Interpretable Survival Analysis with Feature Selection: A Novel Machine Learning Approach

Alapfogalmak

DyS, a novel glass-box machine learning model, achieves competitive discriminative performance for survival analysis while providing inherent interpretability through feature importance and feature impact plots. DyS can also perform feature selection during the model fitting process, yielding improved interpretability for datasets with many features.

Kivonat

The paper presents DyS, a new glass-box machine learning model for survival analysis. Key highlights:

1. DyS is designed to be both interpretable and performant, providing explanations inherently due to the model structure. It can generate feature importances as well as feature impact plots at specific evaluation times.

2. DyS can perform feature selection during the model fitting process, both on the main effects and on the interaction terms. This allows DyS to generate feature-sparse interpretable predictions, without requiring separate feature selection as a preprocessing step.

3. DyS uses a two-stage fitting approach, which, when combined with feature-sparsity, allows it to scale to large survival analysis problems where other approaches are either too slow or require separate feature selection.

4. Empirical results on benchmark survival analysis datasets demonstrate that DyS is competitive with state-of-the-art survival models in terms of discrimination, while being highly interpretable. On a large-scale heart failure prediction task, DyS outperforms other methods when feature selection is required.

Statisztikák

Smaller datasets (flchain, metabric, mimic, support): DyS achieves mean AUC scores ranging from 0.669 to 0.951, competitive with or outperforming state-of-the-art survival models.
Larger heart failure dataset (2410 features, 670,000 patients): DyS selects 45-65 features and achieves mean AUC of 0.826, compared to 0.829 for the best baseline.
When selecting exactly 10 features, DyS achieves mean AUC of 0.799, outperforming all other 10-feature baselines.

Idézetek

"DyS is a glass-box machine learning method, providing explanations inherently due to the model structure."
"DyS can automatically select features during fitting, yielding improved interpretability for datasets with many features without requiring separate feature selection as preprocessing."
"DyS can handle large datasets: for example, a dataset with 2000 features and 500,000 samples can be fit in less than an hour with 1 GPU and 30 GB of RAM."

Főbb Kivonatok

Interpretable Prediction and Feature Selection for Survival Analysis

by Mike Van Nes... : arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.14689.pdf

Mélyebb kérdések

How can the feature selection capabilities of DyS be extended to handle more complex feature interactions, such as higher-order interactions or non-linear interactions?

DyS currently uses a two-stage fitting approach for feature selection, where main effects are fitted first and then interactions between active main effects are considered. To handle more complex feature interactions, DyS can be extended in the following ways:

Higher-Order Interactions: To incorporate higher-order interactions, DyS can be modified to include interactions between more than two features. This would involve expanding the interaction terms in the model to capture interactions between multiple features simultaneously. By allowing for higher-order interactions, DyS can better capture complex relationships between features.

Non-Linear Interactions: DyS can be enhanced to accommodate non-linear interactions by using non-linear shape functions in the model. Instead of assuming linear relationships between features, non-linear functions such as polynomials, splines, or neural networks can be used to capture more intricate interactions. This flexibility in modeling non-linear relationships can improve the model's ability to capture complex feature interactions.

Regularization Techniques: Incorporating regularization techniques specific to handling complex interactions, such as group lasso or elastic net, can help in selecting relevant features and interactions while penalizing unnecessary complexity. These regularization methods can encourage sparsity in the model while allowing for the inclusion of higher-order or non-linear interactions as needed.

Automated Feature Engineering: Implementing automated feature engineering techniques, such as feature crossing or feature transformation, can help create new features that capture complex interactions between existing features. By generating new features based on combinations of existing ones, DyS can capture more intricate relationships in the data.

By extending DyS to handle higher-order interactions, non-linear relationships, and incorporating advanced regularization techniques and automated feature engineering, the model's feature selection capabilities can be enhanced to capture more complex feature interactions in survival analysis tasks.

How could the potential limitations of the two-stage fitting approach used in DyS be further improved or generalized?

The two-stage fitting approach used in DyS, where main effects are fitted first followed by interactions, has certain limitations that can be addressed for further improvement and generalization:

Efficiency: One limitation of the two-stage fitting approach is the potential increase in computational complexity, especially for datasets with a large number of features and interactions. To improve efficiency, parallel processing techniques or distributed computing frameworks can be utilized to speed up the fitting process and handle larger datasets more effectively.

Scalability: The two-stage fitting approach may face scalability challenges when dealing with extremely large datasets or a high number of features. Implementing optimization strategies tailored for scalability, such as mini-batch training or online learning, can help DyS handle massive datasets without compromising performance.

Interaction Screening: While the two-stage fitting approach focuses on interactions between active main effects, incorporating more sophisticated interaction screening methods can enhance the model's ability to identify relevant interactions efficiently. Techniques like permutation importance or SHAP values can be integrated to prioritize interactions for inclusion in the model.

Flexibility: To generalize the two-stage fitting approach, DyS can be extended to adaptively adjust the number of stages based on the dataset characteristics. For instance, for datasets with a small number of features, a single-stage fitting approach may be more suitable, while for high-dimensional datasets, a multi-stage fitting strategy can be employed.

Regularization: Introducing additional regularization techniques during the two-stage fitting process can help prevent overfitting and improve the generalization ability of DyS. Incorporating regularization terms specific to main effects and interactions can enhance the model's robustness and stability.

By addressing these limitations through improved efficiency, scalability, interaction screening, flexibility in the fitting process, and enhanced regularization, the two-stage fitting approach in DyS can be further refined and generalized for a wider range of survival analysis applications.

Given the interpretability of DyS, how could its insights be leveraged to drive new scientific discoveries or hypotheses in the domain of survival analysis, beyond just predictive performance?

The interpretability of DyS offers valuable insights that can be leveraged to drive new scientific discoveries and hypotheses in the domain of survival analysis:

Identifying Novel Risk Factors: By analyzing the feature importances and impact plots provided by DyS, researchers can uncover previously unrecognized risk factors associated with the event of interest. These insights can lead to the discovery of new biomarkers, lifestyle factors, or clinical variables that significantly impact survival outcomes.

Understanding Feature Relationships: DyS's ability to visualize how changes in individual features impact the model's predictions at different evaluation times can help researchers understand the complex relationships between variables. By exploring feature interactions and their effects on survival probabilities, new hypotheses about the underlying mechanisms of the event can be formulated.

Temporal Analysis: DyS's time-dependent predictions and feature impact plots enable researchers to conduct temporal analysis of survival data. By examining how feature effects evolve over time, researchers can gain insights into the dynamic nature of risk factors and their varying impacts on survival outcomes at different stages of the event progression.

Subgroup Analysis: DyS's interpretability can facilitate subgroup analysis by identifying distinct patterns of feature effects within different subpopulations. Researchers can explore how certain features influence survival outcomes in specific subgroups, leading to the discovery of personalized treatment strategies or interventions tailored to different patient profiles.

Validation of Existing Theories: DyS can be used to validate existing theories or hypotheses in survival analysis by providing transparent and interpretable model explanations. Researchers can confirm the relevance of known risk factors or prognostic indicators and explore how these factors interact to influence survival probabilities.

By leveraging DyS's interpretability to uncover new risk factors, understand feature relationships, conduct temporal analysis, explore subgroups, and validate existing theories, researchers can drive new scientific discoveries and generate hypotheses that advance the field of survival analysis beyond traditional predictive performance metrics. DyS's transparent and explainable nature empowers researchers to extract meaningful insights from survival data and contribute to the development of innovative approaches for predicting and understanding time-to-event outcomes.