Development and Validation of Novel Deep-Learning Models Using Multiple Data Types for Lung Cancer Survival.

ABSTRACT: A well-established lung-cancer-survival-prediction model that relies on multiple data types, multiple novel machine-learning algorithms, and external testing is absent in the literature. This study aims to address this gap and determine the critical factors of lung cancer survival. We selected non-small-cell lung cancer patients from a retrospective dataset of the Taipei Medical University Clinical Research Database and Taiwan Cancer Registry between January 2008 and December 2018. All patients were monitored from the index date of cancer diagnosis until the event of death. Variables, including demographics, comorbidities, medications, laboratories, and patient gene tests, were used. Nine machine-learning algorithms with various modes were used. The performance of the algorithms was measur

SUBMITTER: Hsu JC 

PROVIDER: S-EPMC9688689 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature