Semantically Describing Predictive Models for Interpretable Insights into Lung Cancer Relapse

doi:doi:10.57702/z26cs7i9

Semantically Describing Predictive Models for Interpretable Insights into Lung Cancer Relapse

SemDesLC predicts lung cancer relapse likelihood, providing oncologists with patient-centric and population-centric analysis. Our approach bridge the gap and fulfill the needs of three different type of users: KG builders, analysts and consumers. This repository contains all the necessary scripts and instructions to reproduce the experiments.

BibTex:

@dataset{Yashrajsinh_Chudasama_and_Disha_Purohit_and_Philipp_D_Rohde_and_Enrique_Iglesias_and_Maria_Torrente_and_Maria-Esther_Vidal_2024,
    abstract = {SemDesLC predicts lung cancer relapse likelihood, providing oncologists with patient-centric and population-centric analysis. Our approach bridge the gap and fulfill the needs of three different type of users: KG builders, analysts and consumers. This repository contains all the necessary scripts and instructions to reproduce the experiments.},
    author = {Yashrajsinh Chudasama and Disha Purohit and Philipp D. Rohde and Enrique Iglesias and Maria Torrente and Maria-Esther Vidal},
    doi = {10.57702/z26cs7i9},
    institution = {TIB},
    keyword = {'healthcare', 'knowledge graphs', 'machine learning'},
    month = {sep},
    publisher = {TIB},
    title = {Semantically Describing Predictive Models for Interpretable Insights into Lung Cancer Relapse},
    url = {https://ldm.kisski.de/dataset/semantically-describing-predictive-models-for-interpretable-insights-into-lung-cancer-relapse},
    year = {2024}
}