This book provides an ideal foundation for readers to understand the application of artificial intelligence (AI) and machine learning (ML) techniques to expert systems in the healthcare sector. It starts with an introduction to the topic and presents chapters which progressively explain decision-making theory that helps solve problems which have multiple criteria that can affect the outcome of a decision. Key aspects of the subject such as machine learning in healthcare, prediction techniques, mathematical models and classification of healthcare problems are included along with chapters which delve in to advanced topics on data science (deep-learning, artificial neural networks, etc.) and practical examples (influenza epidemiology and retinoblastoma treatment analysis).
- Introduces readers to the basics of AI and ML in expert systems for healthcare
- Focuses on a problem solving approach to the topic
- Provides information on relevant decision-making theory and data science used in the healthcare industry
- Includes practical applications of AI and ML for advanced readers
- Includes bibliographic references for further reading
The reference is an accessible source of knowledge on multi-criteria decision-support systems in healthcare for medical consultants, healthcare policy makers, researchers in the field of medical biotechnology, oncology and pharmaceutical research and development.
The application gives a ranking result based on the selected criteria, their corresponding values and assigned weights. The book will also contain several practical applications of how decision-making theory could be used in solving problems related to selection of the best alternatives.
About the editor:
Dr Ilker Ozsahin have extensive experience in medical imaging devices such as PET and SPECT and been to University of Illinois at Urbana-Champaign, Illinois, the USA, to work on the growth of carbon nanotubes. He worked at Universidad Autonoma de Barcelona in Spain as a Ph.D. student for developing a PET scanner. He worked as a research assistant for six years in the Physics Department at Cukurova University in Adana, Turkey. Then, he worked at Harvard Medical School and Massachusetts General Hospital as a postdoctoral fellow. Also worked at the University of Macau as a visiting fellow for multi-pinhole brain and cardiac SPECT collimator design and implementation. Currently, as an Associate Professor in the Department of Biomedical Engineering at Near East University, He is working on the simulation of novel high-sensitivity and high-resolution PET and SPECT scanners, multi-criteria decision-making application on healthcare, as well as deep learning in medical imaging such as Alzheimer's for high-accuracy classification and early detection.
Dr Dilber Uzun Ozsahin graduated from the Department of Physics at Cukurova University in 2006. He have worked at CERN, Geneva, during 2008-2010 for my Master's thesis. He completed his Ph.D. studies in 2014 at Universitat Autonoma de Barcelona, Spain. In 2015 he worked at Gordon Center for Medical Imaging, NMMI Radiology Department, Massachusetts General Hospital & Harvard Medical School as a post-doc. He worked on a new technique called laser-induced optical barriers (LIOB) technique to improve nuclear medicine imaging devices' cost and performance. He have designed a high-performance cardiac SPECT system in a cost-effective manner using the LIOB technique. Recently, He have been working on the application of biomedical instrumentation using artificial intelligence, multi-criteria decision analysis in engineering and healthcare, and artificial intelligence in healthcare. Currently, He is working at the University of Sharjah as an Associate Professor.
Keywords: Artificial intelligence and machine learning, data science used in the healthcare industry, prediction techniques, mathematical models and classification of healthcare, artificial neural networks, influenza epidemiology and retinoblastoma treatment analysis, Binary Response, Classifier in Health Research, Consumer Preference, Econometrics, Healthcare, Hospital Preference, Logistic Function, Logistic Regression, Marginal Effects, Maximum Likelihood Estimator, Microdata, Private Hospital Choice, Sigmoid Function.
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