⌘+K

Pulse Explore Communities Researchers Journals

Home Communities Explore

July 1, 2024JAMIA OpenOpen Access

Using real-world electronic health record data to predict the development of 12 cancer-related symptoms in the context of multimorbidity

Anindita Bandyopadhyay Alaa Albashayreh Nahid Zeinali Weiguo Fan Stephanie Gilbertson‐White

View Full Paper

Ask AI

Bookmark

Share

Abstract

This study uses electronic health record (EHR) data to predict 12 common cancer symptoms, assessing the efficacy of machine learning (ML) models in identifying symptom influencers.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Bandyopadhyay et al. (2024) studied this question.

synapsesocial.com/papers/68e61ca7b6db6435875af046 https://doi.org/10.1093/jamiaopen/ooae082

View Full Paper

Ask AI

Bookmark

Share

Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Development of Machine Learning Systems to Predict Cancer-Related Symptoms With Validation Across a Health Care System2025 · 2 citations
2Cancer Risk Prediction Using Machine Learning for Supporting Early Cancer Diagnosis in Symptomatic Patients: A Systematic Review of Model Types2025 · 14 citations
3Prediction of Cancer Symptom Trajectory Using Longitudinal Electronic Health Record Data and Long Short-Term Memory Neural Network2024 · 23 citations
4Machine Learning Approach To Predict Multiple Diseases Based On Symptoms2024 · 6 citations
5Clinical utility of EHR-based predictive models for identifying high-risk individuals in early cancer detection2025 · 1 citations