# CarePre: An Intelligent Clinical Decision Assistance System

arXiv: https://arxiv.org/abs/1811.02218 · HTML: https://mlsanity.com/paper/1811.02218/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2018.

## Built on by (most cited first, 4 of 4)

- [Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies](https://mlsanity.com/paper/2107.09951/) (2021)
- [Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis](https://mlsanity.com/paper/2309.12368/) (2023)
- [Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skills](https://mlsanity.com/paper/2410.04253/) (2024)
- ["It depends": Configuring AI to Improve Clinical Usefulness Across Contexts](https://mlsanity.com/paper/2407.11978/) (2024)

## Abstract (arXiv)

Clinical decision support systems (CDSS) are widely used to assist with medical decision making. However, CDSS typically require manually curated rules and other data which are difficult to maintain and keep up-to-date. Recent systems leverage advanced deep learning techniques and electronic health records (EHR) to provide more timely and precise results. Many of these techniques have been developed with a common focus on predicting upcoming medical events. However, while the prediction results from these approaches are promising, their value is limited by their lack of interpretability. To address this challenge, we introduce CarePre, an intelligent clinical decision assistance system. The system extends a state-of-the-art deep learning model to predict upcoming diagnosis events for a focal patient based on his/her historical medical records. The system includes an interactive framework together with intuitive visualizations designed to support the diagnosis, treatment outcome analysis, and the interpretation of the analysis results. We demonstrate the effectiveness and usefulness of CarePre system by reporting results from a quantities evaluation of the prediction algorithm and a case study and three interviews with senior physicians.