Inspiration
With the increasing complexity of modern healthcare, many patients take multiple medications simultaneously, increasing the risk of drug-drug interactions (DDIs). Understanding these interactions is crucial for preventing adverse effects, improving patient safety, and optimizing treatment plans. We were inspired by the need to provide an accessible and user-friendly platform for both patients and healthcare professionals to quickly identify and understand DDIs.
What it does
MedLife is a web-based application that allows users to search for medications and obtain detailed information about their uses, common side effects, and potential interactions with other drugs. Users can input multiple medications, and the system will provide insights into known interactions, possible adverse effects, and recommendations for safer drug combinations.
How we built it
Frontend: HTML, CSS, and React to create a user-friendly interface. Backend: Node.js, Express.js, and Google Gemini API to retrieve and process drug-related data.
Challenges we ran into
Implementing machine learning models: We initially aimed to develop a predictive DDI model using GATs based on research papers. However, this approach proved to be computationally expensive, and data limitations made it difficult to achieve meaningful results. Data limitations: High-quality, labeled datasets for unknown DDIs were scarce, making it challenging to train a reliable predictive model. Integration with APIs: Ensuring smooth communication between the React frontend and the Google Gemini API required debugging and optimization.
Accomplishments that we're proud of
Successfully developed a functional and accessible interface for users to explore drug interactions. Integrated the Google Gemini API to provide reliable and informative medication data. Explored advanced machine learning techniques, which, despite the challenges, helped us better understand the complexities of predicting DDIs. Created a tool that has real-world applications in healthcare and patient safety.
What we learned
The importance of balancing ambitious machine learning goals with practical implementation. How to efficiently integrate APIs into a React-based web application. The significance of high-quality datasets in predictive modeling, especially in the healthcare domain. The value of adaptability—when our initial machine learning approach didn't work as expected, we successfully pivoted to a more feasible solution.
What's next for MedLife
Enhancing the dataset: We aim to integrate additional databases, such as FDA drug interaction databases and open-source biomedical datasets, to improve the accuracy of our tool. Machine learning improvements: Although our initial predictive model was challenging, we plan to refine our approach by incorporating more structured datasets and exploring alternative ML architectures. User experience improvements: Adding features such as personalized medication tracking, alerts for potential interactions, and recommendations for alternative medications. Mobile app development: Expanding MedLife into a mobile application for easier access on-the-go.
Log in or sign up for Devpost to join the conversation.