## Inspiration

We want to fix healthcare! 48% of physicians in the US are burned out, which is a driver for higher rates of medical error, lower patient satisfaction, higher rates of depression and suicide. Three graduate students at Stanford have been applying design thinking to the burnout epidemic. A CS grad from USC joined us for TreeHacks!

We conducted 300 hours of interviews, learned iteratively using low-fidelity prototypes, to discover,
i) There was no “check engine” light that went off warning individuals to “re-balance”
ii) Current wellness services weren’t designed for individuals working 80+ hour weeks
iii) Employers will pay a premium to prevent burnout

And Code Coral was born.

## What it does

Our platform helps highly-trained individuals and teams working in stressful environments proactively manage their burnout. The platform captures your phones’ digital phenotype to monitor the key predictors of burnout using machine learning. With timely, bite-sized reminders we reinforce individuals’ atomic wellness habits and provide personalized services from laundry to life-coaching.

Check out more information about our project goals: <https://youtu.be/zjV3KeNv-ok>

## How we built it

We built the backend using a combination of API's to Fitbit/Googlemaps/Apple Health/Beiwe; Built a machine learning algorithm and relied on an App Builder for the front end.

## Challenges we ran into

API's not working the way we want. Collecting and aggregating "tagged" data for our machine learning algorithm. Trying to figure out which features are the most relevant!

## Accomplishments that we're proud of

We had figured out a unique solution to addressing burnout but hadn't written any lines of code yet! We are really proud to have gotten this project off the ground!

i) Setting up a system to collect digital phenotyping features from a smart phone ii) Building machine learning experiments to hypothesis test going from our digital phenotype to metrics of burnout iii) We figured out how to detect anomalies using an individual's baseline data on driving, walking and time at home using the Microsoft Azure platform iv) Build a working front end with actual data!

Note - login information to codecoral.net: username - test password - testtest

## What we learned

We are learning how to set up AWS, a functioning back end, building supervised learning models, integrating data from many source to give new insights. We also flexed our web development skills.

## What's next for Coral Board

We would like to connect the backend data and validating our platform with real data!
## Inspiration

Our hack was inspired by CANSOFCOM's "Help Build a Better Quantum Computer Using Orqestra-quantum library". Our team was interested in exploring quantum computing so it was a natural step to choose this challenge.

## What it does

We implemented a Zapata QuantumBackend called PauliSandwichBackend to perform Pauli sandwiching on a given gate in a circuit. This decreases noise in near-term quantum computers and returns a new circuit with this decreased noise. It will then run this new circuit with a given backend.

## How we built it

Using python we built upon the Zapata QuantumBackend API. Using advanced math and quantum theory to build an algorithms dedicated to lessening the noise and removing error from quantum computing. We implanted a new error migration technique with Pauli Sandwiching

## Challenges we ran into

We found it challenging to find clear documentation on the subject of quantum computing. Between the new API and for the most of us a first time experience with quantum theory we had to delicate a large chunk of our time on research and trial and error.

## Accomplishments that we're proud of

We are extremely proud of the fact we were able to get so far into a very niche section of computer science. While we did not have much experience we have jump a new experience that a very small group of people actually get work with.

## What we learned

We learned much about facing unfamiliar ground. While we have a strong background in code and math we did run into many challenge trying to understand quantum physics. Not only did this open a new software to us it was a great experience to be put back into the unknown with logic we were unfamiliar with it

## What's next for Better Quantum Computer

We hope to push forward our new knowledge on quantum computers to develop not only this algorithm, but many to come as quantum computing is such an unstable and untap resource at this time
## Inspiration

So many people around the world, including those dear to us, suffer from mental health issues such as depression. Here in Berkeley, for example, the resources put aside to combat these problems are constrained. Journaling is one method commonly employed to fight mental issues; it evokes mindfulness and provides greater sense of confidence and self-identity.

## What it does

SmartJournal is a place for people to write entries into an online journal. These entries are then routed to and monitored by a therapist, who can see the journals of multiple people under their care. The entries are analyzed via Natural Language Processing and data analytics to give the therapist better information with which they can help their patient, such as an evolving sentiment and scans for problematic language. The therapist in turn monitors these journals with the help of these statistics and can give feedback to their patients.

## How we built it

We built the web application using the Flask web framework, with Firebase acting as our backend. Additionally, we utilized Microsoft Azure for sentiment analysis and Key Phrase Extraction. We linked everything together using HTML, CSS, and Native Javascript.

## Challenges we ran into

We struggled with vectorizing lots of Tweets to figure out key phrases linked with depression, and it was very hard to test as every time we did so we would have to wait another 40 minutes. However, it ended up working out finally in the end!

## Accomplishments that we're proud of

We managed to navigate through Microsoft Azure and implement Firebase correctly. It was really cool building a live application over the course of this hackathon and we are happy that we were able to tie everything together at the end, even if at times it seemed very difficult

## What we learned

We learned a lot about Natural Language Processing, both naively doing analysis and utilizing other resources. Additionally, we gained a lot of web development experience from trial and error.

## What's next for SmartJournal

We aim to provide better analysis on the actual journal entires to further aid the therapist in their treatments, and moreover to potentially actually launch the web application as we feel that it could be really useful for a lot of people in our community.