Inspiration: Why go to the hospital when you can treat yourself at home? What!? We know that not all diseases are treatable at home. But what if there’s a tool for letting you know ‘if’ you need to go to the hospital. The degree of confidence plays a vital role in providing insight into how a symptom shows an initial sign of chronic disease. Sometimes, by taking some precautionary measures, you can avoid the possibility of a disease in its initial stages. AI spab is one such tool that provides predictive insights for both doctors and patients to track a certain disease based on symptoms.
Dataset: http://people.dbmi.columbia.edu/~friedma/Projects/DiseaseSymptomKB/index.html
What it does: It provides predictive analysis based on the symptoms provided by the patient. It provides doctors the ability to track patients' data and see what diseases are prevalent in a certain area.
How we built it: We used Google Cloud Platform’s AutoML API to train the model and deploy it to run the predictive model
Challenges we ran into: Since all the members are new to Google Cloud Services, we had a challenging time building Cloud API’s but ultimately with the help of the mentors, we could solve the issue.
What’s next for AISpab: Making the general checkups completely virtualized, making it usable across different hospitals for the easy transforming of records, making it such that it can be used to automatically schedule appointments for doctors based on a patients health priority
Built with: Google Cloud Platform: AutoML, Firebase, Tensorflow. Frontend: VueJS
Built With
- automl
- ml
- node.js
- vuejs
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