English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Electronic health record (EHR) analysis has become increasingly important in improving the quality of human healthcare. To leverage the full insights from the big EHRs, it is very important to define some application scenarios for which the relevant data can be extracted for training machine learning models to accomplish the expected goals. In thispaper, we develop a system on how to recommend medical treatment solutions for patients living in the countryside and small cities when they happen to have schizophrenia but the doctors in the local hospitals do not have sufficient expertise to deal with such challenges. In the EHRs, we take the patients’ symptom descriptions as documents and then develop NLP and unsupervised machine learning techniques to analyze such documents to find the relevant and effective treatment solutions provided by medical experts. Extensive experimental results with different vector representations for documents show that the binary keyword vector representation works best to find relevant and effective medical treatmentplans and solutions from the EHRs for any input symptom description.

Chinese electronic health record analysis for recommending medical treatment solutions with NLP and unsupervised learning