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Public Health Genomics and Precision Health Knowledge Base (v9.0)
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Precision Health Database|Search|Public Health Genomics and Precision Health Knowledge Base (PHGKB)
Last data update: Apr 17, 2024
. (Total: 63598 Documents since 2012)
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Prediction of tumor lysis syndrome in childhood acute lymphoblastic leukemia based on machine learning models: a retrospective study.
Yao Xiao et al. Front Oncol 2024 141337295
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Machine Learning for Diagnosis and Screening of Chronic Lymphocytic Leukemia Using Routine Complete Blood Count (CBC) Results.
Regina Padmanabhan et al. Stud Health Technol Inform 2023 305279-282
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Comparing machine learning algorithms to predict 5-year survival in patients with chronic myeloid leukemia.
Shanbehzadeh Mostafa et al. BMC medical informatics and decision making 2022 22(1) 236
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An Automated View Classification Model for Pediatric Echocardiography Using Artificial Intelligence.
Gearhart Addison et al. Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography 2022
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Artificial Intelligence-Augmented Propensity Score, Cost Effectiveness and Computational Ethical Analysis of Cardiac Arrest and Active Cancer with Novel Mortality Predictive Score.
Monlezun Dominique J et al. Medicina (Kaunas, Lithuania) 2022 58(8)
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Improving the Accuracy of Ensemble Machine Learning Classification Models Using a Novel Bit-Fusion Algorithm for Healthcare AI Systems.
Mishra Sashikala et al. Frontiers in public health 2022 10858282
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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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Explainable machine learning for chronic lymphocytic leukemia treatment prediction using only inexpensive tests.
Meiseles Amiel et al. Computers in biology and medicine 2022 145105490
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Artificial intelligence-assisted mapping of proliferation centers allows the distinction of accelerated phase from large cell transformation in chronic lymphocytic leukemia.
El Hussein Siba et al. Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc 2022
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Applications of Artificial Intelligence in Pediatric Oncology: A Systematic Review.
Ramesh Siddhi et al. JCO clinical cancer informatics 2021 51208-1219
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A Machine Learning Approach to the Classification of Acute Leukemias and Distinction From Nonneoplastic Cytopenias Using Flow Cytometry Data.
Monaghan Sara A et al. American journal of clinical pathology 2021
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Personalized Prediction Model to Risk Stratify Patients With Myelodysplastic Syndromes.
Nazha Aziz et al. Journal of clinical oncology : official journal of the American Society of Clinical Oncology 2021 JCO2002810
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A Systematic Review of The Impact of Commercial Aircraft Activity on Air Quality Near Airports.
Riley Karie et al. City and environment interactions 2021 11
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Development and Evaluation of a Leukemia Diagnosis System Using Deep Learning in Real Clinical Scenarios.
Zhou Min et al. Frontiers in pediatrics 2021 9693676
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A Machine Learning Model to Successfully Predict Future Diagnosis of Chronic Myelogenous Leukemia With Retrospective Electronic Health Records Data.
Hauser Ronald G et al. American journal of clinical pathology 2021
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The LEukemia Artificial Intelligence Program (LEAP) in Chronic Myeloid Leukemia in Chronic Phase: A Model to Improve Patient Outcomes.
Sasaki Koji et al. American journal of hematology 2020 Nov
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Acute myeloid leukemia and artificial intelligence, algorithms and new scores.
Radakovich Nathan et al. Best practice & research. Clinical haematology 2020 Sep 33(3) 101192
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The Evolving Landscape of Myelodysplastic Syndrome Prognostication.
Shreve Jacob et al. Clinical hematology international 2020 Jun 2(2) 43-48
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Artificial Neural Network Modeling of Novel Coronavirus (COVID-19) Incidence Rates across the Continental United States.
Mollalo Abolfazl et al. International journal of environmental research and public health 2020 17(12)
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An Evaluation of the Utility of Big Data to Supplement Cancer Treatment Information: Linkage Between IQVIA Pharmacy Database and the Surveillance, Epidemiology, and End Results Program.
Tran Quyen et al. Journal of the National Cancer Institute. Monographs 2020 May 2020(55) 72-81
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Machine learning can identify newly diagnosed patients with CLL at high risk of infection.
Agius Rudi et al. Nature communications 2020 Jan 11(1) 363
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Computational analysis of flow cytometry data in hematological malignancies: future clinical practice?
Duetz Carolien et al. Current opinion in oncology 2019 Dec
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Cell Population Data-Driven Acute Promyelocytic Leukemia Flagging Through Artificial Neural Network Predictive Modeling.
Haider Rana Zeeshan et al. Translational oncology 2019 Nov 13(1) 11-16
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Hybrid machine learning models for predicting types of Human T-cell Lymphotropic Virus.
Sharma Gaurav et al. IEEE/ACM transactions on computational biology and bioinformatics 2019 Sep
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18 F-FDG PET/CT Radiomic Analysis with Machine Learning for Identifying Bone Marrow Involvement in the Patients with Suspected Relapsed Acute Leukemia.
Li Hebei et al. Theranostics 2019 9(16) 4730-4739
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Patient-based prediction algorithm of relapse after allo-HSCT for acute Leukemia and its usefulness in the decision-making process using a machine learning approach.
Fuse Kyoko et al. Cancer medicine 2019 Jul
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Page last reviewed:
Feb 1, 2024
Page last updated:
Apr 17, 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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