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Public Health Genomics and Precision Health Knowledge Base (v9.0)
PHGKB
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Precision Health Database|Search|Public Health Genomics and Precision Health Knowledge Base (PHGKB)
Last data update: Apr 25, 2024
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Records 1 - 18 (of 18 Records)
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Machine Learning Models for Predicting Sudden Sensorineural Hearing Loss Outcome: A Systematic Review.
Amirhossein Aghakhani et al. Ann Otol Rhinol Laryngol 2023 34894231206902
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Identification of High Likelihood of Dementia in Population-Based Surveys using Unsupervised Clustering: a Longitudinal Analysis.
Amin Gharbi-Meliani et al. medRxiv : the preprint server for health sciences 2023
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Artificial Neural Network-Assisted Classification of Hearing Prognosis of Sudden Sensorineural Hearing Loss With Vertigo.
Sheng-Chiao Lin et al. IEEE journal of translational engineering in health and medicine 2023 11170-181
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Machine Learning Prediction of Objective Hearing Loss With Demographics, Clinical Factors, and Subjective Hearing Status.
Tyler J Gathman et al. Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery 2023
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Evaluation of Machine Learning Algorithms and Explainability Techniques to Detect Hearing Loss From a Speech-in-Noise Screening Test.
Lenatti Marta et al. American journal of audiology 2022 1-19
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A Deep Learning Approach to Predict Conductive Hearing Loss in Patients With Otitis Media With Effusion Using Otoscopic Images.
Zeng Junbo et al. JAMA otolaryngology-- head & neck surgery 2022
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Older Adults' and Clinicians' Perspectives on a Smart Health Platform for the Aging Population: Design and Evaluation Study.
Cristiano Alessia et al. JMIR aging 2022 5(1) e29623
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Digital approaches to automated and machine learning assessments of hearing: a scoping review.
Wasmann Jan-Wilem et al. Journal of medical Internet research 2021
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Using Machine Learning and the National Health and Nutrition Examination Survey to Classify Individuals With Hearing Loss.
Ellis Gregory M et al. Frontiers in digital health 2021 3723533
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Computational Audiology: New Approaches to Advance Hearing Health Care in the Digital Age.
Wasmann Jan-Willem A et al. Ear and hearing 2021
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Predicting Depression From Hearing Loss Using Machine Learning.
Crowson Matthew G et al. Ear and hearing 2021 Jan
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Contributions and limitations of using machine learning to predict noise-induced hearing loss.
Chen Feifan et al. International archives of occupational and environmental health 2021 Jan
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Public health policy-making for hearing loss: stakeholders' evaluation of a novel eHealth tool.
Dritsakis Giorgos et al. Health research policy and systems 2020 Oct 18(1) 125
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Artificial Intelligence Applications in Otology: A State of the Art Review.
You Eunice et al. Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery 2020 Jun 194599820931804
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A Data-informed Public Health Policy-Makers Platform.
Brdaric Dario et al. International journal of environmental research and public health 2020 May 17(9)
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Application of Big Data to Support Evidence-Based Public Health Policy Decision-Making for Hearing.
Saunders Gabrielle H et al. Ear and hearing 2020 Jan
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Internet-based interventions for adults with hearing loss, tinnitus and vestibular disorders: a protocol for a systematic review.
Beukes Eldr? W et al. Systematic reviews 2018 7(1) 205
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Predicting cochlear dead regions in patients with hearing loss through a machine learning-based approach: A preliminary study.
Chang Young-Soo et al. PloS one 2019 14(6) e0217790
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Page last reviewed:
Feb 1, 2024
Page last updated:
Apr 25, 2024
Content source:
Public Health Genomics Branch in the Division of Blood Disorders and Public Health Genomics
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National Center on Birth Defects and Developmental Disabilities
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