An evaluation of machine learning techniques to predict the outcome of children treated for Hodgkin-Lymphoma on the AHOD0031 trial Journal Article
| Authors: | Beaulac, C.; Rosenthal, J. S.; Pei, Q.; Friedman, D.; Wolden, S.; Hodgson, D. |
| Article Title: | An evaluation of machine learning techniques to predict the outcome of children treated for Hodgkin-Lymphoma on the AHOD0031 trial |
| Abstract: | In this manuscript, we analyze a data set containing information on children with Hodgkin Lymphoma (HL) enrolled on a clinical trial. Treatments received and survival status were collected together with other covariates such as demographics and clinical measurements. Our main task is to explore the potential of machine learning (ML) algorithms in a survival analysis context in order to improve over the Cox Proportional Hazard (CoxPH) model. We discuss the weaknesses of the CoxPH model we would like to improve upon and then we introduce multiple algorithms, from well-established ones to state-of-the-art models, that solve these issues. We then compare every model according to the concordance index and the Brier score. Finally, we produce a series of recommendations, based on our experience, for practitioners that would like to benefit from the recent advances in artificial intelligence. © 2020 Taylor & Francis. |
| Keywords: | survival analysis; clinical trial; oncology; proportional hazards; concordance index; machine learning; cobalt compounds; machine learning techniques; state of the art; phosphorus compounds; clinical measurements; multiple algorithms |
| Journal Title: | Applied Artificial Intelligence |
| Volume: | 34 |
| Issue: | 14 |
| ISSN: | 0883-9514 |
| Publisher: | Taylor & Francis Group |
| Date Published: | 2020-01-01 |
| Start Page: | 1100 |
| End Page: | 1114 |
| Language: | English |
| DOI: | 10.1080/08839514.2020.1815151 |
| PROVIDER: | scopus |
| PMCID: | PMC7963212 |
| PUBMED: | 33731974 |
| DOI/URL: | |
| Notes: | Article -- Export Date: 1 December 2020 -- Source: Scopus |
