Machine Learning Survival Models for Relapse Prediction in a Early Stage Lung Cancer Patient

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine Learning Survival Models for Relapse Prediction in a Early Stage Lung Cancer Patient

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Machine Learning Survival Models for Relapse Prediction in a Early Stage Lung Cancer Patient

Popis výsledku anglicky

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.

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

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN

ISBN

9781665488679

ISSN

2161-4393

e-ISSN

2161-4407

Počet stran výsledku

8

Strana od-do

1-8

Název nakladatele

IEEE

Místo vydání

Broadbeach, Australia

Místo konání akce

Broadbeach, Australia

Datum konání akce

1. 1. 2023

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

001046198700044