使用贝叶斯模型的患者分类概率模型

P. Tansitpong
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引用次数: 0

摘要

该研究强调贝叶斯分类算法在预测医疗机构病人就诊情况方面的有效性。贝叶斯算法检查过去的病人数据,检测入院动态中的复杂模式,包括人口、临床和时间因素。通过使用贝叶斯原理,预测模型能够估算出某些患者人口统计学特征在某些时间间隔内出现的概率,从而协助资源分配和运营管理。估算出的概率可用于选择人员配备、资源分配和运营策略。不同观察结果中概率估计值的差异提高了预测的有用性,从而加强了医疗管理和规划的有效性。
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Probabilistic Model of Patient Classification Using Bayesian Model
The research emphasizes the effectiveness of Bayesian classification algorithms in predicting patient visits in healthcare settings. Bayesian algorithms examine past patient data to detect intricate patterns in admission dynamics, including demographic, clinical, and temporal factors. Through the use of Bayesian principles, prediction models are able to estimate the probability of certain patient demographics occurring at certain intervals, therefore assisting in the allocation of resources and the management of operations. Probabilities that have been estimated are used to make choices on staffing, resource allocation, and operational strategy. The variation in probability estimates across different observations improves the predictive usefulness, hence strengthening the effectiveness in healthcare management and planning.
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CiteScore
3.20
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0.00%
发文量
43
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