Artificial Intelligence (AI) is getting a lot of attention lately because it can change many areas of our lives. Better computer power and more data have helped AI do amazing things, like improving medical tests and making self-driving cars. AI can also help businesses make better decisions and work more efficiently, which is why it's becoming more popular and widely used. How can one integrate the OpenAI API calls into an existing IRIS Interoperability application?
Striking a Balance Between Health Analytics and Engaging Features
In today's digital era, health and lifestyle apps have flooded the market, promising a wide array of benefits from fitness tracking to nutrition planning and focusing on trendy features, such as step counting, calorie tracking, and habit monitoring. While the surge in app development is undoubtedly a positive sign of society's growing interest in health and wellness features can be engaging and motivate users to adopt healthier lifestyles, they often lack the depth required for serious health analytics.
Many factors affect a person's quality of life, and one of the most important is sleep. The quality of our sleep determines our ability to function during the day and affects our mental and physical health. Good quality sleep is critical to our overall health and well-being. Therefore, by analyzing indicators preceding sleep, we can determine the quality of our sleep. This is precisely the functionality of the Sheep's Galaxy application.
Sheep's Galaxy is a sample application that works with InterSystems' IntegratedML and IRIS Cloud SQL technologies and provides the user with a tool to analyze and improve sleep quality. The analysis of sleep takes into account factors such as noise levels, room lighting, sleep duration, caffeine consumption, and more, allowing the user to reconsider their sleep-related habits and create optimal conditions for sleep in the future.
Presentation video:
https://www.youtube.com/watch?v=eZ9Wak831x4&ab_channel=MariaGladkova
The app is based on the following technologies:
Frontend part:
To build this app we used Angular framework. It helped us to create a simple single page application. We used Angular v15, and all Angular components were implemented as standalones to streamline the authoring experience. We didn’t use Angular modules and it's a good practice to scale an app in the future if it need. We also used Smart Component Architecture – all components in our frontend application are divided into "smart" and "dumb" components. This concepts helps us to separate the business logic code and presentation code between these components. All business logic and requests to server are kept in the isolated services. To process our backend data we use RxJS - a library for composing asynchronous and event-based programs by using observable sequences. To style our app we used Angular Material - it is a User Interface component library which developers can use in their Angular projects to speed up development of elegant and consistent user interfaces. This library offers a lot of reusable and beautiful UI components - we added some of them like Cards, Inputs, Data Tables, Datepickers, and much more. Below we present an overview of typical user workflow. First, user goes through either the registration process, if using it for the first time, or through authorization screen.
Using the app, the user enters information about sleep, such as activity level during the day, number of cups of coffee, sleeping comfort, stress level and amount of positive emotions, as well as room light and bedtime.
After each data entry, the user will receive a notification of sleep quality. This data is then analyzed using machine learning algorithms to provide users with insights into their sleep patterns.
Backend part:
Fastapi is a python framework based on two technologies: Pydantic and Starlette. It has the following features:
The project structure consists of routers with endpoints, models for each entity and processing services.
Each endpoint appears in the atomic documentation at
Pydantic models automatically validate incoming and outgoing data.
The process of working with user data is built on the protocol, which allows you to work with data in a secure way.
The process of interaction with the database is implemented through IRIS SQL connection using the DB API.
IRIS Cloud SQL with IntegratedML:
First, you need to login to InterSystems Cloud Services Portal. Here you need to create a new IRIS Cloud SQL deployment. Be sure to include IntegratedML when you create a new deployment. When it's ready, you can obtain connection parameters to use in docker-compose.yml:
By opening 'IntegratedML Tools' menu you have access to create, train, validate your model, and have the ability to generate predictions on a selected field in your model table.
In our app, we predict sleep quality based on user data. To do this, we fill in the fields in the Prediction section as follows:
In the generated query, the prediction field contains a prediction of the quality of sleep, the probability_quality field contains the probability that the dream will be "qualitative".
Links:
To learn more about our project or use it as a template for your future work: https://openexchange.intersystems.com/package/Sheep%E2%80%99s-Galaxy
Thanks:
Our team would like to thank InterSystems and Banksia Global for an opportunity to work with cutting-edge technology on important issues.
Developers of project:
Brainstorming the project we would build to showcase in the current female health themed InterSystems FHIR Contest, our girl band decided that we need to do something practical for the ordinary user and to solve some burning issues of the modern life. This discussion led to the idea of creating a project that will help women not to forget their health in daily grind - FemTech Reminder.
Project presentation video:
https://www.youtube.com/watch?v=LaHJYejc-5I
The FemTech Reminder project contains four main components:
The user communicates with our system via a chatbot. It allows the user to get notifications directly into the messenger without installing additional software. Using a bot, the user can register in Reminder service and associate their profile with a patient record on the FHIR server, allowing us to receive notifications based on patient information in the future. The Reminder server sends notifications once a day, analyzing user data.
Three types of notifications are implemented at the time of publication of this article.
Age Notifications:
Notifications with age-specific recommendations are based on Worldwide Health Organization (WHO) recommendations regarding medical checkup. This type of notification is characterized by age range and frequency of notifications. The server decides sending notifications to the user by analyzing these criteria.
You can view all recommendations using the GUI.
The web interface also allows you to add recommendations.
Pregnancy notification:
Pregnancy recommendations based on the recommendations of the WHO prenatal care model. While sending a notification, the system checks the patient’s week of pregnancy, it is calculated based on FHIR server's pregnancy observation record.
Appointment notifications:
Appointment notifications will be sent the day after the doctor’s visit. The patient will get a recommendation from the FHIR server, which was filed by their doctor during the visit.
To learn more about our project or use it as a template for your future work: https://openexchange.intersystems.com/package/FemTech-Reminder
Thanks:
Our team would like to thank InterSystems and Banksia Global for an opportunity to work with cutting-edge technology on important present-day female issues. We hope that our bot-reminder open-source project will help our colleagues to innovate and solve complex healthcare issues to help women to feel good and stay healthy.
Developers of project: