[transposed]

I think a symptom checker bot has much to offer (though the devil is in the details). How is information extracted from a curated knowledgebase? In any case, a system with such probabilistic machinery could add a lot to our healthcare system since it would not make human errors such as base rate neglect. Furthermore, would existing doctors approve of machine responses?

[adamra]

I am the author of the article and also the designer of the NLP system. If you're asking about how the mentions of the medical concepts (as defined in the curated knowledge base) are extracted from user messages then here is a short explanation.

The bot uses an entity recognition engine for this. The engine is available via API [1]. The engine itself uses dictionary phrases from the knowledge base and a custom matching strategy that operates on dependency graphs, which allows to abstract from some surface details (e.g., understand that “pain in my left leg” is “pain in leg”). It also contains a modified version of the DepNeg algorithm [2] to detect negated mentions (“I don't have headache”, “no history of chest pain”).

[1] https://developer.infermedica.com/docs/nlp [2] http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3392064/