Our team was involved in the design and implementation of MedNition’s machine learning framework, worked with diverse anonymized patient data and used a variety of medical information ontologies.
Digamma was engaged by the Silicon Valley-based Social Inertia to create a product that allows users to choose a cause they care about and actively participate in it.
In addition to developing the final product, we provided strategic recommendations on how to effectively incorporate machine learning into the app to provide an even more engaging user experience.
The iOS app is called ActOn and is available on the App Store.
“What was helpful for me was that we were able to have a direct relationship with Digamma’s engineers. Even though Sergey, our developer, was eight to nine time zones away, we worked and communicated together very efficiently over Skype. Sergey was an outstanding senior engineer and if felt as if he was an extension to our team. I have had an excellent experience working with Digamma and I have already sent several referrals to their company over the years. I would definitely recommend Digamma to anybody looking for a strong development team.”
– Michael White, Founder and CEO, Social Inertia
The machine learning components of the project included:
- Extraction of textual content from web pages and RSS/ATOM feeds.
- Content-based recommendations
- Topic extraction
Machine learning techniques Digamma used:
- Shallow text analysis with densitometric features based on quantitative linguistic text laws
- Topic discovery
- Locality-sensitive hashing