CONGRATS this is so exciting!!!

aww thank you!!! you too for your best paper 😌🫶🏼

Ahh thank you! ☺️

yay!! 🤩

I really enjoyed (and learned a LOT from) working on this project with these wonderful co-authors:
@dmshanmugam.bsky.social
Ashley Beecy
Gabriel Sayer
@destrin.bsky.social
@nkgarg.bsky.social
@emmapierson.bsky.social
7/7

Our work underscores the importance of accounting for health disparities; we lay a foundation for doing so with a method to (1) estimate disease severity in the presence of health disparities and (2) identify disparity patterns that can inform public health interventions. 6/

The interpretability and identifiability of our model also allow us to learn fine-grained descriptions of disparities. Fitting our model on heart failure patient data from NewYork-Presbyterian, our model identifies groups that face each type of health disparity. 5/

We prove that *failing to* account for these disparities biases severity estimates. By jointly accounting for all three, our model more accurately recovers severity. Indeed, accounting for these disparities in real heart failure data does meaningfully shift severity estimates. 4/

We propose an interpretable disease progression model that captures 3 key disparities: certain patient groups may (1) start receiving care at higher disease severity levels, (2) experience faster disease progression, or (3) receive less frequent care conditional on severity. 3/

Disease progression models are often used to help healthcare providers diagnose and treat chronic diseases. But these models have historically failed to account for health disparities that bias the data they are trained on. 2/