/Medsenger-ChatBot

Medsenger is a smart medical chatbot powered by Hypercare API which interacts with patients suffering from discomfort by taking in the user's symptoms and intelligently identifying potential diseases.

Primary LanguageJavaScript

Medsenger-ChatBot

Medsenger is a smart medical chatbot powered by Hypercare API which interacts with patients suffering from discomfort by taking in the user's symptoms and intelligently identifying potential diseases. Inspiration:

There is a considerable fraction of the world population that does not have easy access to doctors and therefore only plan a visit to the doctor in case of emergencies. What if there was a way to remotely access professional medical information? With Medsenger, you can do a simple medical diagnosis to identify any signs of disease(s) or medical problems. This is not only useful for people living in remote areas but is useful for all by helping save time and enable people to take precaution before they enter a serious state of illness.

What It Does:

Feeling discomfort and not being sure of the problem can be problematic if left ignored. Want to save a trip to the general physician? Try Medsenger - A smart human-like chatbot that will help you identify the potential disease(s) you might be suffering from. All you have to do is input your area(s) of discomfort and Messenger will ask you to describe some symptoms that you have been noticing. Our smart algorithms will match your input with our vast database of medical problems and then identify the potential one(s)

How We Built It:

Medsenger is powered by Hypercare API using Postman and uses javascript with node.js platform. It uses Amazon-Web-Services (AWS) to store data and trigger webhooks which enables our chatbot to reply automatically. It also uses hapijs/wreck HTTP Client Utilities.

Challenges We Ran Into:

Our team came in prepared with an idea in mind but it was only after discussing it with other people and seniors that we understood how farfetched our idea was for this time-frame. After spending some time brainstorming we redefined the final idea. By this time, we had already lost a lot of time but we were determined to present a working product. When we started writing code, we struggled with cross-platform linking, that is, we had divided our work into different parts but when the time came to integrate it, it was practically impossible to integrate the platforms within the given time-frame. We were losing precious time but we didn't give up. We consulted and took help from Hypercare and other professionals at the sponsor booth. Fighting bugs and other several minor issues, we were able to finalize our product.

Accomplishments That We Are Proud Of:

We are proud to make a finished product within the given time-frame even if it meant sacrificing sleep. We are proud to get out of our comfort zone and seek people for help and that we didn't give up even after facing several challenges. Lastly, we are also proud to build a smart solution to a problem and we think our product has tremendous potential to help people who are living in remote areas and do not have easy access to a general physician.

What We Learned:

The most important lesson we learned is that although planning is an important part of the plan, it is even more important to realize the feasibility of the idea and this should be done by discussing it with other people. Another lesson we learned that research is an important part of the planning and executing an idea without proper research can lead to dead ends and a lot of waste of time and recourses.

What's Next For Medsenger:

We believe that Medsenger has the potential to help people a lot of people especially those who live in remote areas and don't have easy access to doctors. By using our product, they can get information about potential diseases they may be suffering from and take precautions as early as possible. We also have a vast list of other features in mind that we would have liked to implement but weren't able to due to time restrictions. For instance, we wanted to recommend nearby doctors and specialists who can treat the identified potential diseases or provide a list of medicines that could cure the potential disease. We want to implement NLP and use machine learning and AI so that our chatbot can become smarter and train itself to better help the people.