  Objective: To design and assess a method to leverage individuals' temporal
data for predicting their healthcare cost. To achieve this goal, we first used
patients' temporal data in their fine-grain form as opposed to coarse-grain
form. Second, we devised novel spike detection features to extract temporal
patterns that improve the performance of cost prediction. Third, we evaluated
the effectiveness of different types of temporal features based on cost
information, visit information and medical information for the prediction task.
  Materials and methods: We used three years of medical and pharmacy claims
data from 2013 to 2016 from a healthcare insurer, where the first two years
were used to build the model to predict the costs in the third year. To prepare
the data for modeling and prediction, the time series data of cost, visit and
medical information were extracted in the form of fine-grain features (i.e.,
segmenting each time series into a sequence of consecutive windows and
representing each window by various statistics such as sum). Then, temporal
patterns of the time series were extracted and added to fine-grain features
using a novel set of spike detection features (i.e., the fluctuation of data
points). Gradient Boosting was applied on the final set of extracted features.
Moreover, the contribution of each type of data (i.e., cost, visit and medical)
was assessed.
  Conclusions: Leveraging fine-grain temporal patterns for healthcare cost
prediction significantly improves prediction performance. Enhancing fine-grain
features with extraction of temporal cost and visit patterns significantly
improved the performance. However, medical features did not have a significant
effect on prediction performance. Gradient Boosting outperformed all other
prediction models.
