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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)
Infectious Diseases
Last data update: Apr 23, 2024
. (Total: 63653 Documents since 2012)
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Detection of acute dengue virus infection, with and without concurrent malaria infection, in a cohort of febrile children in Kenya, 2014-2019, by clinicians or machine learning algorithms.
David M Vu et al. PLOS Glob Public Health 2023 3(7) e0001950
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A machine learning model to assess potential misdiagnosed dengue hospitalization.
Claudia Yang Santos et al. Heliyon 2023 9(6) e16634
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Detecting dengue fever in children using online Rasch analysis to develop algorithms for parents: An APP development and usability study.
Ting-Yun Hu et al. Medicine 2023 102(13) e33296
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DDPM: A Dengue Disease Prediction and Diagnosis Model Using Sentiment Analysis and Machine Learning Algorithms.
Gaurav Gupta et al. Diagnostics (Basel, Switzerland) 2023 13(6)
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Learning meaningful latent space representations for patient risk stratification: Model development and validation for dengue and other acute febrile illness.
Bernard Hernandez et al. Frontiers in digital health 2023 51057467
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WebGIS-Based Real-Time Surveillance and Response System for Vector-Borne Infectious Diseases.
Momna Javaid et al. International journal of environmental research and public health 2023 20(4)
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A systematic review of dengue outbreak prediction models: Current scenario and future directions.
Xing Yu Leung et al. PLoS neglected tropical diseases 2023 17(2) e0010631
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Machine Learning-Based Detection of Dengue from Blood Smear Images Utilizing Platelet and Lymphocyte Characteristics.
Mayrose Hilda et al. Diagnostics (Basel, Switzerland) 2023 13(2)
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Dengue Prediction in Latin America Using Machine Learning and the One Health Perspective: A Literature Review.
Cabrera Maritza et al. Tropical medicine and infectious disease 2022 7(10)
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Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches.
Keshavamurthy Ravikiran et al. One health (Amsterdam, Netherlands) 2022 15100439
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Role of artificial intelligence-internet of things (AI-IoT) based emerging technologies in the public health response to infectious diseases in Bangladesh.
Rahman Md Siddikur et al. Parasite epidemiology and control 2022 18e00266
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A clinical decision-support system for dengue based on fuzzy cognitive maps.
Hoyos William et al. Health care management science 2022
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Deep learning models for forecasting dengue fever based on climate data in Vietnam.
Hau Nguyen Van et al. PLoS neglected tropical diseases 2022 16(6) e0010509
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Machine Learning Based Forecast of Dengue Fever in Brazilian Cities using Epidemiological and Meteorological Variables.
Roster Kirstin et al. American journal of epidemiology 2022
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Data-driven computational intelligence applied to dengue outbreak forecasting: a case study at the scale of the city of Natal, RN-Brazil.
Sanchez-Gendriz Ignacio et al. Scientific reports 2022 12(1) 6550
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Infectious diseases prevention and control using an integrated health big data system in China.
Zhou Xudong et al. BMC infectious diseases 2022 22(1) 344
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The Diagnosis of Dengue in Patients Presenting With Acute Febrile Illness Using Supervised Machine Learning and Impact of Seasonality.
Ming Damien K et al. Frontiers in digital health 2022 4849641
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Influenza, dengue and common cold detection using LSTM with fully connected neural network and keywords selection.
Nadda Wanchaloem et al. BioData mining 2022 15(1) 5
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Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review.
Sylvestre Emmanuelle et al. PLoS neglected tropical diseases 2022 16(1) e0010056
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Dengue models based on machine learning techniques: A systematic literature review.
Hoyos William et al. Artificial intelligence in medicine 2021 119102157
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Added-value of mosquito vector breeding sites from street view images in the risk mapping of dengue incidence in Thailand.
Su Yin Myat et al. PLoS neglected tropical diseases 2021 15(3) e0009122
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Comparing machine learning with case-control models to identify confirmed dengue cases.
Ho Tzong-Shiann et al. PLoS neglected tropical diseases 2020 Nov 14(11) e0008843
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Large-scale multivariate forecasting models for Dengue - LSTM versus random forest regression.
Mussumeci Elisa et al. Spatial and spatio-temporal epidemiology 2020 Nov 35100372
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Predicting dengue outbreaks at neighbourhood level using human mobility in urban areas.
Bomfim Rafael et al. Journal of the Royal Society, Interface 2020 Oct 17(171) 20200691
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Weekly dengue forecasts in Iquitos, Peru; San Juan, Puerto Rico; and Singapore.
Benedum Corey M et al. PLoS neglected tropical diseases 2020 Oct 14(10) e0008710
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Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia.
Zhao Naizhuo et al. PLoS neglected tropical diseases 2020 Sep 14(9) e0008056
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Predicting dengue importation into Europe, using machine learning and model-agnostic methods.
Salami Donald et al. Scientific reports 2020 Jun 10(1) 9689
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Correlation between Google Trends on dengue fever and national surveillance report in Indonesia.
Husnayain Atina et al. Global health action 2019 12(1) 1552652
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Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data.
Conway Mike et al. Yearbook of medical informatics 2019 Aug 28(1) 208-217
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Can tracking people through phone-call data improve lives?
A Maxmen, Nature News, May 28, 2019
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Page last reviewed:
Feb 1, 2024
Page last updated:
Apr 23, 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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