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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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Precision Health Database|Search|Public Health Genomics and Precision Health Knowledge Base (PHGKB)
Heart, Lung, Blood and Sleep Disorders
Last data update: Apr 25, 2024
. (Total: 63675 Documents since 2012)
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Comparing Active Surveillance and Watchful Waiting With Radical Treatment Using Machine Learning Models Among Patients With Prostate Cancer.
Siqi Hu et al. JCO Clin Cancer Inform 2023 7e2300083
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Development and validation of machine learning models for postoperative venous thromboembolism prediction in colorectal cancer inpatients: a retrospective study.
Li Qin et al. Journal of gastrointestinal oncology 2023 14(1) 220-232
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Predicting perinatal mortality based on maternal health status and health insurance service using homogeneous ensemble machine learning methods.
Bogale Dawit S et al. BMC medical informatics and decision making 2022 22(1) 341
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A non-invasive machine learning mechanism for early disease recognition on Twitter: The case of anemia.
Sarsam Samer Muthana et al. Artificial intelligence in medicine 2022 134102428
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Prediction of anemia using facial images and deep learning technology in the emergency department.
Zhang Aixian et al. Frontiers in public health 2022 10964385
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A Comparison of Logistic Regression Against Machine Learning Algorithms for Gastric Cancer Risk Prediction Within Real-World Clinical Data Streams.
Huang Robert J et al. JCO clinical cancer informatics 2022 6e2200039
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A Deep Learning Model for the Automatic Recognition of Aplastic Anemia, Myelodysplastic Syndromes, and Acute Myeloid Leukemia Based on Bone Marrow Smear.
Wang Meifang et al. Frontiers in oncology 2022 12844978
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Development and internal validation of machine learning algorithms for end-stage renal disease risk prediction model of people with type 2 diabetes mellitus and diabetic kidney disease.
Zou Yutong et al. Renal failure 2022 44(1) 562-570
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Automated prediction of low ferritin concentrations using a machine learning algorithm.
Kurstjens Steef et al. Clinical chemistry and laboratory medicine 2022
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Anemia or other comorbidities? using machine learning to reveal deeper insights into the drivers of acute coronary syndromes in hospital admitted patients.
Alsayegh Faisal et al. PloS one 2022 17(1) e0262997
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Deep learning evaluation of biomarkers from echocardiogram videos.
Hughes J Weston et al. EBioMedicine 2021 73103613
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Cardiac Diagnostic Feature and Demographic Identification (CDF-DI): An IoT Enabled Healthcare Framework Using Machine Learning.
Kumar Deepak et al. Sensors (Basel, Switzerland) 2021 21(19)
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Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia.
Bitew Fikrewold H et al. Public health nutrition 2021 1-29
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The TVGH-NYCU Thal-Classifier: Development of a Machine-Learning Classifier for Differentiating Thalassemia and Non-Thalassemia Patients.
Fu Yi-Kai et al. Diagnostics (Basel, Switzerland) 2021 11(9)
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Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong.
Lee Sharen et al. BMJ open diabetes research & care 2021 9(1)
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A Framework for Criteria-Based Selection and Processing of Fast Healthcare Interoperability Resources (FHIR) Data for Statistical Analysis: Design and Implementation Study.
Gruendner Julian et al. JMIR medical informatics 2021 9(4) e25645
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Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review.
Burlacu Alexandru et al. BioMed research international 2020 20209867872
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Accelerating massively parallel hemodynamic models of coarctation of the aorta using neural networks.
Feiger Bradley et al. Scientific reports 2020 Jun 10(1) 9508
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Data needed to respond appropriately to anemia when it is a public health problem.
Williams Anne M et al. Annals of the New York Academy of Sciences 2019 1450(1) 268-280
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Application of Machine Learning Models to Evaluate Hypoglycemia Risk in Type 2 Diabetes.
Mueller Luke et al. Diabetes therapy : research, treatment and education of diabetes and related disorders 2020 Feb
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Patient Management Assisted by a Neural Network Reduces Mortality in an Intermediate Care Unit.
Heili-Frades Sarah et al. Archivos de bronconeumologia 2020 Jan
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Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients.
Dauvin Antonin et al. NPJ digital medicine 2019 2116
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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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