Oracle partner DSP helps National Institute for Health Research improve the clinical journey for patients regaining hearing using cochlear implants.
“DSP chose Oracle Cloud Infrastructure Data Science to build machine learning models for National Institute for Health Research because it offers a comfortable and productive open source Python experience for our data scientists, and it also provides the flexibility to fully control the creation of the model so that the results are understandable by the customer.”
The National Institute for Health Research conducts research on cochlear implants to treat severe hearing loss for people of all ages. These devices are surgically implanted into the ear and can restore the ability to understand speech in profoundly deaf individuals. In children who are born profoundly deaf, cochlear implants can offer the ability to gain spoken language, making children under the age of one a high priority. New implants are programmed based on the individual and that programming must be updated about six times over the first two years.
In the midst of a pandemic, the National Institute of Health Research sought to reduce the number of times a new implant recipient must return to the clinic for new programming. In addition, it wanted to increase the number of patients that could be served by reducing the level of expertise and experience required to program a new cochlear implant.
Using clinical data they collected, the National Institute for Health Research is working with DSP to determine if machine learning can help with these challenges.
Why DSP Chose Oracle
DSP chose Oracle Cloud Infrastructure Data Science as its tool for building machine learning models because it provides a familiar, Python-based experience with complete visibility and control in creating models. The Accelerated Data Science toolkit included with Data Science is providing extra speed and guidance in creating machine learning models by identifying promising algorithms.
Using Oracle Cloud Infrastructure Data Science helped the National Institute for Health Research team gain new insights into how it can apply cutting-edge machine-learning approaches to try to solve a challenging clinical need.
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