Inspiration
Personalized healthcare and women’s health. NSMP endometrial cancer lacks specific tools to personalize prognosis and treatment.
What it does
It predicts the risk of recurrence over time for patients with NSMP endometrial cancer using clinical and pathological data, and classifies patients into risk groups.
How we built it
We curated a real clinical dataset, defined a time-to-event endpoint, applied survival-based statistical models, and translated the results into a conceptual clinical calculator.
Challenges we ran into
Heterogeneous clinical data, mixed date formats, missing values, and the need to carefully define clinically meaningful endpoints.
Accomplishments that we’re proud of
Creating a clinically interpretable prognostic model and framing it as a scalable digital health tool.
What we learned
The importance of data quality, endpoint definition, and interpretability in clinical prediction models.
Built With
- janitor
- r
- shiny
- survival
Log in or sign up for Devpost to join the conversation.