Related Experiment Video
Updated: May 28, 2026
07:13
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
Published on: April 18, 2025
Machine Learning Risk Stratification Approach Using Patient-Reported Outcomes for Forecasting Unplanned Health Care
Akina Natori1, Jerry R Bonnell2, Vasileios Stathias3,4
1Division of Medical Oncology, Department of Medicine, University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, Coral Gables, FL.
JCO Clinical Cancer Informatics
|May 26, 2026
Summary
Integrating patient-reported outcomes with electronic health records improves cancer survivorship risk prediction. Machine learning models using dynamic data windows better forecast health care use and symptom burden, enabling proactive care.
Area of Science:
- Oncology
- Health Informatics
- Machine Learning
Background:
- Cancer survivorship care presents challenges in risk stratification due to complex longitudinal data.
- Multimodal data, including patient-reported outcomes (PROs) and electronic health records (EHRs), are crucial for effective prediction.
Purpose of the Study:
- To evaluate the added value of integrating PROs with EHR data for predicting adverse cancer survivorship outcomes.
- To identify optimal temporal windowing strategies for machine learning models in survivorship risk prediction.
Main Methods:
- A cohort of 25,592 cancer survivors was analyzed over 36 months.
- Data included baseline measures, treatments, PROs, and healthcare utilization.
- LASSO and CATBOOST models were applied with various temporal representations (static, cumulative, sliding windows) to predict monthly healthcare utilization and symptom burden.
Main Results:
- CATBOOST models with time-windowed predictors improved healthcare utilization prediction by 27% (AP = 0.207).
- PRO integration nearly doubled performance for symptom burden prediction (AP = 0.132 vs. 0.071).
- Top 10% of high-risk patients captured over 50% of utilization and symptom burden events.
Conclusions:
- Cancer survivorship risk is dynamic and outcome-specific, requiring tailored prediction approaches.
- Decoupled, dynamic temporal windows offer a flexible framework for precision-based survivorship care.
- Integrating PROs and EHRs with advanced machine learning enhances risk stratification.
Related Concept Videos
01:21Cancer Survival Analysis
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
01:24Kaplan-Meier Approach
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,...
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
01:20Actuarial Approach
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
01:20Comparing the Survival Analysis of Two or More Groups
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...