Modeling Treatment Delays for Patients using Feature Label Pairs in a Time Series
This work addresses pharmaceutical sales strategy planning by targeting physicians based on patient progression predictions, but it appears incremental as it builds on existing time series methods.
The paper tackles the problem of predicting patient disease and treatment progression for pharmaceutical targeting by developing a time-sensitive framework that uses feature-label pairs from service history to train a model, resulting in improved accuracy.
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.