A Risk Prediction Model Based on Combined CBC and CPD Parameters for Triage of Non-Hodgkin Lymphoma Inpatients.
A machine-learning model using routine blood tests may help doctors quickly identify high-risk non-Hodgkin lymphoma patients who need closer attention.
OBJECTIVE: This study aimed to construct and validate a machine learning triage model based on combined routine complete blood count (CBC) and cell population data (CPD) parameters, for stratifying non-Hodgkin lymphoma (... The SVM model constructed with the 8 combined CBC and CPD indicators demonstrated the optimal predictive efficacy and robustness across both cohorts, with AUC=0.894 (training, 95%CI: 0.858-0.923) and AUC=0.803 (validatio...
What the study was
- Study design
- Cohort study
- Population
- adults
- Sample size
- 510
- Category
- Diagnostics
- Maturity
- Exploratory
- Journal
- Annals of clinical and laboratory science
Why it surfaced
Score 7/10 [PROMISING_PRELIMINARY]: Cohort study (Journal Article) matched 'CBC-based diagnostics and ML in hematolo'. Components — novelty:1/3, relevance:3/3, design:1/2, population:2/2. Confidence: high. Conservative scoring applied per v1.3 rubric.
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