# Learning Hidden Patterns from Patient Multivariate Time Series Data Using Convolutional Neural Networks: A Case Study of Healthcare Cost Prediction

arXiv: https://arxiv.org/abs/2009.06783 · HTML: https://mlsanity.com/paper/2009.06783/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2020.

## Built on by (most cited first, 7 of 7)

- [Time Series Prediction using Deep Learning Methods in Healthcare](https://mlsanity.com/paper/2108.13461/) (2021)
- [TimeWak: Temporal Chained-Hashing Watermark for Time Series Data](https://mlsanity.com/paper/2506.06407/) (2025)
- [FedGlu: A personalized federated learning-based glucose forecasting algorithm for improved performance in glycemic excursion regions](https://mlsanity.com/paper/2408.13926/) (2024)
- [Healthcare cost prediction for heterogeneous patient profiles using deep learning models with administrative claims data](https://mlsanity.com/paper/2502.12277/) (2025)
- [New care pathways for supporting transitional care from hospitals to home using AI and personalized digital assistance](https://mlsanity.com/paper/2504.13877/) (2025)
- [Static and multivariate-temporal attentive fusion transformer for readmission risk prediction](https://mlsanity.com/paper/2407.11096/) (2024)
- [Detecting Diffusion-Generated Time Series Under Generator Shift](https://mlsanity.com/paper/2605.28355/) (2026)

## Abstract (arXiv)

Objective: To develop an effective and scalable individual-level patient cost prediction method by automatically learning hidden temporal patterns from multivariate time series data in patient insurance claims using a convolutional neural network (CNN) architecture. Methods: We used three years of medical and pharmacy claims data from 2013 to 2016 from a healthcare insurer, where data from the first two years were used to build the model to predict costs in the third year. The data consisted of the multivariate time series of cost, visit and medical features that were shaped as images of patients' health status (i.e., matrices with time windows on one dimension and the medical, visit and cost features on the other dimension). Patients' multivariate time series images were given to a CNN method with a proposed architecture. After hyper-parameter tuning, the proposed architecture consisted of three building blocks of convolution and pooling layers with an LReLU activation function and a customized kernel size at each layer for healthcare data. The proposed CNN learned temporal patterns became inputs to a fully connected layer. Conclusions: Feature learning through the proposed CNN configuration significantly improved individual-level healthcare cost prediction. The proposed CNN was able to outperform temporal pattern detection methods that look for a pre-defined set of pattern shapes, since it is capable of extracting a variable number of patterns with various shapes. Temporal patterns learned from medical, visit and cost data made significant contributions to the prediction performance. Hyper-parameter tuning showed that considering three-month data patterns has the highest prediction accuracy. Our results showed that patients' images extracted from multivariate time series data are different from regular images, and hence require unique designs of CNN architectures.