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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this work, we propose a novel goal-oriented dialog task, automatic symptomdetection. We build a system that can interact with patients through dialog todetect and collect clinical symptoms automatically, which can save a doctor'stime interviewing the patient. Given a set of explicit symptoms provided by thepatient to initiate a dialog for diagnosing, the system is trained to collectimplicit symptoms by asking questions, in order to collect more information formaking an accurate diagnosis. After getting the reply from the patient for eachquestion, the system also decides whether current information is enough for ahuman doctor to make a diagnosis. To achieve this goal, we propose two neuralmodels and a training pipeline for the multi-step reasoning task. We also builda knowledge graph as additional inputs to further improve model performance.Experiments show that our model significantly outperforms the baseline by 4%,discovering 67% of implicit symptoms on average with a limited number ofquestions.

Knowledge Grounded Conversational Symptom Detection with Graph Memory Networks