Evidence map›Paper›PMID 38296152›Full record

ArticleAmerican journal of ophthalmology2024

Using Natural Language Processing to Identify Different Lens Pathology in Electronic Health Records.

Joshua D Stein, Yunshu Zhou, Chris A Andrews, Judy E Kim, Victoria Addis, Jill Bixler, Nathan Grove, Brian McMillan, Saleha Z Munir, Suzann Pershing and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in American journal of ophthalmology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. A Review of Recent Developments in Artificial Intelligence and Big Data Technologies for Ophthalmology Referrals and Clinical Practice.Medical science monitor : international medical journal of experimental and clinical research · 2026
    Review
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Joshua D SteinFrom the W.K. Kellogg Eye Center, Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, USA (J.D.S., Y.Z., C.A.A., J.B.); Department of Health Management and Policy, University of Michigan School of Public Health, Ann Arbor, Michigan, USA (J.D.S.). Electronic address: jdstein@med.umich.edu.
Yunshu ZhouFrom the W.K. Kellogg Eye Center, Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, USA (J.D.S., Y.Z., C.A.A., J.B.).
Chris A AndrewsFrom the W.K. Kellogg Eye Center, Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, USA (J.D.S., Y.Z., C.A.A., J.B.).
Judy E KimDepartment of Ophthalmology and Visual Sciences, Medical College of Wisconsin, Milwaukee, Wisconsin, USA (J.E.K.).
Victoria AddisDepartment of Ophthalmology, University of Pennsylvania, Philadelphia, Pennsylvania, USA (V.A.).
Jill BixlerFrom the W.K. Kellogg Eye Center, Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, USA (J.D.S., Y.Z., C.A.A., J.B.).
Nathan GroveDepartment of Ophthalmology, University of Colorado School of Medicine, Aurora, Colorado, USA (N.G.).
Brian McMillanDepartment of Ophthalmology and Visual Sciences, West Virginia University, Morgantown, West Virginia, USA (B.M.).
Saleha Z MunirDepartment of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, Maryland, USA (S.Z.M.).
Suzann PershingByers Eye Institute at Stanford, Department of Ophthalmology, Stanford University, Stanford, California, USA (S.P., S.Y.W.); VA Palo Alto Health Care System, Palo Alto, California, USA (S.P.).
Jeffrey S SchultzDepartment of Ophthalmology, Montefiore Medical Center, New York, New York, USA (J.S.S.).
Brian C StaggDepartment of Ophthalmology, University of Utah, Salt Lake City, Utah, USA (B.C.S.).
Sophia Y WangByers Eye Institute at Stanford, Department of Ophthalmology, Stanford University, Stanford, California, USA (S.P., S.Y.W.).
Fasika WoretaDepartment of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA (F.W.).
SOURCE Consortium

Funding

Using Informatics to Evaluate and Predict Cataract Surgery Impact on Alzheimer's Disease and Related Dementias and Mild Cognitive Impairment OutcomesR01AG072582 · NIA · STANFORD UNIVERSITY · PI Suzann Pershing · 2022 to 2026
$3.8M
Enhanced Identification of Ocular Phenotypes and Outcomes in Electronic Health Record DataR01EY032475 · NEI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI STEIN, JOSHUA D · 2022 to 2025
$2.7M
Health Evaluation and Access to Leverage Technology for Improved DR Outcomes (HEAL-DR)R01EY036341 · NEI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI DUSTIN D. FRENCH, Joshua D Stein · 2024 to 2026
$2.1M
Health Disparities in Utilization, Quality, and Outcomes for Three Common Ocular Conditions (HealthDOC)R01EY034444 · NEI · NORTHWESTERN UNIVERSITY AT CHICAGO · PI DUSTIN D. FRENCH, Joshua D Stein · 2023 to 2026
$1.8M
Improving Glaucoma Care Using a Scalable Decision Support SystemK23EY032577 · NEI · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · PI Brian Stagg · 2022 to 2026
$1.3M
NEI NIH HHS K23 EY032577NEI NIH HHS R01 EY032475NEI NIH HHS R01 EY034444NEI NIH HHS R01 EY036341NIA NIH HHS R01 AG072582
6 · The paper itself

Abstract

purposeNearly all published ophthalmology-related Big Data studies rely exclusively on International Classification of Diseases (ICD) billing codes to identify patients with particular ocular conditions. However, inaccurate or nonspecific codes may be used. We assessed whether natural language processing (NLP), as an alternative approach, could more accurately identify lens pathology.

designDatabase study comparing the accuracy of NLP versus ICD billing codes to properly identify lens pathology.

methodsWe developed an NLP algorithm capable of searching free-text lens exam data in the electronic health record (EHR) to identify the type(s) of cataract present, cataract density, presence of intraocular lenses, and other lens pathology. We applied our algorithm to 17.5 million lens exam records in the Sight Outcomes Research Collaborative (SOURCE) repository. We selected 4314 unique lens-exam entries and asked 11 clinicians to assess whether all pathology present in the entries had been correctly identified in the NLP algorithm output. The algorithm's sensitivity at accurately identifying lens pathology was compared with that of the ICD codes.

resultsThe NLP algorithm correctly identified all lens pathology present in 4104 of the 4314 lens-exam entries (95.1%). For less common lens pathology, algorithm findings were corroborated by reviewing clinicians for 100% of mentions of pseudoexfoliation material and 99.7% for phimosis, subluxation, and synechia. Sensitivity at identifying lens pathology was better for NLP (0.98 [0.96-0.99] than for billing codes (0.49 [0.46-0.53]).

conclusionsOur NLP algorithm identifies and classifies lens abnormalities routinely documented by eye-care professionals with high accuracy. Such algorithms will help researchers to properly identify and classify ocular pathology, broadening the scope of feasible research using real-world data.

Indexed as

AlgorithmsElectronic Health RecordsInternational Classification of DiseasesLens, CrystallineNatural Language ProcessingCataractFemaleHumansLens DiseasesMale

Identifiers

PMID38296152
PMCPMC11098689

What Socratic holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.