Bayesian Double Feature Allocation for Phenotyping With Electronic Health Records
用于电子健康记录表型识别的贝叶斯双重特征分配AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要聚焦健康记录的分类矩阵分解与潜在疾病识别，未提供对应所列计量领域细分的充分依据。
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用于电子健康记录表型识别的贝叶斯双重特征分配
- 研究问题
- 如何从电子健康记录中的分类数据识别患者共享的潜在疾病，并利用已知疾病信息改善潜在结构的可识别性与医学解释？
- 方法与思路
- 作者提出分类矩阵分解，以双重特征分配同时配置行与列的特征，并在贝叶斯框架中纳入已知疾病的先验信息。
- 材料陈述的贡献
- 摘要称方法经误设模型模拟及稀疏潜因子模型比较，在中国健康记录中识别十种潜在疾病，并提供软件实现。
- 阅读关注点
- 应核对潜在疾病的统计定义、先验如何影响识别、误设模拟的比较设置，以及医学文献交叉核对的具体标准。
- 阅读边界
- 仅据版本未注明的索引摘要，无法判断临床有效性、先验敏感性或推广范围；潜在疾病解释不能直接视为已验证的病因。
原文摘要
Electronic health records (EHR) provide opportunities for deeper understanding of human phenotypes - in our case, latent disease - based on statistical modeling. We propose a categorical matrix factorization method to infer latent diseases from EHR data. A latent disease is defined as an unknown biological aberration that causes a set of common symptoms for a group of patients. The proposed approach is based on a novel double feature allocation model which simultaneously allocates features to the rows and the columns of a categorical matrix. Using a Bayesian approach, available prior information on known diseases (e.g., hypertension and diabetes) greatly improves identifiability and interpretability of the latent diseases. We assess the proposed approach by simulation studies including mis-specified models and comparison with sparse latent factor models. In the application to a Chinese EHR data set, we identify 10 latent diseases, each of which is shared by groups of subjects with specific health traits related to lipid disorder, thrombocytopenia, polycythemia, anemia, bacterial and viral infections, allergy, and malnutrition. The identification of the latent diseases can help healthcare officials better monitor the subjects' ongoing health conditions and look into potential risk factors and approaches for disease prevention. We cross-check the reported latent diseases with medical literature and find agreement between our discovery and reported findings elsewhere. We provide an R package "dfa" implementing our method and an R shiny web application reporting the findings.