Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development
Abstract Despite increasing interest in using Artificial Intelligence (AI) and Machine Learning (ML) models for drug development, effectively interpreting their predictions remains a challenge, which limits their impact on clinical decisions. We address this issue by providing a practical guide to S...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-11-01
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| Series: | Clinical and Translational Science |
| Online Access: | https://doi.org/10.1111/cts.70056 |
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| _version_ | 1850144726647308288 |
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| author | Ana Victoria Ponce‐Bobadilla Vanessa Schmitt Corinna S. Maier Sven Mensing Sven Stodtmann |
| author_facet | Ana Victoria Ponce‐Bobadilla Vanessa Schmitt Corinna S. Maier Sven Mensing Sven Stodtmann |
| author_sort | Ana Victoria Ponce‐Bobadilla |
| collection | DOAJ |
| description | Abstract Despite increasing interest in using Artificial Intelligence (AI) and Machine Learning (ML) models for drug development, effectively interpreting their predictions remains a challenge, which limits their impact on clinical decisions. We address this issue by providing a practical guide to SHapley Additive exPlanations (SHAP), a popular feature‐based interpretability method, which can be seamlessly integrated into supervised ML models to gain a deeper understanding of their predictions, thereby enhancing their transparency and trustworthiness. This tutorial focuses on the application of SHAP analysis to standard ML black‐box models for regression and classification problems. We provide an overview of various visualization plots and their interpretation, available software for implementing SHAP, and highlight best practices, as well as special considerations, when dealing with binary endpoints and time‐series models. To enhance the reader's understanding for the method, we also apply it to inherently explainable regression models. Finally, we discuss the limitations and ongoing advancements aimed at tackling the current drawbacks of the method. |
| format | Article |
| id | doaj-art-a531fbaf62f44e0aabb7b301a4749c92 |
| institution | OA Journals |
| issn | 1752-8054 1752-8062 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | Clinical and Translational Science |
| spelling | doaj-art-a531fbaf62f44e0aabb7b301a4749c922025-08-20T02:28:16ZengWileyClinical and Translational Science1752-80541752-80622024-11-011711n/an/a10.1111/cts.70056Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug developmentAna Victoria Ponce‐Bobadilla0Vanessa Schmitt1Corinna S. Maier2Sven Mensing3Sven Stodtmann4AbbVie Deutschland GmbH & Co. KG Ludwigshafen GermanyAbbVie Deutschland GmbH & Co. KG Ludwigshafen GermanyAbbVie Deutschland GmbH & Co. KG Ludwigshafen GermanyAbbVie Deutschland GmbH & Co. KG Ludwigshafen GermanyAbbVie Deutschland GmbH & Co. KG Ludwigshafen GermanyAbstract Despite increasing interest in using Artificial Intelligence (AI) and Machine Learning (ML) models for drug development, effectively interpreting their predictions remains a challenge, which limits their impact on clinical decisions. We address this issue by providing a practical guide to SHapley Additive exPlanations (SHAP), a popular feature‐based interpretability method, which can be seamlessly integrated into supervised ML models to gain a deeper understanding of their predictions, thereby enhancing their transparency and trustworthiness. This tutorial focuses on the application of SHAP analysis to standard ML black‐box models for regression and classification problems. We provide an overview of various visualization plots and their interpretation, available software for implementing SHAP, and highlight best practices, as well as special considerations, when dealing with binary endpoints and time‐series models. To enhance the reader's understanding for the method, we also apply it to inherently explainable regression models. Finally, we discuss the limitations and ongoing advancements aimed at tackling the current drawbacks of the method.https://doi.org/10.1111/cts.70056 |
| spellingShingle | Ana Victoria Ponce‐Bobadilla Vanessa Schmitt Corinna S. Maier Sven Mensing Sven Stodtmann Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development Clinical and Translational Science |
| title | Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development |
| title_full | Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development |
| title_fullStr | Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development |
| title_full_unstemmed | Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development |
| title_short | Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development |
| title_sort | practical guide to shap analysis explaining supervised machine learning model predictions in drug development |
| url | https://doi.org/10.1111/cts.70056 |
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