ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2023
Understanding Cancer Caregiving and Predicting Burden: An Analytics and Machine Learning Approach.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Advance care planning impact on caregivers and end-of-life care in advanced cancer.Journal of the National Cancer Institute · 2026Trial
- Factors associated with inflammatory cytokines in family caregivers of allogeneic hematopoietic stem cell transplantation (HSCT) recipients.Brain, behavior, and immunity · 2025Trial
- The national standards for cancer survivorship care in action: caregiver experiences across stage, phase, and geography.Journal of cancer survivorship : research and practice · 2026Article
- Digital Health Engagement Behaviors in US Family Caregivers: Trend Analysis Using the Health Information National Trends Survey 2019-2022 Data.Journal of medical Internet research · 2026Article
- Caregiver Burden in Caring for Family Members with Cancer in the Makkah Region, Saudi Arabia: A Cross-Sectional Study.Healthcare (Basel, Switzerland) · 2026Article
- Parental cancer caregiving in emerging and young adulthood: the impact of sibling dynamics on the caregiving experience.Journal of health psychology · 2026Article
- Health Care Providers' Perceptions of Unmet Needs Among African American Cancer Caregivers: Qualitative Investigation Among US Medical Professionals.JMIR cancer · 2026Article
- Evaluation of Large Language Models in Tailoring Educational Content for Cancer Survivors and Their Caregivers: Quality Analysis.JMIR cancer · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer caregivers are often informal family members who may not be prepared to adequately meet the needs of patients and often experience high stress along with significant physical, emotional, and financial burdens. Accurate prediction of caregiver's burden level is highly valuable for early intervention and support. In this study, we used several machine learning approaches to build prediction models from the National Alliance for Caregiving/AARP dataset. We performed data cleansing and imputation on the raw data to give us a working dataset of cancer caregivers. Then a series of feature selection methods were used to identify predictive risk factors for burden level. Using supervised machine learning classifiers, we achieved reasonably good prediction performance (Accuracy ∼ 0.94; AUC ∼ 0.97; F1∼ 0.93). We identify a small set of 15 features that are strong predictors of burden and can be used to build Clinical Decision Support Systems.
Indexed as
Identifiers
38222371PMC10785947What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.