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

The use of information management systems that are owned by hospitals is still limited to being used only for the operation of daily patient service transactions and making reports only. The use of SIMRS is not optimal, it should pile the data stored in the database server can be used to generate new information if we dig deeper with the IT approach. This study uses data mining techniques with K-Means clustering method to cluster the patient's medical record data. The results of this study produce column 4 clusters consisting of districts, diagnoses of diseases, age and sex.The results of this study produce column 4 clusters consisting of districts, diagnoses of diseases, age and sex. Cluster 1 produced many patients consisting of 79(15%) female patients, Cluster 2 produced many patients consisting of 214(50%) male patients. Likewise Cluster 3 produced 89(17%) female patients. people and cluster 4 produced many patients consisting of 152(28%) female patients.The grouping of patient medical record data produces new information about the pattern of grouping of disease spread in each district based on the patient's medical record data from Anwar Medika Hospital as much as 534 data with a completion time of 0.06 seconds

Klasterisasi Data Rekam Medis Pasien Menggunakan Metode K-Means Clustering di Rumah Sakit Anwar Medika Balong Bendo Sidoarjo