## Inspiration🧠

Even with today’s cutting edge technology and leading scientific research that helps us develop, advance and improve in everyday life, those with rare genetic diseases are still left behind.

Living with life threatening condition with little to no cure, considering, “less than 5% of more than 7,000 rare diseases believed to affect humans currently have an effective treatment”, is already frustrating, but when doctors aren’t knowledgeable/experienced enough to treat such cases, or when patients have only themselves to rely on to search for any experimental drugs, the everyday struggle becomes a nightmare to deal with.
But what’s even more tragic is despite there being “300 million people worldwide [suffering a rare disease], [where] approximately 4% of the total world population is affected by [one] at any given time” , people still have to go through the exhausting trial and error process of finding a cure/treatment, EVEN when in several cases, they share exactly the same disease!

Shockingly enough, there isn’t ANY collection of data or analysis being shared, on what medications/treatments work for different people, and which ones help or harm them!

**Citation**
Kaufmann, P., Pariser, A.R. & Austin, C. From scientific discovery to treatments for rare diseases – the view from the National Center for Advancing Translational Sciences – Office of Rare Diseases Research. Orphanet J Rare Dis 13, 196 (2018). <https://doi.org/10.1186/s13023-018-0936-x>

Wakap SN, Lambert DM, Alry A, et al. Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database [published online September 16, 2019]. Eur J Hum Genet. doi: 10.1038/s41431-019-0508-0. <https://ojrd.biomedcentral.com/articles/10.1186/s13023-018-0936-x>

## What it does 💻

For our project, we have tried our best to match Varient’s goal in partially helping develop a diagnosis assistance tool for the rare disease population (with genetic mutations), so that it becomes a crucial gadget in finding appropriate drug treatments, providing accurate and up to date information, while also facilitating support in decision making.

Our My Heroes gene assistant web app’s specific features include:

Ability to select images that indicate a relevant gene in the report

Generating and displaying relevant keywords, such as names of related disease and mutated gene names.

Providing insights on how the related disease can be treated.

Supporting patients understand key information from the reports.

The user interface includes: a User registration/login (for authorization and account information purposes), a Dropbox/file attachment ( for images), a Catalog of uploads (for the usability of modifying/deleting items), Display of labeled/annotated report, and a Summary page

## How we built it 🔧

1. Used Python for the backend and Machine learning component of the app.
2. Implemented pytesseract OCR to extract texts/key words (mutation names on ) from the images(image labeling) supplied by the report, and labeled them with OpenCV, along with;
3. Using spacy’s en\_ner\_bionlp13cg\_md (pretrained NLP model for medical report text processing) to extract relevant keywords from the texts.
4. Used streamlit library to deploy the machine learning web app.
5. Worked with React.js for frontend (login, signup, the navbar, settings), Firebase for User authentication and Google authentication integration and Firestore (NoSQL database) implementation as well as storage.
6. We utilized Google Docs/Discord for brainstorming, and Trello for distributing and keeping track of time and tasks assigned.
7. Utilized Figma for designing and prototyping.

## Challenges we ran into 🔥

1. Familiarizing ourselves with Figma to build a complex but easy to use medical record health app.
2. We had trouble integrating the NLP model part to the frontend and ended up using streamlit to make the backend functional.
3. Even though we were aware the Machine Learning part would take a significant chunk of our time, we didn't realize just how much it actually did. We also required and were working with all hands on deck which prevented us from other tasks.
4. With one of our members being a novice programmer and involved with another large scale event commitment taking place at the same time as this event, we were short one team member
5. Another team member lacking significant experience in Machine Learning and related technologies resulted in a lack of cohesiveness throughout the process.
6. None of us was familiar with how to use Flask, and only one of us was familiar with REST API’s. We also had several issues integrating with the frontend (connecting API’s, sending post requests, getting data back), and had to figure out an alternative solution by using Streamlit to display images, modify it using functions in Python, and display the new image and extracted keywords. We also had issues deploying the streamlit app, as we kept getting errors.

## Accomplishments that we're proud of 💪

We are proud of being able to collaborate and work together despite our overall lack of experience in Machine Learning and differences in previous experiences within the team mates.
We are also proud to have built a functional ML app, and make it usable to the user because we spent most of our time getting the NLP to work.

