Uncertainty-Aware Predictive Process Monitoring in Healthcare: Explainable Insights into Probability Calibration for Conformal Prediction
In high-stakes decision-making environments, predictive models must deliver not only high accuracy but also reliable uncertainty estimations and transparent explanations. This study explores the integration of probability calibration techniques with Conformal Prediction (CP) within a predictive proc...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/14/7925 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1839616599367090176 |
|---|---|
| author | Maxim Majlatow Fahim Ahmed Shakil Andreas Emrich Nijat Mehdiyev |
| author_facet | Maxim Majlatow Fahim Ahmed Shakil Andreas Emrich Nijat Mehdiyev |
| author_sort | Maxim Majlatow |
| collection | DOAJ |
| description | In high-stakes decision-making environments, predictive models must deliver not only high accuracy but also reliable uncertainty estimations and transparent explanations. This study explores the integration of probability calibration techniques with Conformal Prediction (CP) within a predictive process monitoring (PPM) framework tailored to healthcare analytics. CP is renowned for its distribution-free prediction regions and formal coverage guarantees under minimal assumptions; however, its practical utility critically depends on well-calibrated probability estimates. We compare a range of post-hoc calibration methods—including parametric approaches like Platt scaling and Beta calibration, as well as non-parametric techniques such as Isotonic Regression and Spline calibration—to assess their impact on aligning raw model outputs with observed outcomes. By incorporating these calibrated probabilities into the CP framework, our multilayer analysis evaluates improvements in prediction region validity, including tighter coverage gaps and reduced minority error contributions. Furthermore, we employ SHAP-based explainability to explain how calibration influences feature attribution for both high-confidence and ambiguous predictions. Experimental results on process-driven healthcare data indicate that the integration of calibration with CP not only enhances the statistical robustness of uncertainty estimates but also improves the interpretability of predictions, thereby supporting safer and robust clinical decision-making. |
| format | Article |
| id | doaj-art-c834e77a92934c419c18c34635e3d38c |
| institution | Matheson Library |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-c834e77a92934c419c18c34635e3d38c2025-07-25T13:12:44ZengMDPI AGApplied Sciences2076-34172025-07-011514792510.3390/app15147925Uncertainty-Aware Predictive Process Monitoring in Healthcare: Explainable Insights into Probability Calibration for Conformal PredictionMaxim Majlatow0Fahim Ahmed Shakil1Andreas Emrich2Nijat Mehdiyev3German Research Center for Artificial Intelligence (DFKI), 66123 Saarbrücken, GermanyGerman Research Center for Artificial Intelligence (DFKI), 66123 Saarbrücken, GermanyGerman Research Center for Artificial Intelligence (DFKI), 66123 Saarbrücken, GermanyGerman Research Center for Artificial Intelligence (DFKI), 66123 Saarbrücken, GermanyIn high-stakes decision-making environments, predictive models must deliver not only high accuracy but also reliable uncertainty estimations and transparent explanations. This study explores the integration of probability calibration techniques with Conformal Prediction (CP) within a predictive process monitoring (PPM) framework tailored to healthcare analytics. CP is renowned for its distribution-free prediction regions and formal coverage guarantees under minimal assumptions; however, its practical utility critically depends on well-calibrated probability estimates. We compare a range of post-hoc calibration methods—including parametric approaches like Platt scaling and Beta calibration, as well as non-parametric techniques such as Isotonic Regression and Spline calibration—to assess their impact on aligning raw model outputs with observed outcomes. By incorporating these calibrated probabilities into the CP framework, our multilayer analysis evaluates improvements in prediction region validity, including tighter coverage gaps and reduced minority error contributions. Furthermore, we employ SHAP-based explainability to explain how calibration influences feature attribution for both high-confidence and ambiguous predictions. Experimental results on process-driven healthcare data indicate that the integration of calibration with CP not only enhances the statistical robustness of uncertainty estimates but also improves the interpretability of predictions, thereby supporting safer and robust clinical decision-making.https://www.mdpi.com/2076-3417/15/14/7925conformal predictionexplainable artificial intelligenceprobability calibrationpredictive process monitoring |
| spellingShingle | Maxim Majlatow Fahim Ahmed Shakil Andreas Emrich Nijat Mehdiyev Uncertainty-Aware Predictive Process Monitoring in Healthcare: Explainable Insights into Probability Calibration for Conformal Prediction Applied Sciences conformal prediction explainable artificial intelligence probability calibration predictive process monitoring |
| title | Uncertainty-Aware Predictive Process Monitoring in Healthcare: Explainable Insights into Probability Calibration for Conformal Prediction |
| title_full | Uncertainty-Aware Predictive Process Monitoring in Healthcare: Explainable Insights into Probability Calibration for Conformal Prediction |
| title_fullStr | Uncertainty-Aware Predictive Process Monitoring in Healthcare: Explainable Insights into Probability Calibration for Conformal Prediction |
| title_full_unstemmed | Uncertainty-Aware Predictive Process Monitoring in Healthcare: Explainable Insights into Probability Calibration for Conformal Prediction |
| title_short | Uncertainty-Aware Predictive Process Monitoring in Healthcare: Explainable Insights into Probability Calibration for Conformal Prediction |
| title_sort | uncertainty aware predictive process monitoring in healthcare explainable insights into probability calibration for conformal prediction |
| topic | conformal prediction explainable artificial intelligence probability calibration predictive process monitoring |
| url | https://www.mdpi.com/2076-3417/15/14/7925 |
| work_keys_str_mv | AT maximmajlatow uncertaintyawarepredictiveprocessmonitoringinhealthcareexplainableinsightsintoprobabilitycalibrationforconformalprediction AT fahimahmedshakil uncertaintyawarepredictiveprocessmonitoringinhealthcareexplainableinsightsintoprobabilitycalibrationforconformalprediction AT andreasemrich uncertaintyawarepredictiveprocessmonitoringinhealthcareexplainableinsightsintoprobabilitycalibrationforconformalprediction AT nijatmehdiyev uncertaintyawarepredictiveprocessmonitoringinhealthcareexplainableinsightsintoprobabilitycalibrationforconformalprediction |