Taylor & Francis

Application of Advanced Optimization Techniques for Healthcare Analytics - Hardback

Edition: 1st Edition
Subjects: Engineering, Environmental science, engineering & technology
ISBN13: 9781032348810
Published: 11 Apr 2023

Format - Hardback
By Mohamed Abdel-Basset

The release of this order may delay up to 4-6 weeks due to congestion at publisher’s warehouse.

Regular price A$210.40
Sale price A$210.40 Regular price A$263.00

Application of Advanced Optimization Techniques for Healthcare Analytics - Hardback

Regular price A$210.40
Sale price A$210.40 Regular price A$263.00
Product description

Application of Advanced Optimization Techniques for Healthcare Analytics, 1st Edition, is an excellent compilation of current and advanced optimization techniques which can readily be applied to solve different hospital management problems. The healthcare system is currently a topic of significant investigation to make life easier for those who are disabled, old, or sick, as well as for young children. The emphasis of the healthcare system has evolved throughout time due to several emerging beneficial technologies, such as personal digital assistants (PDAs), data mining, the internet of things, metaheuristics, fog computing, and cloud computing.

Metaheuristics are strong technology for tackling several optimization problems in various fields, especially healthcare systems. The primary advantage of metaheuristic algorithms is their ability to find a better solution to a healthcare problem and their ability to consume as little time as possible. In addition, metaheuristics are more flexible compared to several other optimization techniques. These algorithms are not related to a specific optimization problem but could be applied to any optimization problem by making some small adaptations to become suitable to tackle it.

The successful outcome of this book will enable a decision-maker or practitioner to pick a suitable optimization approach when making decisions to schedule patients under crowding environments with minimized human errors.

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