Machine and human interpretable patient visualizations

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3A10456092" target="_blank" >RIV/00216208:11320/22:10456092 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11110/22:10456092

Výsledek na webu

<a href="https://doi.org/10.1109/BIBM55620.2022.9995611" target="_blank" >https://doi.org/10.1109/BIBM55620.2022.9995611</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/BIBM55620.2022.9995611" target="_blank" >10.1109/BIBM55620.2022.9995611</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine and human interpretable patient visualizations

Popis výsledku v původním jazyce

A growing amount of data is stored in electronic health records, which are crucial for the clinical decision-making process. A large part of these data has a tabular form, consisting of numerical and categorical values originating from various laboratory examinations and sensors. Unlike medical images and clinical notes, tabular data lack higher semantics and, combined with the high dimensionality and heterogeneity, their interpretation by a human is challenging. On the other hand, we have witnessed superior performance of deep convolutional neural network (DCNN) models in the visual medical domain. In this paper, we propose visual representations of complex tabular medical data readable simultaneously by humans and machines. To show that these representations can encode the patient&apos;s data semantics effectively, we use them to fine-tune a DCNN to predict the disability level of patients suffering from multiple sclerosis. Our experiments show that the visual models could match the performance of non-visual models. Moreover, the visual representations add the benefit o f s ummarizing complex information about the patient&apos;s state to a human.

Název v anglickém jazyce

Machine and human interpretable patient visualizations

Popis výsledku anglicky

A growing amount of data is stored in electronic health records, which are crucial for the clinical decision-making process. A large part of these data has a tabular form, consisting of numerical and categorical values originating from various laboratory examinations and sensors. Unlike medical images and clinical notes, tabular data lack higher semantics and, combined with the high dimensionality and heterogeneity, their interpretation by a human is challenging. On the other hand, we have witnessed superior performance of deep convolutional neural network (DCNN) models in the visual medical domain. In this paper, we propose visual representations of complex tabular medical data readable simultaneously by humans and machines. To show that these representations can encode the patient&apos;s data semantics effectively, we use them to fine-tune a DCNN to predict the disability level of patients suffering from multiple sclerosis. Our experiments show that the visual models could match the performance of non-visual models. Moreover, the visual representations add the benefit o f s ummarizing complex information about the patient&apos;s state to a human.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

ISBN

978-1-66546-819-0

ISSN

—

e-ISSN

—

Počet stran výsledku

3

Strana od-do

3879-3881

Název nakladatele

IEEE

Místo vydání

Albuquerque

Místo konání akce

Las Vegas, NV, USA

Datum konání akce

6. 12. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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