# Prediction of Individual Outcomes for Asthma Sufferers

## TL;DR

This paper develops a longitudinal model to predict individual asthma outcomes and optimize medication recommendations, using US survey data and latent variable techniques to improve clinical decision-making.

## Contribution

It introduces a novel latent process model for medication effects that accounts for prescription refill data, enhancing personalized asthma treatment predictions.

## Key findings

- Effective prediction of adverse asthma events
- Reduced bias in medication effect estimation
- Framework generalizable to other conditions

## Abstract

We consider the problem of individual-specific medication level recommendation (initiation, removal, increase, or decrease) for asthma sufferers. Asthma is one of the most common chronic diseases in both adults and children, affecting 8% of the US population and costing $37-63 billion/year in the US. Asthma is a complex disease, whose symptoms may wax and wane, making it difficult for clinicians to predict outcomes and prognosis. Improved ability to predict prognosis can inform decision making and may promote conversations between clinician and provider around optimizing medication therapy. Data from the US Medical Expenditure Panel Survey (MEPS) years 2000-2010 were used to fit a longitudinal model for a multivariate response of adverse events (Emergency Department or In-patient visits, excessive rescue inhaler use, and oral steroid use). To reduce bias in the estimation of medication effects, medication level was treated as a latent process which was restricted to be consistent with prescription refill data. This approach is demonstrated to be effective in the MEPS cohort via predictions on a validation hold out set and a synthetic data simulation study. This framework can be easily generalized to medication decisions for other conditions as well.