# Modeling Treatment Delays for Patients using Feature Label Pairs in a Time Series

## TL;DR

This paper introduces a time series framework for predicting patient disease progression and treatment delays, enhancing pharmaceutical targeting strategies by leveraging historical service data and future event prediction.

## Contribution

It presents a novel time-sensitive modeling approach that uses feature-label pairs from service history to improve prediction accuracy of patient treatment delays.

## Key findings

- Improved prediction accuracy for treatment delays.
- Effective use of time features from service history.
- Framework applicable to pharmaceutical targeting strategies.

## Abstract

Pharmaceutical targeting is one of key inputs for making sales and marketing strategy planning. Targeting list is built on predicting physician's sales potential of certain type of patient. In this paper, we present a time-sensitive targeting framework leveraging time series model to predict patient's disease and treatment progression. We create time features by extracting service history within a certain period, and record whether the event happens in a look-forward period. Such feature-label pairs are examined across all time periods and all patients to train a model. It keeps the inherent order of services and evaluates features associated to the imminent future, which contribute to improved accuracy.