Our search product supports doctors with any data questions, whether related to quality management, clinical trials or research. As a machine learning engineers you will build modules that extract information from clinical reports, making it available to healthcare professionals. The company is small, focussed on collaboration and goal oriented.
Due to the complexity of EHR data, it doesn’t lend itself well for automated analysis. A key factor in this complexity is that a large part of the valuable information is recorded as text (notes, referral letters, questionnaires). We’re now building a data platform where, using the latest machine learning technologies, this unstructured data is organized and combined with the structured data sources of the EHRs. Furthermore, our platform includes two state-of-the-art applications for identifying patient cohorts based on certain criteria and for collecting clinical data.
As an AI engineer you will apply machine learning algorithms to the unstructured data of EHRs. You make use of the latest research to create machine learning tools that will enhance the experience of our users. What you do has a strong impact on the success of our product.
Together with the AI department you will form a small and self-organising team, responsible for building the infrastructure, implementing the algorithms and converting them to production ready models. EHR data is highly messy, so you must be prepared to getting your hands dirty on this.
Here are some example projects that our AI team is working on right now:
- Linking clinical codes to medical text
- Extracting attributes of medical concepts from text
Finally, you will be part of a larger cross-functional dynamic team consisting of developers, designers, product owners and data engineers. We are user (i.e. doctor) focused, have an experimental mindset and we iterate quickly.
What does a typical AI workday at CTcue look like?
In the morning you have a brainstorm with the developers, data engineers and the product owners about how to improve features. You’ll then work individually on one of our big projects such as normalization of medical concepts or bulk validation. You’ll discuss with Reinier and Lydia how to gather the data you need in an efficient way and you conclude the day by reading research papers in order to find a solution for a complex problem that was bugging you last week.
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