**How to run the app**
Pytesseract
For windows:
Via <https://github.com/UB-Mannheim/tesseract/wiki>
For mac

Download and install the spacy model:
Download en\_ner\_bionlp13cg\_md via <https://allenai.github.io/scispacy/>
pip install spacy
Pip install

## What we learned ✍️

. Restoring the health of the patients by streamlining the process and help the doctors provide the best treatment for such specific and rare diseases. (Our app could be used as an assistant (AI assistant?) Or personal record tracker or personal assistant 2. It facilitates universal information sharing, and keeps all the data in one place (some people might get private treatments which don't require the use if a health card, so they can input their info in this central platform for easier, quicker,and efficient process.

## What's next for My Heroes ✨

Integrating the machine learning app to the frontend so that the app can have actual users and a smooth, simple UI Design .

To improve the accessibility features of the app.

We would love to see our app to be in the hands of our patiently waiting users as soon as possible! We hope that with its improvements, it helps them provide some peace of mind, and hopefully makes life easy for them.
Check out our project at <http://gitcured.com/>

## Inspiration

* We chose the Treehacks Health challenge about creating a crowdsourced question platform for sparking conversations between patients and physicians in order to increase understanding of medical conditions.
* We really wanted to build a platform that did much more than just educate users with statistics and discussion boards. We also wanted to explore the idea that not many people understand how different medical conditions work conjunctively.
* Often, people don't realize that medical conditions don't happen one at a time. They can happen together, thus raising complications with prescribed medication that, when taken at the same time, can be dangerous together and may lead to unpredictable outcomes. These are issues that the medical community is well aware of but your average Joe might be oblivious to.
* Our platform encourages people to ask questions and discuss the effects of living with two or more common diseases, and take a closer look at the apex that form when these diseases begin to affect the effects of each other on one's body.

## What it does

* In essence, the platform wants patients to submit questions about their health, discuss these topics in a freestyle chat system while exploring statistics, cures and related diseases.
* By making each disease, symptom, and medication a tag rather than a category, the mixing of all topics is what fuels the full potential of this platform. Patients, and even physicians, who might explore the questions raised regarding the overlap between, for example Diabetes and HIV, contribute to the collective curiosity to find out what exactly happens when a patient is suffering both diseases at the same time, and the possible outcomes from the interactions between the drugs that treat both diseases.
* Each explored topic is searchable and the patient can delve quite deep into the many combinations of concepts. GitCured really is fueled by the questions that patients think of about their healthcare, and depend on their curiosity to learn and a strong community to discuss ideas in chat-style forums.

## How we built it

Languages used: Node.js, Sockets.IO, MongoDB, HTML/CSS, Javascript, ChartJS, Wolfram Alpha, Python, Bootstrap

## Challenges we ran into

* We had problems in implementing a multi-user real-time chat using sockets.io for every question that has been asked on our platform.
* Health data is incredibly hard to find. There are certain resources, such as data.gov and university research websites that are available, but there is no way to ensure quality data that can be easily parseable and usable for a health hack. Most data that we did find didn't help us much with the development of this app but it provided an insight to us to understand the magnitude of the health related problems.
* Another issue we faced was to differentiate ourselves from other services that meet part of the criteria of the prompt. Our focus was to critically think how each medical concept affects people, along with providing patients a platform to discuss their healthcare. The goal was to design a space that encourages creative and curious thinking, and ask questions that might never have been previously answered. We wanted to give patients a space to discuss and critically think about how each medical concept affects each other.

## Accomplishments that we're proud of

We were pretty surprised we got this far into the development of this app. While it isn't complete, as apps never are, we had a great experience of putting ideas together and building a health-focused web platform from scratch.

## What we learned

* There is a very big issue that there is no central and reliable source for health data. People may have clear statistics on finance or technology, but there is so much secrecy and inconsistencies that come with working with data in the medical field. This creates a big, and often invisible, problem where computer scientists find it harder and harder to analyze biomedical data compared to other types of data. If we hadn't committed to developing a patient platform, I think our team would have worked on designing a central bank of health data that can be easily implementable in new and important health software. Without good data, development of bio technology will always be slow when developers find themselves trapped or stuck.
## Inspiration

**250,000 people in the United States die every year from medical negligence. However, it is not negligence that leads to the most fatalities, complications, and increased hospitalizations. Rather, it is the lack of universally accessible patient medical records and software to encourage teams of doctors to work together for every patient. After thousands of hours spent shadowing and working in the medical sector, our team set off to catalyze modern medicine.**

In the current system of medical records, Emergency Room physicians are given little to no information on patients, and in many severe cases, have no time to collect it. Patients with certain diseases can suffer fatal consequences if given many common medications, including Aspirin. ER doctors have no way of knowing this if the patient is unconscious or non expressive.

