Eminent Role of Machine Learning in the Healthcare Data Management
SpringerBriefs in Applied Sciences and Technology, ISSN: 2191-5318, Page: 33-47
2021
- 9Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures9
- Readers9
Book Chapter Description
The large quantities of data that can be produced in the medical sector. Each healthcare institution has its own patient records that include important details. When correctly evaluated, the healthcare domain will produce value from this data. A critical step necessary for the learning and application of clinical medicine is to bring medical informatics into the broad scope of medical education. Current main research areas can be categorized according to the organisation, introduction, and assessment of health information systems, the representation of medical expertise, and the study and interpretation of underlying signals and evidence. Machine learning has become really popular in the last few decades, and different methods of machine learning have been developed. It concentrates on the analyzing, developing, designing and implementing of techniques. The algorithms for machine learning use a well-defined learning method that best fits the purpose of the medical data analytics. Simple principles of the healthcare sector and machine learning will be defined in this study. The chapter shows how data analytics and machine learning can assist in the healthcare process, also posing certain obstacles, possibilities that need to be explored in order to achieve successful analytics in healthcare diagnosis.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85108679555&origin=inward; http://dx.doi.org/10.1007/978-981-16-3029-3_3; https://link.springer.com/10.1007/978-981-16-3029-3_3; https://link.springer.com/content/pdf/10.1007/978-981-16-3029-3_3; https://dx.doi.org/10.1007/978-981-16-3029-3_3; https://link.springer.com/chapter/10.1007/978-981-16-3029-3_3
Springer Science and Business Media LLC
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