Publication date: Available online 28 March 2018
Source:Academic Radiology
Author(s): W. Katherine Tan, Saeed Hassanpour, Patrick J. Heagerty, Sean D. Rundell, Pradeep Suri, Hannu T. Huhdanpaa, Kathryn James, David S. Carrell, Curtis P. Langlotz, Nancy L. Organ, Eric N. Meier, Karen J. Sherman, David F. Kallmes, Patrick H. Luetmer, Brent Griffith, David R. Nerenz, Jeffrey G. Jarvik
Rationale and ObjectivesTo evaluate a natural language processing (NLP) system built with open-source tools for identification of lumbar spine imaging findings related to low back pain on magnetic resonance and x-ray radiology reports from four health systems.Materials and MethodsWe used a limited data set (de-identified except for dates) sampled from lumbar spine imaging reports of a prospectively assembled cohort of adults. From N = 178,333 reports, we randomly selected N = 871 to form a reference-standard dataset, consisting of N = 413 x-ray reports and N = 458 MR reports. Using standardized criteria, four spine experts annotated the presence of 26 findings, where 71 reports were annotated by all four experts and 800 were each annotated by two experts. We calculated inter-rater agreement and finding prevalence from annotated data. We randomly split the annotated data into development (80%) and testing (20%) sets. We developed an NLP system from both rule-based and machine-learned models. We validated the system using accuracy metrics such as sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).ResultsThe multirater annotated dataset achieved inter-rater agreement of Cohen's kappa > 0.60 (substantial agreement) for 25 of 26 findings, with finding prevalence ranging from 3% to 89%. In the testing sample, rule-based and machine-learned predictions both had comparable average specificity (0.97 and 0.95, respectively). The machine-learned approach had a higher average sensitivity (0.94, compared to 0.83 for rules-based), and a higher overall AUC (0.98, compared to 0.90 for rules-based).ConclusionsOur NLP system performed well in identifying the 26 lumbar spine findings, as benchmarked by reference-standard annotation by medical experts. Machine-learned models provided substantial gains in model sensitivity with slight loss of specificity, and overall higher AUC.
from Imaging via alkiviadis.1961 on Inoreader https://ift.tt/2GVojdK
Εγγραφή σε:
Σχόλια ανάρτησης (Atom)
Δημοφιλείς αναρτήσεις
-
Abstract Objective To evaluate Chinese medicine (CM) formula Bazheng Powder (八正散) as an alternative therapeutic option for female patients...
-
To evaluate the effect of Recurrence Score® results (RS; Oncotype DX® multigene assay ODX) on treatment recommendations by Swiss multidiscip...
-
Abstract Soil conditioners can be used to compensate for the insufficient soil nutrition and organic matter (OM) of arable soils. However, ...
-
Ocular Vestibular Evoked Myogenic Potentials: Where Are We Now? Objective: Over the last decade, ocular vestibular evoked myogenic potential...
-
Abstract Objective To study the effects of Astragalus polysaccharide (APS), the primary effective component of the Chinese herb medicine A...
-
Geriatric trauma: A population-based study Saint Shiou-Sheng Chen, Li-Chien Chien Formosan Journal of Surgery 2019 52(2):39-44 Background: G...
-
Pharmacogenomics in palliative medicine Mahadev Rao Indian Journal of Palliative Care 2019 25(2):169-171 A survey of medical professionals i...
-
Objectives Adult sagittal posture is established during childhood and adolescence. A flattened or hypercurved spine is associated with poore...
-
Related Articles Screening for Atrial Fibrillation using Economical and accurate TechnologY (SAFETY)-a pilot study. BMJ Open. 2017 Ja...
Δεν υπάρχουν σχόλια:
Δημοσίευση σχολίου