本文的关于数值特征的转化和解释值得学习（NMF：用行解释列；用已知维度解释未知维度）
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Multi-layer Representation Learning for Medical Concepts | |||||||||||
| 文献作者 | Edward Choi | ||||||||||
| 文献发表年限 | 2016 | ||||||||||
| 文献关键字 | |||||||||||
| 2Vec; Representation Learning; Medical Concepts; Healthcare An- alytics; Neural Networks | |||||||||||
| 摘要描述 | |||||||||||
| Proper representations of medical concepts such as diagnosis, medication, procedure codes and visits from Electronic Health Records (EHR) has broad applications in healthcare analytics. Patient EHR data consists of a sequence of visits over time, where each visit includes multiple medical concepts, e.g., diagnosis, procedure, and medication codes. This hierarchical structure provides two types of relational information, namely sequential order of visits and co-occurrence of the codes within a visit. In this work, we propose Med2Vec , which not only learns the representations for both medical codes and visits from large EHR datasets with over million visits, but also allows us to interpret the learned representations confirmed positively by clinical experts. In the experiments, Med2Vec shows significant improvement in prediction accuracy in clinical applications compared to baselines such as Skip-gram, GloVe, and stacked autoencoder, while providing clinically meaningful interpretation. | |||||||||||
