On Analyzing Complex Data Within Clinical Decision Support Systems

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

On Analyzing Complex Data Within Clinical Decision Support Systems

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

On Analyzing Complex Data Within Clinical Decision Support Systems

Popis výsledku anglicky

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.

Druh

C - Kapitola v odborné knize

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/NU21-08-00432" target="_blank" >NU21-08-00432: Predikce funkčního vyústění schizofrenie z multimodálních neurozobrazovacích a klinických dat</a><br>

Návaznosti

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

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

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

ISBN

9781668450925

Počet stran výsledku

21

Strana od-do

84-104

Počet stran knihy

380

Název nakladatele

IGI Global

Místo vydání

Hershey

Kód UT WoS kapitoly

—