Application of Machine Learning Methods in NPH
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F23%3A00370036" target="_blank" >RIV/68407700:21730/23:00370036 - isvavai.cz</a>
Výsledek na webu
<a href="https://doi.org/10.1007/978-3-031-36522-5_19" target="_blank" >https://doi.org/10.1007/978-3-031-36522-5_19</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-36522-5_19" target="_blank" >10.1007/978-3-031-36522-5_19</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Application of Machine Learning Methods in NPH
Popis výsledku v původním jazyce
The chapter explores the use of machine learning (ML) in Normal Pressure Hydrocephalus (NPH) diagnosis and treatment. It delves into various ML techniques, such as artificial neural networks and decision trees, for analyzing medical data, particularly in predictive medicine. The chapter also discusses different ML learning problems, feature extraction and selection in clinical datasets, and the evaluation of ML models using standard diagnostic tests. It also highlights limitations and potential biases in ML applications in neurosurgery. Additionally, the article provides insights into specific applications, including lumbar infusion test-based data and phase-contrast MRI-based data, and reviews other ML studies in NPH, showcasing the diverse approaches and methodologies employed in the field.
Název v anglickém jazyce
Application of Machine Learning Methods in NPH
Popis výsledku anglicky
The chapter explores the use of machine learning (ML) in Normal Pressure Hydrocephalus (NPH) diagnosis and treatment. It delves into various ML techniques, such as artificial neural networks and decision trees, for analyzing medical data, particularly in predictive medicine. The chapter also discusses different ML learning problems, feature extraction and selection in clinical datasets, and the evaluation of ML models using standard diagnostic tests. It also highlights limitations and potential biases in ML applications in neurosurgery. Additionally, the article provides insights into specific applications, including lumbar infusion test-based data and phase-contrast MRI-based data, and reviews other ML studies in NPH, showcasing the diverse approaches and methodologies employed in the field.
Klasifikace
Druh
O - Ostatní výsledky
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
<a href="/cs/project/NU23-04-00551" target="_blank" >NU23-04-00551: Komplexní multidoménová diagnostická baterie pro NPH</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Ostatní
Rok uplatnění
2023
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů