Relation Extraction And Medical Knowledge Base Creation For The Anamnesis Build-Up

The Result

Created a database for online clinic chatbot to support automatic filling anamnesis from the clients. The bot is recognizing the client’s answer and filled the json document (it is very simple by using AWS service). If the quality of the json document is not sufficient (not enough data – TensorFlow) some additional questions are generated and result json documents are merged.

The Challenge

The client approached Mellivora experts to create an automatic bot for working with patients online. NLP technologies had to be used for the purpose of gathering knowledge base for the anamnesis build-up. The team was provided with the links to websites of online chats with the doctors and text records of the dialogues with the patients. Mellivora team had to find the relations to create a precise anamnesis form.

The Solution

To reach the set goal, we used Medical Named Entity and Relationship Extraction (NERe) by Amazon Comprehend which helps to return the medical information such as medication, medical condition, test, treatment and procedures (TTP), anatomy, and Protected Health Information (PHI). It also helps to identify relationships between extracted sub-types associated to Medications and TTP. There is also contextual information provided as entity “traits” (negation, or if a diagnosis is a sign or symptom).

Technology Stack

Technology stack was based on AWS and is as folows:

About Mellivora Software

Mellivora Software helps SME and Enterprise businesses build custom IT solutions for a bunch of specific industries. Our core expertise is focused around:

  • Big Data, DevOps and AWS technologies
  • NLP technologies
  • Data Science/Machine Learning