The problem PharmaPredict solves

The problem I aim to address with the Drug-Drug Interaction (DDI) Predictor is the occurrence of adverse effects and reduced treatment efficacy when multiple drugs are taken together. This phenomenon, known as drug-drug interactions, is a critical challenge in healthcare. Here's why it's important:

Patient Safety
Drug interactions are a leading cause of adverse drug reactions (ADRs). A study by the American Journal of Medicine found that 88% of ADRs were due to drug interactions.

Healthcare Costs
The economic impact of drug interactions is substantial. Research published in PLOS ONE estimated that drug interactions contribute to a significant increase in healthcare costs.

Elderly Population
With an aging population, the elderly are more susceptible to polypharmacy (taking multiple medications). A study in the Journal of the American Geriatrics Society showed that 35% of older adults are prescribed potentially inappropriate medications.

Complex Treatment Regimens
Many patients with chronic conditions require multiple medications. Simplifying treatment regimens can improve adherence and health outcomes.

Avoidable Hospitalizations
Drug interactions can lead to hospital admissions. Research in the Journal of Clinical Pharmacy and Therapeutics indicated that up to 10% of hospital admissions are due to drug interactions.

Precision Medicine
Personalized medicine is the future of healthcare. Predicting and managing drug interactions is crucial for tailoring treatments to individual patients.

Challenges I ran into

Feature Engineering: . Deciding which features to use and how to represent them in a way that the model can understand required careful consideration and experimentation.

Hyperparameter Tuning: .I had to strike a balance between finding the best-performing hyperparameters and available computational resources.

Deployment and User Interface: Developing a user-friendly web application for end-users to interact with the model introduced challenges related to user interface design, ensuring smooth deployment, and handling user inputs effectively.

Model Interpretability: Deep learning models can be challenging to interpret. Understanding why the model makes specific predictions is crucial, especially in healthcare applications. We worked on techniques to make the model's predictions more interpretable and explainable.