Sequential Representation of Sparse Heterogeneous Data for Diabetes Risk Prediction
- Resource Type
- Conference
- Authors
- Chaturvedi, Rochana; Rashid, Mudassir; Layden, Brian T.; Boyd, Andrew; Cinar, Ali; Di Eugenio, Barbara
- Source
- 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Bioinformatics and Biomedicine (BIBM), 2023 IEEE International Conference on. :831-834 Dec, 2023
- Subject
- Bioengineering
Computing and Processing
Engineering Profession
Robotics and Control Systems
Signal Processing and Analysis
Soft sensors
Pipelines
Predictive models
Prediction algorithms
Data models
Diabetes
Data mining
Machine Learning
Natural Language Processing
Disease Prediction
- Language
- ISSN
- 2156-1133
Type 2 diabetes (T2D) is a major public health problem, and opportunistic screening to detect T2D at an early stage can help initiate interventions that delay or prevent the disease and its complications. In this study, we use electronic health records (EHR) and concepts extracted from clinical notes to predict future T2D risk. Our deep neural network-based model captures the temporal sequence of patient visits. We use explainable AI algorithms to assess the model decisions and observe alignment with the domain knowledge of clinical experts.