Pulse: health information at your fingertips

Given a set of symptoms, we suggest the most likely cause.
"Disease-Symptom Knowledge Databse" dataset from Columbia University was used to build this feature (along with retrieval algo we wrote).
Ask questions about diseases without going to a hospital.
This feature is powered by BERT - a recent SOTA model for question answering.
Users have a personal page with computed insights from their insurance provider such as Humana.
We've indexed a lot of data about diseases from the web and stored in IRIS - as a side bonus you get NLP insights from IRIS platform.

Inspiration

We focused on Humana challenge to minimize unnecessary provider interactions, and as a result lower costs. A lot of times patients simply lack resources to answer their health questions, or go to a doctor with a minor illness. Pulse allows you to self-service such requests. Our tool is especially useful to users who don't have access to a healthcare professional.

What it does

Pulse allows you to self-service some of the medical requests you might have:

Given a list of symptoms, we provide the most likely cause.
We answer questions you might have about diseases.
Voice interface which uses NLP and NLU to identify the likely disease given the symptoms.
Personal insights page from insurance provider.

How we built it

Question answering about diseases is done using BERT - a former SOTA model for such task. It is deployed on a separate server (needs GPU to run fast enough), with fine-tuning on data used. For this purpose we wrote a web crawler to index pages about diseases from Mayo Clinic.
Symptom-to-cause is done with a custom algorithm, "Disease-Symptom Knowledge Databse" dataset from Columbia University was used.
Voice interface built using BERT (over SQUAD dataset) and TTS modules.
Web part is done is Flask.
Data is stored in IRIS database.