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SurvSHAP: A Proxy-Based Algorithm for Explaining Survival Models with SHAP
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0001-9416-5647
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR). RISE Research Institutes of Sweden.ORCID iD: 0000-0003-3272-4145
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0001-5163-2997
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR). Lund University, Lund, Sweden.ORCID iD: 0000-0003-1145-4297
2022 (English)In: 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA) / [ed] Joshua Zhexue Huang; Yi Pan; Barbara Hammer; Muhammad Khurram Khan; Xing Xie; Laizhong Cui; Yulin He, Piscataway, NJ: IEEE, 2022Conference paper, Published paper (Refereed)
Abstract [en]

Survival Analysis models usually output functions (survival or hazard functions) rather than point predictions like regression and classification models. This makes the explanations of such models a challenging task, especially using the Shapley values. We propose SurvSHAP, a new model-agnostic algorithm to explain survival models that predict survival curves. The algorithm is based on discovering patterns in the predicted survival curves, the output of the survival model, that would identify significantly different survival behaviors, and utilizing a proxy model and SHAP method to explain these distinct survival behaviors. Experiments on synthetic and real datasets demonstrate that the SurvSHAP is able to capture the underlying factors of the survival patterns. Moreover, SurvSHAP results on the Cox Proportional Hazard model are compared with the weights of the model to show that we provide faithful overall explanations, with more fine-grained explanations of the sub-populations. We also illustrate the wrong model and explanations learned by a Cox model when applied to heterogeneous sub-populations. We show that a non-linear machine learning survival model with SurvSHAP can better model the data and provide better explanations than linear models.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2022.
Keywords [en]
SurvSHAP, Explainable AI, Survival Patterns, SHAP, Shapley values, Proxy Model, Survival Analysis, Machine Learning
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-49149DOI: 10.1109/DSAA54385.2022.10032392ISI: 000967751000099Scopus ID: 2-s2.0-85148538187ISBN: 978-1-6654-7330-9 (electronic)ISBN: 978-1-6654-7331-6 (print)OAI: oai:DiVA.org:hh-49149DiVA, id: diva2:1736098
Conference
The 9th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2022), Shenzhen, China, October 13-16, 2022
Part of project
eXplainable Predictive Maintenance, Swedish Research Council
Funder
Knowledge Foundation
Note

Funding: This research was funded by the CHIST-ERA grant CHIST-ERA-19-XAI-012 and CAISR+ project funded by the Swedish Knowledge Foundation.

Som manuscript i avhandling/As manuscript in thesis.

Available from: 2023-02-10 Created: 2023-02-10 Last updated: 2023-12-19Bibliographically approved
In thesis
1. Machine Learning Survival Models: Performance and Explainability
Open this publication in new window or tab >>Machine Learning Survival Models: Performance and Explainability
2023 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Survival analysis is an essential statistics and machine learning field in various critical applications like medical research and predictive maintenance. In these domains understanding models' predictions is paramount. While machine learning techniques are increasingly applied to enhance the predictive performance of survival models, they simultaneously sacrifice transparency and explainability. 

Survival models, in contrast to regular machine learning models, predict functions rather than point estimates like regression and classification models. This creates a challenge regarding explaining such models using the known off-the-shelf machine learning explanation techniques, like Shapley Values, Counterfactual examples, and others.   

Censoring is also a major issue in survival analysis where the target time variable is not fully observed for all subjects. Moreover, in predictive maintenance settings, recorded events do not always map to actual failures, where some components could be replaced because it is considered faulty or about to fail in the future based on an expert's opinion. Censoring and noisy labels create problems in terms of modeling and evaluation that require to be addressed during the development and evaluation of the survival models.

Considering the challenges in survival modeling and the differences from regular machine learning models, this thesis aims to bridge this gap by facilitating the use of machine learning explanation methods to produce plausible and actionable explanations for survival models. It also aims to enhance survival modeling and evaluation revealing a better insight into the differences among the compared survival models.

In this thesis, we propose two methods for explaining survival models which rely on discovering survival patterns in the model's predictions that group the studied subjects into significantly different survival groups. Each pattern reflects a specific survival behavior common to all the subjects in their respective group. We utilize these patterns to explain the predictions of the studied model in two ways. In the first, we employ a classification proxy model that can capture the relationship between the descriptive features of subjects and the learned survival patterns. Explaining such a proxy model using Shapley Values provides insights into the feature attribution of belonging to a specific survival pattern. In the second method, we addressed the "what if?" question by generating plausible and actionable counterfactual examples that would change the predicted pattern of the studied subject. Such counterfactual examples provide insights into actionable changes required to enhance the survivability of subjects.

We also propose a variational-inference-based generative model for estimating the time-to-event distribution. The model relies on a regression-based loss function with the ability to handle censored cases. It also relies on sampling for estimating the conditional probability of event times. Moreover, we propose a decomposition of the C-index into a weighted harmonic average of two quantities, the concordance among the observed events and the concordance between observed and censored cases. These two quantities, weighted by a factor representing the balance between the two, can reveal differences between survival models previously unseen using only the total Concordance index. This can give insight into the performances of different models and their relation to the characteristics of the studied data.

Finally, as part of enhancing survival modeling, we propose an algorithm that can correct erroneous event labels in predictive maintenance time-to-event data. we adopt an expectation-maximization-like approach utilizing a genetic algorithm to find better labels that would maximize the survival model's performance. Over iteration, the algorithm builds confidence about events' assignments which improves the search in the following iterations until convergence.

We performed experiments on real and synthetic data showing that our proposed methods enhance the performance in survival modeling and can reveal the underlying factors contributing to the explainability of survival models' behavior and performance.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2023. p. 25
Series
Halmstad University Dissertations ; 108
Keywords
Survival Analysis, Explainable Artificial Intelligence, Survival Patterns, Counterfactual Explanations, Evaluation Metrics, Concordance Index
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-52269 (URN)978-91-89587-30-4 (ISBN)978-91-89587-29-8 (ISBN)
Presentation
2024-01-18, Wigforss, Hus J, Kristan IV:s väg 3, Halmstad, 09:00 (English)
Opponent
Supervisors
Available from: 2023-12-19 Created: 2023-12-18 Last updated: 2024-02-01Bibliographically approved

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Alabdallah, AbdallahPashami, SepidehRögnvaldsson, ThorsteinnOhlsson, Mattias

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