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Using Natural Language Processing to Identify Different Lens Pathology in Electronic Health Records.
Stein, Joshua D; Zhou, Yunshu; Andrews, Chris A; Kim, Judy E; Addis, Victoria; Bixler, Jill; Grove, Nathan; McMillan, Brian; Munir, Saleha Z; Pershing, Suzann; Schultz, Jeffrey S; Stagg, Brian C; Wang, Sophia Y; Woreta, Fasika.
Affiliation
  • Stein JD; From 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
  • Zhou Y; From 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.).
  • Andrews CA; From 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.).
  • Kim JE; Department of Ophthalmology and Visual Sciences, Medical College of Wisconsin, Milwaukee, Wisconsin, USA (J.E.K.).
  • Addis V; Department of Ophthalmology, University of Pennsylvania, Philadelphia, Pennsylvania, USA (V.A.).
  • Bixler J; From 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.).
  • Grove N; Department of Ophthalmology, University of Colorado School of Medicine, Aurora, Colorado, USA (N.G.).
  • McMillan B; Department of Ophthalmology and Visual Sciences, West Virginia University, Morgantown, West Virginia, USA (B.M.).
  • Munir SZ; Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, Maryland, USA (S.Z.M.).
  • Pershing S; Byers 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.).
  • Schultz JS; Department of Ophthalmology, Montefiore Medical Center, New York, New York, USA (J.S.S.).
  • Stagg BC; Department of Ophthalmology, University of Utah, Salt Lake City, Utah, USA (B.C.S.).
  • Wang SY; Byers Eye Institute at Stanford, Department of Ophthalmology, Stanford University, Stanford, California, USA (S.P., S.Y.W.).
  • Woreta F; Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA (F.W.).
Am J Ophthalmol ; 262: 153-160, 2024 Jun.
Article in En | MEDLINE | ID: mdl-38296152
ABSTRACT

PURPOSE:

Nearly 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.

DESIGN:

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

METHODS:

We 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.

RESULTS:

The 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]).

CONCLUSIONS:

Our 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.
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Full text: 1 Collection: 01-internacional Health context: 1_ASSA2030 Database: MEDLINE Main subject: Algorithms / Natural Language Processing / International Classification of Diseases / Electronic Health Records / Lens, Crystalline Type of study: Prognostic_studies Limits: Female / Humans / Male Language: En Journal: Am J Ophthalmol Year: 2024 Document type: Article
Fulltext
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Full text: 1 Collection: 01-internacional Health context: 1_ASSA2030 Database: MEDLINE Main subject: Algorithms / Natural Language Processing / International Classification of Diseases / Electronic Health Records / Lens, Crystalline Type of study: Prognostic_studies Limits: Female / Humans / Male Language: En Journal: Am J Ophthalmol Year: 2024 Document type: Article