June 2023
Volume 64, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2023
Use of Natural Language Processing to Accurately Identify Cataracts and Other Lens Pathology in Electronic Health Record Data – A Study Using the Sight Outcomes Research Collaborative (SOURCE) Repository
Author Affiliations & Notes
  • Joshua D Stein
    Ophthalmology, University of Michigan Michigan Medicine, Ann Arbor, Michigan, United States
    University of Michigan School of Public Health, Ann Arbor, Michigan, United States
  • Yunshu Zhou
    Ophthalmology, University of Michigan Michigan Medicine, Ann Arbor, Michigan, United States
  • Chris A. Andrews
    Ophthalmology, University of Michigan Michigan Medicine, Ann Arbor, Michigan, United States
  • Victoria Addis
    Ophthalmology, Penn Medicine, Philadelphia, Pennsylvania, United States
  • Jill Bixler
    Ophthalmology, University of Michigan Michigan Medicine, Ann Arbor, Michigan, United States
  • Nathan Grove
    University of Colorado, Denver, Colorado, United States
  • Judy E Kim
    Ophthalmology, Medical College of Wisconsin, Milwaukee, Wisconsin, United States
  • Brian McMillian
    Ophthalmology, West Virginia University, Morgantown, West Virginia, United States
  • Saleha Munir
    Ophthalmology, University of Maryland Medical System, Baltimore, Maryland, United States
  • Jeffrey Schultz
    Ophthalmology, Montefiore Health System, Bronx, New York, United States
  • Brian C Stagg
    Ophthalmology, University of Utah Health, Salt Lake City, Utah, United States
  • Sophia Wang
    Ophthalmology, Stanford Medicine, Stanford, California, United States
  • Fasika Woreta
    Ophthalmology, Johns Hopkins University, Baltimore, Maryland, United States
  • Footnotes
    Commercial Relationships   Joshua Stein Abbvie, Janssen, Ocular Therapeutix, Code F (Financial Support); Yunshu Zhou None; Chris Andrews None; Victoria Addis None; Jill Bixler None; Nathan Grove None; Judy Kim None; Brian McMillian None; Saleha Munir None; Jeffrey Schultz None; Brian Stagg None; Sophia Wang None; Fasika Woreta None
  • Footnotes
    Support  NEI (R01 EY032475); NIA (R01 AG07258201); Research to Prevent Blindness; Abbvie; Janssen, Ocular Therapeutix
Investigative Ophthalmology & Visual Science June 2023, Vol.64, 1199. doi:
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    • Get Citation

      Joshua D Stein, Yunshu Zhou, Chris A. Andrews, Victoria Addis, Jill Bixler, Nathan Grove, Judy E Kim, Brian McMillian, Saleha Munir, Jeffrey Schultz, Brian C Stagg, Sophia Wang, Fasika Woreta; Use of Natural Language Processing to Accurately Identify Cataracts and Other Lens Pathology in Electronic Health Record Data – A Study Using the Sight Outcomes Research Collaborative (SOURCE) Repository. Invest. Ophthalmol. Vis. Sci. 2023;64(8):1199.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Nearly all published Big Data analyses in ophthalmology have been completely reliant upon ICD billing codes to identify the presence of ocular pathology and to assess for disease stability. However, for a variety of reasons, billing codes can be inaccurate. Here we validate an alternative way to identify and characterize ocular pathology using natural language processing (NLP).

Methods : We developed an NLP algorithm capable of searching free text lens exam data inputted by clinicians into the electronic health record. The algorithm identifies the type(s) of cataract present, cataract density, presence of intraocular lenses (IOLs), location of the IOL, status of the capsule, and presence of other lens pathology (e.g., lens dislocation, phimosis). We applied our algorithm to all 17.5 million lens exam records in the SOURCE repository, which captures eye care for more than 3 million eye care recipients receiving care at 11 large US health care systems. We randomly selected 4314 unique lens exam entries and asked 11 clinicians to review the NLP output to validate whether it correctly identified and categorized the lens pathology present.

Results : Among the 4314 lens exam entries, the NLP algorithm correctly identified and properly categorized all lens pathology present in 4104 (95.1%) of the entries as validated by clinicians. There were only 210 entries (4.9%) identified with errors. Among less common lens pathology, the mentions identified by the algorithm were corroborated by a reviewing clinician for 100% of mentions of pseudoexfoliation material; 99.7% for mentions of phimosis, subluxation, and synechiae; and 96.9% for mentions of phacodonesis. Algorithm errors included incorrectly accounting for negation (e.g., “no evidence of pxf”) and improper assignment of “pigment on the lens capsule” as evidence of posterior capsule opacification.

Conclusions : We developed an algorithm using NLP that accurately identifies and classifies lens abnormalities routinely documented by eye care professionals. Algorithms such as this will help researchers properly identify and classify ocular pathology to augment the sorts of questions that can be answered using electronic health record data.

This abstract was presented at the 2023 ARVO Annual Meeting, held in New Orleans, LA, April 23-27, 2023.

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