Natural Language Processing to Quantify Microbial Keratitis Measurements

Maganti, Nenita; Tan, Huan; Niziol, Leslie M.; Amin, Sejal; Hou, Andrew; Singh, Karandeep; Ballouz, Dena; Woodward, Maria A.

doi:10.1016/j.ophtha.2019.06.003

Cited by 14 publications

(15 citation statements)

References 6 publications

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“…Initial NLP studies did not use ML and focused mostly on algorithmic text extraction of relevant text from clinical notes using rule-based search and keyword extraction for parameters such as visual acuity (VA) ( 79 – 81 ), demographic data (i.e., age, sex) as well as clinical data (i.e., intraocular pressure, visual acuity) related to glaucoma ( 82 ) and cataract identification ( 83 ). Subsequent studies focused on using similar algorithmic rule-based search retrieving text relevant to the diagnosis and identification of several diseases such as herpes zoster ophthalmicus ( 84 ), pseudoexfoliation syndrome ( 85 ), microbial keratitis ( 25 ), and fungal endophthalmitis ( 24 ). While most published work has focused on extracting information from clinical visit notes ( 24 , 84 , 86 ), Stein et al extracted a combination of unstructured data, problem lists, clinical notes, and billing code documentation for multi-modal extraction of pseudoexfoliation syndrome ( 85 ).…”

Section: Resultsmentioning

confidence: 99%

“…The majority of these applications within ophthalmology have focused on imagebased AI including diagnosis of diabetic retinopathy (15,16), age-related macular degeneration (17,18), retinopathy of prematurity (19,20), and glaucoma (21-23), among others. Though structured datasets (such as extracted tabular data from EHRs) and large image datasets have been studied extensively in ophthalmic big data applications, far fewer AI studies in ophthalmology have utilized unstructured, or freetext, data such as EHR clinical notes from office visits (24)(25)(26)(27). Because clinical notes represent the majority of provider documentation regarding each office visit, there remains a large amount of untapped free-text data (up to 80% of data in the EHR) that may be useful in predictive AI or analytics (28).…”

Section: Introductionmentioning

confidence: 99%

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Applications of natural language processing in ophthalmology: present and future

Chen

Baxter²

2022

Front. Med.

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Advances in technology, including novel ophthalmic imaging devices and adoption of the electronic health record (EHR), have resulted in significantly increased data available for both clinical use and research in ophthalmology. While artificial intelligence (AI) algorithms have the potential to utilize these data to transform clinical care, current applications of AI in ophthalmology have focused mostly on image-based deep learning. Unstructured free-text in the EHR represents a tremendous amount of underutilized data in big data analyses and predictive AI. Natural language processing (NLP) is a type of AI involved in processing human language that can be used to develop automated algorithms using these vast quantities of available text data. The purpose of this review was to introduce ophthalmologists to NLP by (1) reviewing current applications of NLP in ophthalmology and (2) exploring potential applications of NLP. We reviewed current literature published in Pubmed and Google Scholar for articles related to NLP and ophthalmology, and used ancestor search to expand our references. Overall, we found 19 published studies of NLP in ophthalmology. The majority of these publications (16) focused on extracting specific text such as visual acuity from free-text notes for the purposes of quantitative analysis. Other applications included: domain embedding, predictive modeling, and topic modeling. Future ophthalmic applications of NLP may also focus on developing search engines for data within free-text notes, cleaning notes, automated question-answering, and translating ophthalmology notes for other specialties or for patients, especially with a growing interest in open notes. As medicine becomes more data-oriented, NLP offers increasing opportunities to augment our ability to harness free-text data and drive innovations in healthcare delivery and treatment of ophthalmic conditions.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applications of natural language processing in ophthalmology: present and future

Chen

Baxter²

2022

Front. Med.

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“…To tackle the problem, artificial intelligence tools can be used such as Natural Language Processing (NLP), a branch of computer science that deals with the interactions between computers and natural human language; it studies the problems connected with the automatic generation and understanding of human language, written or spoken [5,6,7].…”

Section: Introductionmentioning

confidence: 99%

A NLP Pipeline for the Automatic Extraction of Microorganisms Names from Microbiological Notes

Mora

Attene

Gazzarata³

et al. 2021

pHealth 2021

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According to the “Istituto Superiore di Sanita‘” (ISS), hospital infections are the most frequent and serious complication of health care. This constitutes a real health emergency which requires incisive and joint action at all levels of the local and national health organization. Most of the valuable information related to the presence of a specific microorganism in the blood are written into the notes field of the laboratory exams results. The main objective of this work is to build a Natural Language Processing (NLP) pipeline for the automatic extraction of the names of microorganisms present in the clinical texts. A sample of 499 microbiological notes have been analysed with the developed system and all the microorganisms names have been extracted correctly, according to the labels given by the expert.

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“… 12 Measurements are not always consistently recorded in the EHR. 13 , 14 Additionally, patients can be seen by multiple ophthalmologists and have multiple follow-ups. Studies have shown that measurements differ among ophthalmologists, even in controlled settings.…”

Section: Introductionmentioning

confidence: 99%

Open-Source Automatic Biomarker Measurement on Slit-Lamp Photography to Estimate Visual Acuity in Microbial Keratitis

Loo

Woodward

Prajna

et al. 2021

Trans. Vis. Sci. Tech.

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Purpose To assess clinical applicability of automatic image analysis in microbial keratitis (MK) by evaluating the relationship between biomarker measurements on slit-lamp photography (SLP) and best-corrected visual acuity (BCVA). Methods Seventy-six patients with MK with SLP images and same-day logarithm of the minimum angle of resolution (logMAR) BCVA were evaluated. MK biomarkers (stromal infiltrate, white blood cell infiltration, corneal edema, hypopyon, epithelial defect) were segmented manually by ophthalmologists and automatically by a novel, open-source, deep learning–based segmentation algorithm. Five measurements (presence, maximum width, total area, proportion of the corneal limbus area affected, centrality) were calculated. Correlations between the measurements and BCVA were calculated. An automatic regression model estimated BCVA from the measurements. Differences in performance between using manual and automatic measurements were evaluated using William's test (for correlation) and the paired-sample t -test (for absolute error). Results Measurements had high correlations of 0.86 (manual) and 0.84 (automatic) with true BCVA. Estimated BCVA had average (mean ± SD) absolute errors of 0.39 ± 0.27 logMAR (manual, median: 0.30) and 0.35 ± 0.28 logMAR (automatic, median: 0.30) and high correlations of 0.76 (manual) and 0.80 (automatic) with true BCVA. Differences between using manual and automatic measurements were not statistically significant for correlations of measurements with true BCVA ( P = .66), absolute errors of estimated BCVA ( P = .15), or correlations of estimated BCVA with true BCVA ( P = .60). Conclusions The proposed algorithm measured MK biomarkers as accurately as ophthalmologists. Measurements were highly correlated with and estimative of visual acuity. Translational Relevance This study demonstrates the potential of developing fully automatic objective and standardized strategies to aid ophthalmologists in the clinical assessment of MK.

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Natural Language Processing to Quantify Microbial Keratitis Measurements

Cited by 14 publications

References 6 publications

Applications of natural language processing in ophthalmology: present and future

Applications of natural language processing in ophthalmology: present and future

A NLP Pipeline for the Automatic Extraction of Microorganisms Names from Microbiological Notes

Open-Source Automatic Biomarker Measurement on Slit-Lamp Photography to Estimate Visual Acuity in Microbial Keratitis

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