Conference proceeding
MedRetriever: Target-Driven Interpretable Health Risk Prediction via Retrieving Unstructured Medical Text
Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp.2414-2423
ACM Conferences
CIKM '21: The 30th ACM International Conference on Information and Knowledge Management
10/26/2021
DOI: 10.1145/3459637.3482273
Abstract
The broad adoption of electronic health record (EHR) systems and the advances of deep learning technology have motivated the development of health risk prediction models, which mainly depend on the expressiveness and temporal modeling capacity of deep neural networks (DNNs) to improve prediction performance. Some further augment the prediction by using external knowledge, however, a great deal of EHR information inevitably loses during the knowledge mapping. In addition, prediction made by existing models usually lacks reliable interpretation, which undermines their reliability in guiding clinical decision-making. To solve these challenges, we propose MedRetriever, an effective and flexible framework that leverages unstructured medical text collected from authoritative websites to augment health risk prediction as well as to provide understandable interpretation. Besides, MedRetriever explicitly takes the target disease documents into consideration, which provide key guidance for the model to learn in a target-driven direction, i.e., from the target disease to the input EHR. To specify, MedRetriever can flexibly choose its backbone from major predictive models to learn the EHR embedding for each visit. After that, the EHR embedding and features of target disease documents are aggregated into a query by self-attention to retrieve highly relevant text segments from the medical text pool, which is stored in the dynamically updated text memory. Finally, the comprehensive EHR embedding and the text memory are used for prediction and interpretation. We evaluate MedRetriever against nine state-of-the-art approaches across three real-world EHR datasets, which consistently achieves the best performance in AUC and recall metrics and outperforms the best baseline by at least 4.8% in recall on three test datasets. Furthermore, we conduct case studies to show the easy-to-understand interpretation by MedRetriever.
Details
- Title: Subtitle
- MedRetriever: Target-Driven Interpretable Health Risk Prediction via Retrieving Unstructured Medical Text
- Creators
- Muchao Ye - Pennsylvania State UniversitySuhan Cui - Pennsylvania State UniversityYaqing Wang - Purdue University West LafayetteJunyu Luo - Pennsylvania State UniversityCao Xiao - Amplitude, Seattle, WA, USAFenglong Ma - Pennsylvania State University
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp.2414-2423
- Conference
- CIKM '21: The 30th ACM International Conference on Information and Knowledge Management
- Publisher
- ACM
- Series
- ACM Conferences
- DOI
- 10.1145/3459637.3482273
- Language
- English
- Date published
- 10/26/2021
- Academic Unit
- Computer Science
- Record Identifier
- 9984696569602771
Metrics
2 Record Views