Explainable Machine Learning (XAI) for Survival in Bone Marrow Transplantation Trials: A Technical Report
Artificial intelligence is gaining interest among clinicians, but its results are difficult to be interpreted, especially when dealing with survival outcomes and censored observations. Explainable machine learning (XAI) has been recently extended to this context to improve explainability, interpreta...
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MDPI AG
2023-09-01
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| Series: | BioMedInformatics |
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| Online Access: | https://www.mdpi.com/2673-7426/3/3/48 |
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| author | Roberto Passera Sofia Zompi Jessica Gill Alessandro Busca |
| author_facet | Roberto Passera Sofia Zompi Jessica Gill Alessandro Busca |
| author_sort | Roberto Passera |
| collection | DOAJ |
| description | Artificial intelligence is gaining interest among clinicians, but its results are difficult to be interpreted, especially when dealing with survival outcomes and censored observations. Explainable machine learning (XAI) has been recently extended to this context to improve explainability, interpretability and transparency for modeling results. A cohort of 231 patients undergoing an allogeneic bone marrow transplantation was analyzed by XAI for survival by two different uni- and multi-variate survival models, proportional hazard regression and random survival forest, having as the main outcome the overall survival (OS) and its main determinants, using the survex package for R. Both models’ performances were investigated using the integrated Brier score, the integrated Cumulative/Dynamic AUC and the concordance C-index. Global explanation for the whole cohort was performed using the time-dependent variable importance and the partial dependence survival plot. The local explanation for each single patient was obtained via the SurvSHAP(t) and SurvLIME plots and the ceteris paribus survival profile. The survex package common interface ensured a good feasibility of XAI for survival, and the advanced graphical options allowed us to easily explore, explain and compare OS results coming from the two survival models. Before the modeling results to be suitable for clinical use, understandability, clinical relevance and computational efficiency were the most important criteria ensured by this XAI for survival approach, in adherence to clinical XAI guidelines. |
| first_indexed | 2024-03-10T23:00:39Z |
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| id | doaj.art-bbc77e2ead814c70816c12c9244782fa |
| institution | Directory Open Access Journal |
| issn | 2673-7426 |
| language | English |
| last_indexed | 2024-03-10T23:00:39Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | BioMedInformatics |
| spelling | doaj.art-bbc77e2ead814c70816c12c9244782fa2023-11-19T09:43:40ZengMDPI AGBioMedInformatics2673-74262023-09-013375276810.3390/biomedinformatics3030048Explainable Machine Learning (XAI) for Survival in Bone Marrow Transplantation Trials: A Technical ReportRoberto Passera0Sofia Zompi1Jessica Gill2Alessandro Busca3Department of Medical Sciences, Division of Nuclear Medicine, University of Torino, Corso AM Dogliotti 18, 10126 Torino, ItalyBone Marrow Transplantation Unit, Department of Oncology, Azienda Ospedaliera Universitaria Città della Salute e della Scienza di Torino, 10126 Torino, ItalyBone Marrow Transplantation Unit, Department of Oncology, Azienda Ospedaliera Universitaria Città della Salute e della Scienza di Torino, 10126 Torino, ItalyBone Marrow Transplantation Unit, Department of Oncology, Azienda Ospedaliera Universitaria Città della Salute e della Scienza di Torino, 10126 Torino, ItalyArtificial intelligence is gaining interest among clinicians, but its results are difficult to be interpreted, especially when dealing with survival outcomes and censored observations. Explainable machine learning (XAI) has been recently extended to this context to improve explainability, interpretability and transparency for modeling results. A cohort of 231 patients undergoing an allogeneic bone marrow transplantation was analyzed by XAI for survival by two different uni- and multi-variate survival models, proportional hazard regression and random survival forest, having as the main outcome the overall survival (OS) and its main determinants, using the survex package for R. Both models’ performances were investigated using the integrated Brier score, the integrated Cumulative/Dynamic AUC and the concordance C-index. Global explanation for the whole cohort was performed using the time-dependent variable importance and the partial dependence survival plot. The local explanation for each single patient was obtained via the SurvSHAP(t) and SurvLIME plots and the ceteris paribus survival profile. The survex package common interface ensured a good feasibility of XAI for survival, and the advanced graphical options allowed us to easily explore, explain and compare OS results coming from the two survival models. Before the modeling results to be suitable for clinical use, understandability, clinical relevance and computational efficiency were the most important criteria ensured by this XAI for survival approach, in adherence to clinical XAI guidelines.https://www.mdpi.com/2673-7426/3/3/48artificial intelligencemachine learningexplainable machine learning (XAI)shapley additive explanations (SHAP)local interpretable model-agnostic explanations (LIME)partial dependence profiles (PDP) |
| spellingShingle | Roberto Passera Sofia Zompi Jessica Gill Alessandro Busca Explainable Machine Learning (XAI) for Survival in Bone Marrow Transplantation Trials: A Technical Report BioMedInformatics artificial intelligence machine learning explainable machine learning (XAI) shapley additive explanations (SHAP) local interpretable model-agnostic explanations (LIME) partial dependence profiles (PDP) |
| title | Explainable Machine Learning (XAI) for Survival in Bone Marrow Transplantation Trials: A Technical Report |
| title_full | Explainable Machine Learning (XAI) for Survival in Bone Marrow Transplantation Trials: A Technical Report |
| title_fullStr | Explainable Machine Learning (XAI) for Survival in Bone Marrow Transplantation Trials: A Technical Report |
| title_full_unstemmed | Explainable Machine Learning (XAI) for Survival in Bone Marrow Transplantation Trials: A Technical Report |
| title_short | Explainable Machine Learning (XAI) for Survival in Bone Marrow Transplantation Trials: A Technical Report |
| title_sort | explainable machine learning xai for survival in bone marrow transplantation trials a technical report |
| topic | artificial intelligence machine learning explainable machine learning (XAI) shapley additive explanations (SHAP) local interpretable model-agnostic explanations (LIME) partial dependence profiles (PDP) |
| url | https://www.mdpi.com/2673-7426/3/3/48 |
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