Patient Safety: Adverse effects of antihypertensive drugs

Inspiration

The inspiration behind building a model to predict individuals at highest risk of experiencing adverse effects from their hypertension medication is to improve patient safety and minimize the potential harm from such medications. With the increasing prevalence of hypertension and the use of medication to manage it, the need for a model to predict potential adverse effects is becoming increasingly important.

What it does

The model predicts which individuals are at highest risk of experiencing adverse effects from their hypertension medication by considering various factors such as age, gender, weight, dose, and the combination of these factors. The model is designed to provide healthcare providers with information to make informed decisions about treatment options and to help patients understand the potential risks associated with their medication.

How we built it

The model was built using a machine learning algorithm that analyzed data from previous studies and patient records. The algorithm considered various factors such as age, gender, weight, dose and the combination of these factors, to determine which individuals were at highest risk of experiencing adverse effects from their hypertension medication. The algorithm was trained on a large dataset and fine-tuned to increase accuracy.

Challenges we ran into

One of the challenges in building the model was obtaining a large and diverse enough dataset to train the algorithm.

Accomplishments that we're proud of

Extracting data from API

What we learned

Through the process of building this model, we learned about the importance of accurate data and the impact that various factors can have on the risk of adverse effects from hypertension medication. We also gained a deeper understanding of machine learning algorithms and their potential applications in healthcare.

What's next for Patient Safety: Adverse effects of antihypertensive drugs

Going forward, we plan to further improve the model by incorporating additional data and considering other relevant factors. We also plan to validate the model in real-world scenarios and to make it available to healthcare providers to help improve patient safety.