On Analyzing Complex Data Within Clinical Decision Support Systems

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F23%3A00565534" target="_blank" >RIV/67985807:_____/23:00565534 - isvavai.cz</a>

Result on the web

<a href="https://dx.doi.org/10.4018/978-1-6684-5092-5.ch004" target="_blank" >https://dx.doi.org/10.4018/978-1-6684-5092-5.ch004</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.4018/978-1-6684-5092-5.ch004" target="_blank" >10.4018/978-1-6684-5092-5.ch004</a>

Result language

angličtina

Original language name

On Analyzing Complex Data Within Clinical Decision Support Systems

Original language description

Clinical decision support systems (CDSSs) represent digital health tools applicable to important tasks within the clinical decision-making process. Training data-driven CDSSs requires extracting medical knowledge from the available information by means of machine learning. The analysis of the complex (possibly big or high-dimensional) training data allows knowledge relevant to be obtained for clinical decisions related to the diagnosis, therapy, or prognosis. This chapter is devoted to training CDSSs by machine learning based on complex data. Remarkable recent examples of CDSSs including those based on deep learning are recalled here. Principles, challenges, or ethical aspects of machine learning are discussed here in the context of CDSSs. Attention is paid to dimensionality reduction, deep learning methods for big data, or explainability of the data analysis methods. Data analysis issues are discussed also for two particular CDSSs on which the author of this chapter participated.

Czech name

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Czech description

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Type

C - Chapter in a specialist book

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/NU21-08-00432" target="_blank" >NU21-08-00432: Predicting functional outcome in schizophrenia from multimodal neuroimaging and clinical data</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Book/collection name

Diverse Perspectives and State-of-the-Art Approaches to the Utilization of Data-Driven Clinical Decision Support Systems

ISBN

9781668450925

Number of pages of the result

21

Pages from-to

84-104

Number of pages of the book

380

Publisher name

IGI Global

Place of publication

Hershey

UT code for WoS chapter

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