Predictive Analytics for Glaucoma Using Data From the All of Us Research Program

Baxter, Sally L.; Saseendrakumar, Bharanidharan Radha; Paul, Paulina; Kim, Jihoon; Bonomi, Luca; Kuo, Tsung-Ting; Loperena, Roxana; Ratsimbazafy, Francis; Boerwinkle, Eric; Cicek, Mine S.; Clark, Cheryl R.; Cohn, Elizabeth; Gebo, Kelly A.; Mayo, Kelsey; Mockrin, Stephen; Schully, Sheri D.; Ramirez, Andrea H.; Ohno-Machado, Lucila

doi:10.1016/j.ajo.2021.01.008

Cited by 38 publications

(25 citation statements)

References 42 publications

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“…ML has shown outstanding results in the detection of glaucoma by recognizing early visual field (VF) loss [13,14], using optical coherence tomography (OCT) retinal nerve fibre layer (RNFL) thickness measurements [15,16], and recently, optical coherence tomography angiography (OCTA) data [17 ▪ ]. Attempts at predicting glaucoma progression through VF progression [18] and using nonophthalmic electronic health data to predict which glaucoma patients would require glaucoma surgery have achieved excellent area-under-curves (AUCs) [19 ▪ ].…”

Section: Text Of Reviewmentioning

confidence: 99%

Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology

Lim

Hong²,

Lam

et al. 2022

Current Opinion in Ophthalmology

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Purpose of reviewThe application of artificial intelligence (AI) in medicine and ophthalmology has experienced exponential breakthroughs in recent years in diagnosis, prognosis, and aiding clinical decision-making. The use of digital data has also heralded the need for privacy-preserving technology to protect patient confidentiality and to guard against threats such as adversarial attacks. Hence, this review aims to outline novel AI-based systems for ophthalmology use, privacy-preserving measures, potential challenges, and future directions of each. Recent findingsSeveral key AI algorithms used to improve disease detection and outcomes include: Data-driven, imagedriven, natural language processing (NLP)-driven, genomics-driven, and multimodality algorithms. However, deep learning systems are susceptible to adversarial attacks, and use of data for training models is associated with privacy concerns. Several data protection methods address these concerns in the form of blockchain technology, federated learning, and generative adversarial networks. SummaryAI-applications have vast potential to meet many eyecare needs, consequently reducing burden on scarce healthcare resources. A pertinent challenge would be to maintain data privacy and confidentiality while supporting AI endeavors, where data protection methods would need to rapidly evolve with AI technology needs. Ultimately, for AI to succeed in medicine and ophthalmology, a balance would need to be found between innovation and privacy.

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Section: Text Of Reviewmentioning

confidence: 99%

Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology

Lim

Hong²,

Lam

et al. 2022

Current Opinion in Ophthalmology

View full text Add to dashboard Cite

show abstract

“…To date, reports from 3 of these projects have been published in peer-reviewed scientific journals. 6,7 The researcher workbench launch has been successful, but important challenges remain in execution of program goals and hypothesis-based discovery science. The analysis environment currentlyrequiresspecificcomputationalabilities(ie,useofRorPython3).…”

Section: All Of Us Is Committed To Broad Sharing Of Data Assets To Drive Biomedical Researchmentioning

confidence: 99%

Progress With the All of Us Research Program

Ramirez

Gebo

Harris

2021

JAMA

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“…Additionally, an approach utilizing a PheRC, rather than one using text-based search, allows for the identification of cases in both publicly available and restricted-access genetic databases, such as UK Biobank or the eMERGE network, where text search is not permitted. These databases will be essential to validate any preliminary GWAS findings from VUMC’s BioVU and will be especially important considering the advantages of multi-site EHRs ( Baxter et al, 2021 ).…”

Section: Future Direction: Application Of Phenome Risk Classifier (Pherc)mentioning

confidence: 99%

Identifying developmental stuttering and associated comorbidities in electronic health records and creating a phenome risk classifier

Pruett

Shaw

Chen

et al. 2021

Journal of Fluency Disorders

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Purpose: This study aimed to identify cases of developmental stuttering and associated comorbidities in de-identified electronic health records (EHRs) at Vanderbilt University Medical Center, and, in turn, build and test a stuttering prediction model. Methods: A multi-step process including a keyword search of medical notes, a text-mining algorithm, and manual review was employed to identify stuttering cases in the EHR. Confirmed cases were compared to matched controls in a phenotype code (phecode) enrichment analysis to reveal conditions associated with stuttering (i.e., comorbidities). These associated phenotypes were used as proxy variables to phenotypically predict stuttering in subjects within the EHR that were not otherwise identifiable using the multi-step identification process described above. Results: The multi-step process resulted in the manually reviewed identification of 1,143 stuttering cases in the EHR. Highly enriched phecodes included codes related to childhood onset fluency disorder, adult-onset fluency disorder, hearing loss, sleep disorders, atopy, a multitude of codes for infections, neurological deficits, and body weight. These phecodes were used as variables to create a phenome risk classifier (PheRC) prediction model to identify additional high likelihood stuttering cases. The PheRC prediction model resulted in a positive predictive value of 83 %. Conclusions: This study demonstrates the feasibility of using EHRs in the study of stuttering and found phenotypic associations. The creation of the PheRC has the potential to enable future studies of stuttering using existing EHR data, including investigations into the genetic etiology.

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Predictive Analytics for Glaucoma Using Data From the All of Us Research Program

Cited by 38 publications

References 42 publications

Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology

Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology

Progress With the All of Us Research Program

Identifying developmental stuttering and associated comorbidities in electronic health records and creating a phenome risk classifier

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