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Related Experiment Video

Updated: May 24, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Machine Learning Models for Predicting Mortality Risk and Survival Time in Lung Cancer Patients Treated with

Van Thuan Nguyen1, Ngoc Hoang Le2, Nhu Quynh Phan1

  • 1International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning models accurately predict patient mortality and survival time using electronic health record (EHR) data. These prognostic models, developed from multi-institution data, can enhance clinical decision-making.

Keywords:
EGFR-TKIsclassificationclinical datalung cancermachine learningmulti-centerregression

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Last Updated: May 24, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Area of Science:

  • * Medical Informatics
  • * Machine Learning in Healthcare
  • * Clinical Prognostics

Background:

  • * Electronic Health Records (EHR) contain vast patient data valuable for clinical research.
  • * Accurate prediction of patient mortality and survival time is crucial for effective healthcare management.
  • * Previous prognostic models often lack the scale and diversity of multi-institution EHR data.

Purpose of the Study:

  • * To develop and evaluate machine learning models for predicting patient mortality.
  • * To estimate patient survival time using time-to-event analysis.
  • * To assess the feasibility of using multi-institution EHR data for prognostic modeling.

Main Methods:

  • * Development of classification models for binary mortality risk prediction.
  • * Construction of survival models for time-to-event analysis.
  • * Utilizing de-identified EHR data from three Taipei Medical University-affiliated hospitals.

Main Results:

  • * The best classification model achieved an Area Under the ROC Curve (AUC) of approximately 0.82.
  • * The optimal survival model demonstrated a concordance index (C-index) of approximately 0.66.
  • * Models showed significant accuracy in predicting patient outcomes.

Conclusions:

  • * Multi-institution EHR data effectively supports the development of accurate prognostic models.
  • * Combining classification and survival analysis enhances predictive capabilities.
  • * These machine learning models offer valuable tools for informing clinical decision-making and improving patient care.