23 Jul Jiva.ai presents at ISCF call
The Industrial Strategy Challenge Fund is part of government’s Industrial Strategy, the long-term plan to raise productivity and earning power in the UK.
The fund is a core pillar in the government’s commitment to increase funding in research and development by £4.7 billion over 4 years to strengthen UK science and business.
It will invest in the world-leading research base and highly-innovative businesses to address the biggest industrial and societal challenges today.
Enabling integrated diagnostics for early detection
Integrative technologies to enhance early diagnosis are at the heart of the most recent funding competition. It is an attempt to pit together the diverse technical and academic minds with the aim of instigating innovation for early diagnosis. It is not difficult to see how the journey can start from data generation and accumulation, perhaps with medical devices as the vehicle, and the destination terminating at computational analysis.
Indeed every one of the 24 presentations at the kick off event, chaired by the Knowledge Transfer Network, had some kind of AI or machine learning element embedded in its trajectory.
We are very happy to see that there is a recognition for the need of this type of technology; the very premise of the technology at the heart of Jiva.ai is one of integration. Out co-founder and CTO, Manish Patel, presented at the event and was very well received.
Manish Patel presents at ISCF 2019
How Jiva.ai can contribute
What do we do? Succinctly, we specialise in machine learning with a focus on integration. It only takes a minute’s thought to realise the complexity of data that comes out of hospital sites alone – from patient medical records, tests and results, diagnostic machine output – almost every corner is drenched with data.
Jiva is a software platform wherein you can model those different but related data sets almost in isolation, with a view to glue them together later. In that way, iteratively, one is able to build bigger and better computer representations of the system.
Our goal is to achieve actionable insight and develop data-driven prediction.