Doctors in different networks have limited communication. This is especially pertinent as almost half of all doctors are in private practice. When a patient’s doctors do not work together, misdiagnosis, polypharmacy, and extreme change of prescriptions become rampant. Commonly, a doctor will give a patient a new medication that does not work well with drugs being prescribed by the patient’s other doctors.

When a patient visits a new doctor for the first time, the doctor must get all their information by interviewing the patient. This means that doctors are forced to rely on a patient’s memory, rather than past medical records, to provide life or death information.

Doctors must be able to work together, using cloud based information, in order to provide modern healthcare.

## What it does

First: an example Next: an explanation

**A patient, John Doe, is involved in a car crash and suffers a severe left arm injury, and immediately rushed to the ER. At the hospital, a doctor scans Doe's medx ID to get Doe's profile on his phone. He checks known allergies to see if Doe is allergic to EpiPen which he wants to use to help stop bleeding. He sees that Doe is severely allergic and uses a pressure system to stop bleeding instead, avoiding a fatal allergic reaction.**

**ER doctors then perform surgery and note this as a procedure. The doctors want to prescribe Doe Vicodin as a pain reliever, but as they can see on his diagnosis medx profile he suffers from substance abuse disorder; so instead they raise the dosage of his already existing Norco prescription, a less addictive opioid.**

**Doe's primary doctor back home now sees on medx that Doe just had a left arm trauma procedure and that his dosage of Norco was raised. Now during Doe's visit to the primary doctor next week, the doctor is fully up to speed on Doe's medical history and can even message Doe's surgical team at the ER any questions, because they are now active in Doe's system. Without medx, Doe's primary doctor and surgical team are not connected and Doe's primary is likely to reduce the Norco prescription back to normal, thinking there was a mistake.**

medx exists as a website and as an accompanying app. A patient creates a profile on the website (age, ethnicity, ssn, name, username) and is **automatically generated a patient ID and data matrix**. The user then downloads the app and can now login to generate the same data matrix. The user will also be sent a physical ID card with this data matrix to keep in a wallet in case of severe accident when the phone cannot be unlocked (patient unconscious).

A physician creates a profile on the website with their DEA identifier, and can now input a patient's ID number when the patient comes to their office for a first visit, or use the medx app to scan a patient's data matrix in emergency medicine. They now gain access to a patient's medical records compliant with HIPPA based regulations and security focus. With medx, a doctor uses a streamlined, universal, cloud based system, instead of being at the mercy of a patient's memory or limited understanding of their medical profile.

Once a physician enter a patient's unique identifier or scans their data matrix, they can access the patient's medications, diagnosis, lab results, medical imaging results, past procedures, and doctor's notes for all of these. medx is unique in that it has built in discussion threads so that doctors can message in real time to coordinate patient care across specialties so that the entire network of care is always working together.

medx uses a collection of algorithms to detect dangerous combinations of medical data in a patient's profile and marks these in red to attract a doctor's attention. An example of this is that a patient with a rare disease called RCVS cannot take Aspirin or other NSAIDS. On the dashboard for a patient with RCVS who was just prescribed aspirin by a doctor, would appear a red warning under a section named "contraindications" which cross references a patient's known diseases and their prescriptions to find dangerous combinations.

medx allows a doctor to download a complete report for the patient so that the doctor can store this in their personal visit records.

## How we built it

medx was built in three parts: frontend, backend, and mobile. We built the backend as an express-based node.js web server using a REST api to deliver data from a mongodb database. The frontend is a dashboard-based interface using Bootstrap. The mobile app is an iOS app built using swift.

## Challenges we ran into

Using software to recognize and generate data matrices. Creating a modern, yet sophisticated, user interface with ease of use as its number one concern.

## Accomplishments that we're proud of

Working together and bridging our disciplines of medicine and computing respectively, to begin to help build a brighter, smarter future.

## What we learned

How to build and deploy a complex, fast, scalable web app. We also learned how to use Google Cloud to deploy a node.js web server. Most importantly, we learned the incredible power of technology to shape our world and transform people's lives.

## What's next for medx

Improving on cybersecurity to fully meet HIPPA standards. Employing machine learning to search for abnormal imaging results. Using machine learning to piece together all elements of medical profile to predict and suggest treatment plans.

(To try out the physician section of our website, please use DEA #: 31415926 Password: pass)