โก Quick Summary
This study utilized machine learning to analyze electronic health record (EHR) data from 8,976 cancer patients, aiming to predict mortality dynamics through pre-death events. The findings revealed three distinct clinical patterns, highlighting the importance of key laboratory parameters such as albumin and C-reactive protein in understanding terminal disease progression.
๐ Key Details
- ๐ Dataset: 8,976 cancer patients, 77 laboratory parameters
- ๐งฉ Features used: Laboratory values including albumin, C-reactive protein, blood urea nitrogen, and lactate dehydrogenase
- โ๏ธ Technology: Gradient-boosting decision trees, SHAP for feature analysis
- ๐ Key findings: Identification of three distinct clinical patterns in patients nearing death
๐ Key Takeaways
- ๐ Machine learning can effectively predict mortality dynamics in cancer patients.
- ๐ก SHAP analysis provides insights into the contribution of individual features over time.
- ๐ฉโ๐ฌ Three distinct clinical patterns were identified in patients approaching death.
- ๐ฅ Key laboratory parameters such as albumin and C-reactive protein are critical in understanding disease progression.
- ๐ Traditional stratification methods may overlook important variations in terminal disease progression.
- ๐ Temporal analysis reveals clinically meaningful state transitions.
- ๐ Potential for personalized risk stratification and optimized end-of-life care strategies.
๐ Background
Understanding the dynamics of patients’ internal states as they approach death from cancer is crucial for improving end-of-life care. Traditional methods often focus on isolated test values or organ dysfunction markers, which may not capture the full picture of a patient’s evolving condition. This study aims to bridge that gap by employing advanced machine learning techniques to analyze comprehensive EHR data.
๐๏ธ Study
Conducted at a single institution, this study analyzed EHR data from 8,976 cancer patients to construct continuous mortality prediction models. By utilizing gradient-boosting decision trees and SHAP for feature analysis, researchers aimed to uncover the temporal dynamics of pre-death events and classify patients into distinct subtypes based on their mortality-related feature dynamics.
๐ Results
The analysis identified three distinct clinical patterns in patients nearing death, with key laboratory parameters such as albumin, C-reactive protein, blood urea nitrogen, and lactate dehydrogenase playing a significant role. Dimensionality reduction techniques demonstrated that SHAP-based patient stratification effectively captured hidden variations in terminal disease progression, which traditional methods failed to do.
๐ Impact and Implications
The findings from this study have the potential to transform how we approach end-of-life care for cancer patients. By leveraging machine learning-driven temporal analysis, healthcare providers can gain deeper insights into the heterogeneous nature of terminal disease progression. This could lead to enhanced personalized risk stratification and more targeted interventions, ultimately improving patient outcomes during critical end-of-life stages.
๐ฎ Conclusion
This study highlights the remarkable potential of machine learning in predicting mortality dynamics in cancer patients. By utilizing advanced analytical techniques, we can uncover clinically meaningful insights that traditional methods may overlook. The future of personalized end-of-life care looks promising, and further research in this area is essential to fully realize these advancements.
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Predicting mortality dynamics in cancer patients: A machine learning approach to pre-death events.
Abstract
Capturing the dynamic changes in patients’ internal states as they approach death due to fatal diseases remains a major challenge in understanding individual pathologies and improving end-of-life care. However, existing methods primarily focus on specific test values or organ dysfunction markers, failing to provide a comprehensive view of the evolving internal state preceding death. To address this, we analyzed electronic health record (EHR) data from a single institution, including 8,976 cancer patients and 77 laboratory parameters, by constructing continuous mortality prediction models based on gradient-boosting decision trees and leveraging them for temporal analyses. We applied Shapley Additive exPlanations (SHAP) to assess the contribution of individual features over time and employed a SHAP-based clustering approach to classify patients into distinct subtypes based on mortality-related feature dynamics. Our analysis identified three distinct clinical patterns in patients near death, with key laboratory parameters-including albumin, C-reactive protein, blood urea nitrogen, and lactate dehydrogenase-playing a critical role. Dimensionality reduction techniques demonstrated that SHAP-based patient stratification effectively captured hidden variations in terminal disease progression, whereas traditional stratification using raw laboratory values failed to do so. These findings suggest that machine learning-driven temporal analysis can reveal clinically meaningful state transitions that conventional approaches overlook, offering new insights into the heterogeneous nature of terminal disease progression. This framework has the potential to enhance personalized risk stratification and optimize individualized end-of-life care strategies by identifying distinct patient trajectories that may inform more targeted interventions.
Author: [‘Yamamoto T’, ‘Sakuragi M’, ‘Tuji Y’, ‘Okamoto Y’, ‘Uchino E’, ‘Yanagita M’, ‘Muto M’, ‘Kamada M’, ‘Okuno Y’]
Journal: PLoS One
Citation: Yamamoto T, et al. Predicting mortality dynamics in cancer patients: A machine learning approach to pre-death events. Predicting mortality dynamics in cancer patients: A machine learning approach to pre-death events. 2025; 20:e0331650. doi: 10.1371/journal.pone.0331650