Capturing Feature-Level Irregularity in Disease Progression Modeling
By Kaiping Zheng, Wei Wang, Jinyang Gao, Kee Yuan Ngiam, Beng Chin Ooi, J. Yip. 2017
34 citations recorded by the source corpus.
Disease progression modeling (DPM) analyzes patients' electronic medical records (EMR) to predict the health state of patients, which facilitates accurate prognosis, early detection and treatment of chronic diseases. However, EMR are irregular because patients visit hospital irregularly based on the need of treatment. For each visit, they are typically given different diagnoses, prescribed various medications and lab tests. Consequently, EMR exhibit irregularity at the feature level. To handle this issue, we propose a model based on the Gated Recurrent Unit by decaying the effect of previous records using fine-grained feature-level time span information, and learn the decaying parameters for different features to take into account their different behaviours like decaying speeds under irregularity. Extensive experimental results in both an Alzheimer's disease dataset and a chronic kidney disease dataset demonstrate that our proposed model of capturing feature-level irregularity can effectively improve the accuracy of DPM.
Subfields: Artificial Intelligence, Epidemiology, Health Information Management
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Beng Chin Ooi
- Slobodan Vucetic
- Meihui Zhang
- Tian Bai
- Shanshan Zhang
- Sheng Wang
- Jinyang Gao
- Teck Khim Ng
In Health Information Management
Citations in the corpus
- Interpretable Representation Learning for Healthcare via Capturing Disease Progression through Time (2018, methodology): “[35] uses a time decay term in the update gate in GRU to find a tradeoff between the previous hidden state and the candidate hidden state.”
- Rafiki: Machine Learning as an Analytics Service System (2018, methodology): “Rafiki has been deployed to support various applications, including food image recognition as a component of a nutrition tacking App(10), disease progression modelling [34] for an integrative healcare analytics system [18], etc.”