Appl Clin Inform 2020; 11(04): 606-616
DOI: 10.1055/s-0040-1715892
Research Article

A Web Application for Adrenal Incidentaloma Identification, Tracking, and Management Using Machine Learning

Wasif Bala
1  Boston Medical Center, One Boston Medical Center Pl, Boston, Massachusetts, United States
,
Jackson Steinkamp
1  Boston Medical Center, One Boston Medical Center Pl, Boston, Massachusetts, United States
,
Timothy Feeney
1  Boston Medical Center, One Boston Medical Center Pl, Boston, Massachusetts, United States
,
Avneesh Gupta
1  Boston Medical Center, One Boston Medical Center Pl, Boston, Massachusetts, United States
,
Abhinav Sharma
2  Department of Family Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
,
Jake Kantrowitz
3  Department of Internal Medicine, Kent Hospital, Brown University Alpert Medical School, Warwick, Rhode Island, United States
,
Nicholas Cordella
1  Boston Medical Center, One Boston Medical Center Pl, Boston, Massachusetts, United States
,
James Moses
1  Boston Medical Center, One Boston Medical Center Pl, Boston, Massachusetts, United States
,
Frederick Thurston Drake
1  Boston Medical Center, One Boston Medical Center Pl, Boston, Massachusetts, United States
› Author Affiliations

Abstract

Background Incidental radiographic findings, such as adrenal nodules, are commonly identified in imaging studies and documented in radiology reports. However, patients with such findings frequently do not receive appropriate follow-up, partially due to the lack of tools for the management of such findings and the time required to maintain up-to-date lists. Natural language processing (NLP) is capable of extracting information from free-text clinical documents and could provide the basis for software solutions that do not require changes to clinical workflows.

Objectives In this manuscript we present (1) a machine learning algorithm we trained to identify radiology reports documenting the presence of a newly discovered adrenal incidentaloma, and (2) the web application and results database we developed to manage these clinical findings.

Methods We manually annotated a training corpus of 4,090 radiology reports from across our institution with a binary label indicating whether or not a report contains a newly discovered adrenal incidentaloma. We trained a convolutional neural network to perform this text classification task. Over the NLP backbone we built a web application that allows users to coordinate clinical management of adrenal incidentalomas in real time.

Results The annotated dataset included 404 positive (9.9%) and 3,686 (90.1%) negative reports. Our model achieved a sensitivity of 92.9% (95% confidence interval: 80.9–97.5%), a positive predictive value of 83.0% (69.9–91.1)%, a specificity of 97.8% (95.8–98.9)%, and an F1 score of 87.6%. We developed a front-end web application based on the model's output.

Conclusion Developing an NLP-enabled custom web application for tracking and management of high-risk adrenal incidentalomas is feasible in a resource constrained, safety net hospital. Such applications can be used by an institution's quality department or its primary care providers and can easily be generalized to other types of clinical findings.

Protection of Human and Animal Subjects

This study was reviewed and approved by the Boston Medical Center and Boston University Medical Campus Institutional Review Board (IRB).




Publication History

Received: 12 April 2020

Accepted: 22 July 2020

Publication Date:
16 September 2020 (online)

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