Generic medical concept embedding and time decay for diverse patient outcome prediction tasks.

ABSTRACT: Many fields, including Natural Language Processing (NLP), have recently witnessed the benefit of pre-training with large generic datasets to improve the accuracy of prediction tasks. However, there exist key differences between the longitudinal healthcare data (e.g., claims) and NLP tasks, which make the direct application of NLP pre-training methods to healthcare data inappropriate. In this article, we developed a pre-training scheme for longitudinal healthcare data that leverages the pairing of medical history and a future event. We then conducted systematic evaluations of various methods on ten patient-level prediction tasks encompassing adverse events, misdiagnosis, disease risks, and readmission. In addition to substantially reducing model size, our results show that a universa

SUBMITTER: Li Y 

PROVIDER: S-EPMC9418804 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature