Summary
Medical errors are recognized as a world-wide problem - this includes mistakes in performing Differential Diagnosis resulting in sending patients to unnecessary tests, missing vital tests, and additional mistakes along the patient management process. In E.U countries alone, medical errors amount to 750,000 Million errors a year, resulting 3.2 million more hospitalizations, 260,000 permanent disabilities and 95,000 deaths each year.
The product is a smartphone application (and a web-based version), designed for doctors and medical students. Inspired by crowd-based platforms, its database is regularly updated using a dedicated editor for inputting medical definitions, concepts, and references from the professional medical literature, and for annotating the knowledge and defining relations between elements. So far, Kahun has already established an exclusive database for Internal Medicine's ontology, comprising of more than 50 thousand elements and relations.
Kahun's innovative Bayesian algorithms, identify probabilistic relations between the elements in the database - within the tens of thousands of elements scattered across many layers of depth, it creates a massive knowledge graph of probabilistic relationships within the medical ontology. By now, Kahun generates more than 3 Million insights for relations between medical concepts.
Kahun allows users to enter variables such as symptoms and lab test results, and presents them a Differential Diagnosis, along with a clear Path to Evidence – track leading from variables to possible result, presented in a graphical correlations map between all elements and with specific references from the literature, and it provides a series of suggestions for patient management such as which lab tests or imaging are the best next step.
The company plans to expand the knowledge graph into additional medical fields and provide even greater value. The company expects to reach total revenues of €207 Million by the end of 2026.
The product is a smartphone application (and a web-based version), designed for doctors and medical students. Inspired by crowd-based platforms, its database is regularly updated using a dedicated editor for inputting medical definitions, concepts, and references from the professional medical literature, and for annotating the knowledge and defining relations between elements. So far, Kahun has already established an exclusive database for Internal Medicine's ontology, comprising of more than 50 thousand elements and relations.
Kahun's innovative Bayesian algorithms, identify probabilistic relations between the elements in the database - within the tens of thousands of elements scattered across many layers of depth, it creates a massive knowledge graph of probabilistic relationships within the medical ontology. By now, Kahun generates more than 3 Million insights for relations between medical concepts.
Kahun allows users to enter variables such as symptoms and lab test results, and presents them a Differential Diagnosis, along with a clear Path to Evidence – track leading from variables to possible result, presented in a graphical correlations map between all elements and with specific references from the literature, and it provides a series of suggestions for patient management such as which lab tests or imaging are the best next step.
The company plans to expand the knowledge graph into additional medical fields and provide even greater value. The company expects to reach total revenues of €207 Million by the end of 2026.
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| Web resources: | https://cordis.europa.eu/project/id/961253 |
| Start date: | 01-08-2020 |
| End date: | 31-07-2022 |
| Total budget - Public funding: | 3 393 568,00 Euro - 2 375 498,00 Euro |
Cordis data
Original description
Medical errors are recognized as a world-wide problem - this includes mistakes in performing Differential Diagnosis resulting in sending patients to unnecessary tests, missing vital tests, and additional mistakes along the patient management process. In E.U countries alone, medical errors amount to 750,000 Million errors a year, resulting 3.2 million more hospitalizations, 260,000 permanent disabilities and 95,000 deaths each year.The product is a smartphone application (and a web-based version), designed for doctors and medical students. Inspired by crowd-based platforms, its database is regularly updated using a dedicated editor for inputting medical definitions, concepts, and references from the professional medical literature, and for annotating the knowledge and defining relations between elements. So far, Kahun has already established an exclusive database for Internal Medicine's ontology, comprising of more than 50 thousand elements and relations.
Kahun's innovative Bayesian algorithms, identify probabilistic relations between the elements in the database - within the tens of thousands of elements scattered across many layers of depth, it creates a massive knowledge graph of probabilistic relationships within the medical ontology. By now, Kahun generates more than 3 Million insights for relations between medical concepts.
Kahun allows users to enter variables such as symptoms and lab test results, and presents them a Differential Diagnosis, along with a clear Path to Evidence – track leading from variables to possible result, presented in a graphical correlations map between all elements and with specific references from the literature, and it provides a series of suggestions for patient management such as which lab tests or imaging are the best next step.
The company plans to expand the knowledge graph into additional medical fields and provide even greater value. The company expects to reach total revenues of €207 Million by the end of 2026.
Status
CLOSEDCall topic
EIC-SMEInst-2018-2020Update Date
27-10-2022
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