# Modeling treatment events in disease progression

## TL;DR

This paper introduces a machine learning framework called CSI that models disease progression trajectories from sparse data, accounting for confounding events, and demonstrates its effectiveness on simulated and real datasets.

## Contribution

The study presents a novel framework, CSI, that effectively models disease progression with sparse data and confounding events, improving clinical trajectory estimation.

## Key findings

- CSI converges to the global minimum.
- CSI outperforms existing methods in simulations.
- Effective on both simulated and real datasets.

## Abstract

Ability to quantify and predict progression of a disease is fundamental for selecting an appropriate treatment. Many clinical metrics cannot be acquired frequently either because of their cost (e.g. MRI, gait analysis) or because they are inconvenient or harmful to a patient (e.g. biopsy, x-ray). In such scenarios, in order to estimate individual trajectories of disease progression, it is advantageous to leverage similarities between patients, i.e. the covariance of trajectories, and find a latent representation of progression. Most of existing methods for estimating trajectories do not account for events in-between observations, what dramatically decreases their adequacy for clinical practice. In this study, we develop a machine learning framework named Coordinatewise-Soft-Impute (CSI) for analyzing disease progression from sparse observations in the presence of confounding events. CSI is guaranteed to converge to the global minimum of the corresponding optimization problem. Experimental results also demonstrates the effectiveness of CSI using both simulated and real dataset.