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Kahun - an interactive medical knowledge base, for modeling medical knowledge and managing diagnostic processes, as well medical knowledge related to COVID-19.


Fully approved GDPR compliance

A thorough examination of CE GDPR and GSPD requirementsAdjusting the platform to hold up to all CE GDPR and GSPD regulatory requirements

Improved UI, Launch V2.0 of the mobile app

– Improving user interface: We intend to re-design the mobile interface to make it more friendly and intuitive. Results view will be optimized.

Launch “COVID-19” dedicated web based map/symptomizer

a. Develop dedicated interface that will enable us to share the continuously updated knowledge cataloged by Kahun in a visible manner with the medical and scientific community, with an overview of the clinical picture relevant for COVID-19 and to easily find access relevant literature. b. We intend to develop a “COVID-19” dedicated symptomizer, which will help physicians in assessing patients and considering their match against a match with other diseases which come up with the COVID-19 differential diagnosis.

New data input mode. Launch V2.0 of the mobile app

Some of the feedback we are getting signals that our findings entry options are limited. Which results either in time-wasted or in in-complete finding entry by the Physician. Data entry will be modified to deal with multiple features entry to enable better description of clinical manifestations. (i.e. a physician will be able to enter findings as a panel and not only one by one. E.g. complete blood count or physical exam).


Evaluating Diagnostic Accuracy of a New Artificial-Intelligence Driven Diagnostic Support Tool

Auteurs: Niv Ben-Shabat; Arial Sloma; Tomer Weizman; David Kiderman; Howard Amital
Publié dans: JMIR Medical Informatics, 2021, ISSN 2291-9694
Éditeur: JMIR Publications
DOI: 10.2196/preprints.32507

Development and validation of a knowledge-driven risk calculator for critical illness in COVID-19 patients

Auteurs: Amos Cahan, Tamar Gottesman, Michal Tzuchman Katz, Roee Masad, Gal Azulay, Dror Dicker, Aliza Zeidman, Evgeny Berkov, Boaz Tadmor, Shaul Lev
Publié dans: The American Journal of Emergency Medicine, 2020, ISSN 0735-6757
Éditeur: W. B. Saunders Co., Ltd.
DOI: 10.1016/j.ajem.2020.09.051

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