Summary
The World Health Organization recognizes mental disorders as one of the most significant public health challenges worldwide as measured by prevalence, burden of disease and disability , and depression as the world’s leading cause of disability. In EC and WHO action plans it is clearly requested that mental health services can be accessed without unfair financial barriers and that treatments are made available on criteria of both efficiency and financial fairness. In this framework personalised medicine is the fastest, most economic and optimized solution to the depression treatment problem. Indeed, the current push for a cost effective personalised medicine based on IT solution will also help with the commercialisation of our Predictix platform. The platform will allow medical practitioners to have up-to-date and accurate knowledge on each case and improve the care that patient receives, with an economic revenue too. Our solution allows for better delivery of available treatments – noted as a vital factor for increasing remission. TALIAZ was founded in 2012 by Dr. Dekel Taliaz and Oren Taliaz. Our mission is to set a new standard in personalized medicine improving quality of life for patients suffering from depression. Taliaz takes advantage of in-house expertise in genetics, data science and central nervous system related disease with the advancements in genetic sequencing and machine learning technologies, to develop leading personal medicine proprietary IP solutions for depression. Our future vision is to use Predictix™ also as a decision support tool for common health concerns such as ADHD and Anxiety. The SME Instrument project now perfectly fits our strategy and vision by enabling a fast finalisation of the platform development and by providing us the resources necessary to demonstrate the benefits of Predictix™ through an international clinical study.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/832230 |
| Start date: | 01-11-2018 |
| End date: | 28-02-2019 |
| Total budget - Public funding: | 71 429,00 Euro - 50 000,00 Euro |
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Original description
The World Health Organization recognizes mental disorders as one of the most significant public health challenges worldwide as measured by prevalence, burden of disease and disability , and depression as the world’s leading cause of disability. In EC and WHO action plans it is clearly requested that mental health services can be accessed without unfair financial barriers and that treatments are made available on criteria of both efficiency and financial fairness. In this framework personalised medicine is the fastest, most economic and optimized solution to the depression treatment problem. Indeed, the current push for a cost effective personalised medicine based on IT solution will also help with the commercialisation of our Predictix platform. The platform will allow medical practitioners to have up-to-date and accurate knowledge on each case and improve the care that patient receives, with an economic revenue too. Our solution allows for better delivery of available treatments – noted as a vital factor for increasing remission. TALIAZ was founded in 2012 by Dr. Dekel Taliaz and Oren Taliaz. Our mission is to set a new standard in personalized medicine improving quality of life for patients suffering from depression. Taliaz takes advantage of in-house expertise in genetics, data science and central nervous system related disease with the advancements in genetic sequencing and machine learning technologies, to develop leading personal medicine proprietary IP solutions for depression. Our future vision is to use Predictix™ also as a decision support tool for common health concerns such as ADHD and Anxiety. The SME Instrument project now perfectly fits our strategy and vision by enabling a fast finalisation of the platform development and by providing us the resources necessary to demonstrate the benefits of Predictix™ through an international clinical study.Status
CLOSEDCall topic
EIC-SMEInst-2018-2020Update Date
27-10-2022
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