Computational Methods and Algorithms for Medicine and Optimized Clinical Practice

0502476 - ÚI 2020 RIV US eng M - Monography Chapter
Kalina, Jan
Mental Health Clinical Decision Support Exploiting Big Data.
Computational Methods and Algorithms for Medicine and Optimized Clinical Practice. Hershey: IGI Global, 2019 - (Chui, K.; Lytras, M.), s. 160-184. ISBN 978-1-5225-8244-1
R&D Projects: GA ČR(CZ) GA19-05704S
Institutional support: RVO:67985807
Keywords : big data * decision support * machine learning * supervised learning * mental health
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

The complexity of clinical decision-making is immensely increasing with the advent of big data with a clinical relevance. Clinical decision systems represent useful e-health tools applicable to various tasks within the clinical decision-making process. This chapter is devoted to basic principles of clinical decision support systems and their benefits for healthcare and patient safety. Big data is crucial input for clinical decision support systems and is helpful in the task to find the diagnosis, prognosis, and therapy. Statistical challenges of analyzing big data in psychiatry are overviewed, with a particular interest for psychiatry. Various barriers preventing telemedicine tools from expanding to the field of mental health are discussed. The development of decision support systems is claimed here to play a key role in the development of information-based medicine, particularly in psychiatry. Information technology will be ultimately able to combine various information sources including big data to present and enforce a holistic information-based approach to psychiatric care.
Permanent Link: http://hdl.handle.net/11104/0294415