Empirical comparison of routinely collected electronic health record data for head and neck cancer-specific survival in machine-learnt prognostic models.

ABSTRACT:

Background

Knowledge of the prognostic factors and performance of machine learning predictive models for 2-year cancer-specific survival (CSS) is limited in the head and neck cancer (HNC) population.

Methods

Data from our facilities' oncology information system (OIS) collected for routine practice (OIS dataset, n = 430 patients) and research purposes (research dataset, n = 529 patients) were extracted on adults diagnosed between 2000 and 2017 with squamous cell carcinoma of the head and neck.

Results

Machine learning demonstrated excellent performance (area under the curve, AUC) in the whole cohort (AUC = 0.97, research dataset), larynx cohort (AUC = 0.98, both datasets), and oropharynx cohort (AUC = 0.99, both datasets). Tumor site and T classification were identifie

SUBMITTER: Kotevski DP 

PROVIDER: S-EPMC10100433 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature