On Predicting Recurrence in Early Stage Non-small Cell Lung Cancer

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00209805%3A_____%2F21%3A00078926" target="_blank" >RIV/00209805:_____/21:00078926 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
On Predicting Recurrence in Early Stage Non-small Cell Lung Cancer
Original language description
Early detection and mitigation of disease recurrence in non-small cell lung cancer (NSCLC) patients is a nontrivial problem that is typically addressed either by rather generic follow-up screening guidelines, self-reporting, simple nomograms, or by models that predict relapse risk in individual patients using statistical analysis of retrospective data. We posit that machine learning models trained on patient data can provide an alternative approach that allows for more efficient development of many complementary models at once, superior accuracy, less dependency on the data collection protocols and increased support for explainability of the predictions. In this preliminary study, we describe an experimental suite of various machine learning models applied on a patient cohort of 2442 early stage NSCLC patients. We discuss the promising results achieved, as well as the lessons we learned while developing this baseline for further, more advanced studies in this area.
Czech name
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Czech description
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
AMIA ... Annual Symposium proceedings. AMIA Symposium
ISBN
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ISSN
1559-4076
e-ISSN
1942-597X
Number of pages
10
Pages from-to
853-861
Publisher name
Bethesda, MD : American Medical Informatics Association
Place of publication
Bethesda
Event location
San Diego, Californie, USA
Event date
Oct 30, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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