ArticleSupportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer2023
Development and external validation of a machine learning-based prediction model for the cancer-related fatigue diagnostic screening in adult cancer patients: a cross-sectional study in China.
Article in Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.
- Diagnostic predictive models for cancer-related fatigue: current evidence and future directions.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Pooled it
- Development and validation of a machine learning model to predict moderate-to-severe cancer-related fatigue in breast cancer.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2025Article
- Development and validation of machine learning models based on stacked generalization to predict psychosocial maladjustment in patients with acute myocardial infarction.BMC psychiatry · 2025Article
- Machine Learning Approaches to Predict Symptoms in People With Cancer: Systematic Review.JMIR cancer · 2024Review
- Review
- Characteristics and predictors associated with cancer-related fatigue among solid and liquid tumors.Journal of cancer research and clinical oncology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 4 institutions in 1 country.
Funding
Abstract
purposeCancer-related fatigue (CRF) is the most common symptom in cancer patients and may interfere with patients' daily activities and decrease survival rate. However, the etiology of CRF has not been identified. Diagnosing CRF is challenging. Thus, our study aimed to develop a CRF prediction model in cancer patients, using data that healthcare professionals routinely obtained from electronic health records (EHRs) based on the 3P model and externally validate this model in an independent dataset collected from another hospital.
methodsBetween April 2022 and September 2022, a cross-sectional study was conducted on adult cancer patients at two first-class tertiary hospitals in China. Data that healthcare professionals routinely obtained from electronic health records (EHRs) based on the 3P model were collected. The outcome measure was according to ICD-10 diagnostic criteria for CRF. Data from one hospital (n = 305) were used for model development and internal validation. An independent data set from another hospital (n = 260) was utilized for external validation. logistic regression, random forest (RF), Naive Bayes (NB), and extreme gradient boosting (XGBoost) were constructed and compared. The model performance was evaluated in terms of both discrimination and calibration.
resultsThe prevalence of CRF in the two centers was 57.9% and 56.1%, respectively. The Random Forest model achieved the highest AUC of 0.86 among the four types of classifiers in the internal validation. The AUC of RF and NB were above 0.7 in the external validation, suggesting that the models also have an acceptable generalization ability.
conclusionsThe incidence of CRF remains high and deserves more attention. The fatigue prediction model based on the 3P theory can accurately predict the risk of CRF. Nonlinear algorithms such as Random Forest and Naive Bayes are more suitable for diagnosing and evaluating symptoms.
Indexed as
Identifiers
What 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.