Combining unsupervised, supervised and rule-based learning: the case of detecting patient allergies in electronic health records.

ABSTRACT:

Background

Data mining of electronic health records (EHRs) has a huge potential for improving clinical decision support and to help healthcare deliver precision medicine. Unfortunately, the rule-based and machine learning-based approaches used for natural language processing (NLP) in healthcare today all struggle with various shortcomings related to performance, efficiency, or transparency.

Methods

In this paper, we address these issues by presenting a novel method for NLP that implements unsupervised learning of word embeddings, semi-supervised learning for simplified and accelerated clinical vocabulary and concept building, and deterministic rules for fine-grained control of information extraction. The clinical language is automatically learnt, and vocabulary, concepts, an

SUBMITTER: Berge GT 

PROVIDER: S-EPMC10507898 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature