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Public Health Genomics and Precision Health Knowledge Base (v9.0)
PHGKB
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Public Health Genomics Branch
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Last data update: Jun 20, 2024
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Collaborative strategies for deploying AI-based physician decision support systems: challenges and deployment approaches.
Mirja Mittermaier et al. NPJ Digit Med 2023 6(1) 137
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Collaborative strategies for deploying artificial intelligence to complement physician diagnoses of acute respiratory distress syndrome.
Negar Farzaneh et al. NPJ digital medicine 2023 6(1) 62
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Leveraging Clinical Informatics and Data Science to Improve Care and Facilitate Research in Pediatric Acute Respiratory Distress Syndrome: From the Second Pediatric Acute Lung Injury Consensus Conference.
Sanchez-Pinto L Nelson et al. Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies 2023 24(Supplement 1 2S) S1-S11
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Developing and evaluating a machine-learning-based algorithm to predict the incidence and severity of ARDS with continuous non-invasive parameters from ordinary monitors and ventilators.
Wu Wenzhu et al. Computer methods and programs in biomedicine 2023 230107328
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Discriminating Acute Respiratory Distress Syndrome from other forms of respiratory failure via iterative machine learning.
Afshin-Pour Babak et al. Intelligence-based medicine 2023 100087
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Machine learning models for predicting acute kidney injury in patients with sepsis associated ARDS.
Zhou Yang et al. Shock (Augusta, Ga.) 2023
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Early prediction of moderate-to-severe condition of inhalation-induced acute respiratory distress syndrome via interpretable machine learning.
Wu Junwei et al. BMC pulmonary medicine 2022 22(1) 193
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Utilizing machine learning to improve clinical trial design for acute respiratory distress syndrome.
Schwager E et al. NPJ digital medicine 2021 4(1) 133
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Utilizing machine learning to improve clinical trial design for acute respiratory distress syndrome
E Schwager et al NPJ Digital Medicine, September 9, 2021
Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome.
Zhang Zhongheng et al. Journal of evidence-based medicine 2020 Nov
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Machine Learning Predicts Prolonged Acute Hypoxemic Respiratory Failure in Pediatric Severe Influenza.
Sauthier Michaël S et al. Critical care explorations 2020 Aug 2(8) e0175
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Is There an Association Between COVID-19 Mortality and the Renin-Angiotensin System-a Call for Epidemiologic Investigations.
Hanff Thomas C et al. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 2020 Mar
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A Computable Phenotype for Acute Respiratory Distress Syndrome Using Natural Language Processing and Machine Learning.
Afshar Majid et al. AMIA ... Annual Symposium proceedings. AMIA Symposium 2018 2018157-165
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A quantitative approach for the analysis of clinician recognition of acute respiratory distress syndrome using electronic health record data.
Bechel Meagan A et al. PloS one 2019 14(9) e0222826
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Machine learning for patient risk stratification for acute respiratory distress syndrome.
Zeiberg Daniel et al. PloS one 2019 14(3) e0214465
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Page last reviewed:
Feb 1, 2024
Page last updated:
Jun 20, 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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