NPV). In the validation set, the overall accuracy was 95.2% (90.0% sensitivity, 97.4% specificity, 76.3% PPV, and 98.5% NPV). The accuracy of identifying distal or basilar occlusion as well as hemorrhage was also high, but there were limitations in identifying cerebral ischemia, ASPECTS, and collateral status. Conclusions NLP may improve the efficiency of large-scale imaging data collection for stroke surveillance and research.

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