Machine Learning Survival Models for Relapse Prediction in a Early Stage Lung Cancer Patient
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F23%3A00133943" target="_blank" >RIV/00216224:14330/23:00133943 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/IJCNN54540.2023.10191078" target="_blank" >http://dx.doi.org/10.1109/IJCNN54540.2023.10191078</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IJCNN54540.2023.10191078" target="_blank" >10.1109/IJCNN54540.2023.10191078</a>
Alternative languages
Result language
angličtina
Original language name
Machine Learning Survival Models for Relapse Prediction in a Early Stage Lung Cancer Patient
Original language description
Lung cancer is one of the leading health complications causing high mortality worldwide. The relapsing behavior of medically treated early-stage lung cancer makes this disease even more complicated. Thus predicting such relapse using a data-centric approach provides a complementary perspective for clinicians to understand the disease. In this preliminary work, we explored off-the-shelf survival models to predict the relapse of early-stage lung cancer patients. We analyzed the survival models on a cohort of 1348 early-stage non-small cell lung cancer (NSCLC) patients in different timestamps. Using the prediction explanation model SHAP (SHapley Additive exPlanations), we further explained the best-performing survival model's predictions. Our explainable predictive model is a potential tool for oncologists that address an unmet clinical need for post-treatment patient stratification based on the relapse hazard.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
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)
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN
ISBN
9781665488679
ISSN
2161-4393
e-ISSN
2161-4407
Number of pages
8
Pages from-to
1-8
Publisher name
IEEE
Place of publication
Broadbeach, Australia
Event location
Broadbeach, Australia
Event date
Jan 1, 2023
Type of event by nationality
CST - Celostátní akce
UT code for WoS article
001046198700044