Differential Diagnosis Model of Hypocellular Myelodysplastic Syndrome and Aplastic Anemia Based on the Medical Big Data Platform
2018
The arrival of the era of big data has brought new ideas to solve problems for all walks of life. Medical clinical data is collected and stored in the medical field by utilizing the medical big data platform. Based on medical information big data, new ideas and methods for the differential diagnosis of hypo-MDS and AA are studied. The basic information, peripheral blood classification counts, peripheral blood cell morphology, bone marrow cell morphology, and other information were collected from patients diagnosed with hypo-MDS and AA diagnosed in the first diagnosis. First, statistical analysis was performed. Then, the logistic regression model, decision tree model, BP neural network model, and support vector machine (SVM) model of hypo-MDS and AA were established. The sensitivity, specificity, Youden index, positive likelihood ratio (
Keywords:
- Machine learning
- Aplastic anemia
- Decision tree model
- Logistic regression
- Differential diagnosis
- Support vector machine
- Hypocellular Myelodysplastic Syndrome
- Youden's J statistic
- Pediatrics
- Likelihood ratios in diagnostic testing
- Artificial intelligence
- Mathematics
- Statistical classification
- Decision tree
- Decision tree learning
- Statistics
- Test set
- Correction
- Source
- Cite
- Save
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