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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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Related Experiment Video

Updated: Jun 25, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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Towards proactive palliative care in oncology: developing an explainable EHR-based machine learning model for

Qingyuan Zhuang1,2, Alwin Yaoxian Zhang3, Ryan Shea Tan Ying Cong4,5

  • 1Division of Supportive and Palliative Care, National Cancer Centre Singapore, 30 Hospital Blvd, Singapore, 168583, Singapore. zhuang.qingyuan@singhealth.com.sg.

BMC Palliative Care
|May 20, 2024
PubMed
Summary

This study developed an explainable machine learning model using electronic health records to predict 365-day mortality risk in advanced cancer patients, facilitating proactive palliative care. The model showed strong performance in identifying patients needing supportive care.

Keywords:
Clinical decision support systemsElectronic Health RecordsMachine learningOncologyPalliative Medicine

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Area of Science:

  • Oncology
  • Medical Informatics
  • Machine Learning

Background:

  • Early identification of end-of-life is crucial for proactive palliative care.
  • Machine learning (ML) models using electronic health records (EHR) show promise for cancer prognostication.
  • Existing models often lack performance transparency, clinical alignment, and explainability, hindering adoption.

Purpose of the Study:

  • To develop an explainable ML model using EHR data to predict 365-day mortality risk in advanced cancer patients.
  • To facilitate the early integration of palliative care services in outpatient settings.
  • To enhance clinical trust and adoption through model transparency and explainability.

Main Methods:

  • A cohort of 5,926 advanced cancer patients (Stage 3-4 solid organ) was analyzed.
  • Extreme Gradient Boosting (XGBoost) was employed to predict 365-day mortality from outpatient EHR data.
  • Shapley Additive Explanations (SHAP) were used for model interpretability, with performance assessed by AUROC, AUPRC, and Brier score.

Main Results:

  • The model achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.861 and an Area Under the Precision-Recall Curve (AUPRC) of 0.771.
  • A Brier score of 0.147 indicated slight overestimation of mortality risk.
  • SHAP values provided global and individual feature impact visualizations.

Conclusions:

  • The developed ML model effectively predicts 365-day mortality in advanced cancer patients.
  • The model demonstrates strong discrimination and precision-recall, supporting personalized prognostication.
  • This tool can aid in the earlier integration of palliative care, improving patient outcomes.