Comparing natural language processing representations of coded disease sequences for prediction in electronic health records.

ABSTRACT:

Objective

Natural language processing (NLP) algorithms are increasingly being applied to obtain unsupervised representations of electronic health record (EHR) data, but their comparative performance at predicting clinical endpoints remains unclear. Our objective was to compare the performance of unsupervised representations of sequences of disease codes generated by bag-of-words versus sequence-based NLP algorithms at predicting clinically relevant outcomes.

Materials and methods

This cohort study used primary care EHRs from 6 286 233 people with Multiple Long-Term Conditions in England. For each patient, an unsupervised vector representation of their time-ordered sequences of diseases was generated using 2 input strategies (212 disease categories versus 9462 diagnostic code

SUBMITTER: Beaney T 

PROVIDER: S-EPMC11187492 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature