Recommendation system for immunization coverage and monitoring

Immunization averts an expected 2 to 3 million deaths every year from diphtheria, tetanus, pertussis (whooping cough), and measles; however, an additional 1.5 million deaths could be avoided if vaccination coverage was improved worldwide. New vaccination technologies provide earlier diagnoses, perso...

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Main Authors: Uzair Aslam Bhatti (Author), Mengxing Huang (Author), Hao Wang (Author), Yu Zhang (Author), Anum Mehmood (Author), Wu Di (Author)
Format: Book
Published: Taylor & Francis Group, 2018-01-01T00:00:00Z.
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042 |a dc 
100 1 0 |a Uzair Aslam Bhatti  |e author 
700 1 0 |a Mengxing Huang  |e author 
700 1 0 |a Hao Wang  |e author 
700 1 0 |a Yu Zhang  |e author 
700 1 0 |a Anum Mehmood  |e author 
700 1 0 |a Wu Di  |e author 
245 0 0 |a Recommendation system for immunization coverage and monitoring 
260 |b Taylor & Francis Group,   |c 2018-01-01T00:00:00Z. 
500 |a 2164-5515 
500 |a 2164-554X 
500 |a 10.1080/21645515.2017.1379639 
520 |a Immunization averts an expected 2 to 3 million deaths every year from diphtheria, tetanus, pertussis (whooping cough), and measles; however, an additional 1.5 million deaths could be avoided if vaccination coverage was improved worldwide. New vaccination technologies provide earlier diagnoses, personalized treatments and a wide range of other benefits for both patients and health care professionals. Childhood diseases that were commonplace less than a generation ago have become rare because of vaccines. However, 100% vaccination coverage is still the target to avoid further mortality. Governments have launched special campaigns to create an awareness of vaccination. In this paper, we have focused on data mining algorithms for big data using a collaborative approach for vaccination datasets to resolve problems with planning vaccinations in children, stocking vaccines, and tracking and monitoring non-vaccinated children appropriately. Geographical mapping of vaccination records helps to tackle red zone areas, where vaccination rates are poor, while green zone areas, where vaccination rates are good, can be monitored to enable health care staff to plan the administration of vaccines. Our recommendation algorithm assists in these processes by using deep data mining and by accessing records of other hospitals to highlight locations with lower rates of vaccination. The overall performance of the model is good. The model has been implemented in hospitals to control vaccination across the coverage area. 
546 |a EN 
690 |a big data for health analysis 
690 |a decision support system 
690 |a health recommendation system 
690 |a health information system 
690 |a Immunologic diseases. Allergy 
690 |a RC581-607 
690 |a Therapeutics. Pharmacology 
690 |a RM1-950 
655 7 |a article  |2 local 
786 0 |n Human Vaccines & Immunotherapeutics, Vol 14, Iss 1, Pp 165-171 (2018) 
787 0 |n http://dx.doi.org/10.1080/21645515.2017.1379639 
787 0 |n https://doaj.org/toc/2164-5515 
787 0 |n https://doaj.org/toc/2164-554X 
856 4 1 |u https://doaj.org/article/75c7cd16f9cd4464b35fb36ea0d338d1  |z Connect to this object online.