scholarly journals Automated Detection of Radiology Reports that Require Follow-up Imaging Using Natural Language Processing Feature Engineering and Machine Learning Classification

2019 ◽  
Vol 33 (1) ◽  
pp. 131-136 ◽  
Author(s):  
Robert Lou ◽  
Darco Lalevic ◽  
Charles Chambers ◽  
Hanna M. Zafar ◽  
Tessa S. Cook
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Automated Detection ◽  
Feature Engineering ◽  
Machine Learning Classification ◽  
Radiology Reports

Automating incidental findings in radiology reports using natural language processing and machine learning to identify and classify pulmonary nodules.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e18093-e18093
Author(s):  
Christi French ◽  
Maciek Makowski ◽  
Samantha Terker ◽  
Paul Alexander Clark
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Pulmonary Nodule ◽  
Incidental Findings ◽  
Lung Nodule ◽  
Radiology Reports

e18093 Background: Pulmonary nodule incidental findings challenge providers to balance resource efficiency and high clinical quality. Incidental findings tend to be undertreated with studies reporting appropriate follow-up rates as low as 29%. Ensuring appropriate follow-up on all incidental findings is labor-intensive; requires the clinical reading and classification of radiology reports to identify high-risk lung nodules. We tested the feasibility of automating this process with natural language processing (NLP) and machine learning (ML). Methods: In cooperation with Sarah Cannon Research Institute (SCRI), we conducted a series of data science experiments utilizing NLP and ML computing techniques on 8,879 free-text, narrative CT (computerized tomography) radiology reports. Reports used were dated from Dec 8, 2015 - April 23, 2017, came from SCRI-affiliated Emergency Department, Inpatient, and Outpatient facilities and were a representative, random sample of the patient populations. Reports were divided into a development set for model training and validation, and a test set to evaluate model performance. Two models were developed - a “Nodule Model” was trained to detect the reported presence of a pulmonary nodule and a rules-based “Sizing Model” was developed to extract the size of the nodule in millimeters. Reports were bucketed into three prediction groups: > = 6 mm, < 6 mm, and no size indicated. Nodules were considered positives and placed in a queue for follow-up if the nodule was predicted > = 6 mm, or if the nodule had no size indicated and the radiology 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 (lung nodule classification model and size extraction model) was 72.9%, recall was 60.3%, and queue precision was 90.2%, exceeding success criteria. Conclusions: The experiments demonstrate the feasibility of NLP and ML 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.

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.

scholarly journals High-fidelity discrimination of ARDS versus other causes of respiratory failure using natural language processing and iterative machine learning

2021 ◽  
Author(s):  
Babak Afshin-Pour ◽  
Michael Qiu ◽  
Shahrzad Hosseini ◽  
Molly Stewart ◽  
Jan Horsky ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Ventilation ◽  
Natural Language Processing ◽  
Respiratory Failure ◽  
Natural Language ◽  
Laboratory Test ◽  
Language Processing ◽  
Test Results ◽  
Radiology Reports ◽  
Laboratory Test Results

ABSTRACTDespite the high morbidity and mortality associated with Acute Respiratory Distress Syndrome (ARDS), discrimination of ARDS from other causes of acute respiratory failure remains challenging, particularly in the first 24 hours of mechanical ventilation. Delay in ARDS identification prevents lung protective strategies from being initiated and delays clinical trial enrolment and quality improvement interventions. Medical records from 1,263 ICU-admitted, mechanically ventilated patients at Northwell Health were retrospectively examined by a clinical team who assigned each patient a diagnosis of “ARDS” or “non-ARDS” (e.g., pulmonary edema). We then applied an iterative pre-processing and machine learning framework to construct a model that would discriminate ARDS versus non-ARDS, and examined features informative in the patient classification process. Data made available to the model included patient demographics, laboratory test results from before the initiation of mechanical ventilation, and features extracted by natural language processing of radiology reports. The resulting model discriminated well between ARDS and non-ARDS causes of respiratory failure (AUC=0.85, 89% precision at 20% recall), and highlighted features unique among ARDS patients, and among and the subset of ARDS patients who would not recover. Importantly, models built using both clinical notes and laboratory test results out-performed models built using either data source alone, akin to the retrospective clinician-based diagnostic process. This work demonstrates the feasibility of using readily available EHR data to discriminate ARDS patients prospectively in a real-world setting at a critical time in their care and highlights novel patient characteristics indicative of ARDS.

scholarly journals Machine learning and natural language processing methods to identify ischemic stroke, acuity and location from radiology reports

PLoS ONE ◽  
2020 ◽  
Vol 15 (6) ◽  
pp. e0234908 ◽  
Author(s):  
Charlene Jennifer Ong ◽  
Agni Orfanoudaki ◽  
Rebecca Zhang ◽  
Francois Pierre M. Caprasse ◽  
Meghan Hutch ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Processing Methods ◽  
Radiology Reports
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