Research Area: Machine Learning
We develop a deep learning model based on Long Short-term Memory (LSTM) to predict blood pressure based on a unique data set collected from physical examination centers capturing comprehensive multi-year physical examination and lab results. In the Multi-attention Collaborative Deep Learning model (MAC-LSTM) we developed for this type of data, we incorporate three types of attention to generate more explainable and accurate results. In addition, we leverage information from similar users to enhance the predictive power of the model due to the challenges with short examination history. Our model significantly reduces predictive errors compared to several state-of-the-art baseline models. Experimental results not only demonstrate our model’s superiority but also provide us with new insights about factors influencing blood pressure. Our data is collected in a natural setting instead of a setting designed specifically to study blood pressure, and the physical examination items used to predict blood pressure are common items included in regular physical examinations for all the users. Therefore, our blood pressure prediction results can be easily used in an alert system for patients and doctors to plan prevention or intervention. The same approach can be used to predict other health-related indexes such as BMI.
Keywords:
Multi-attention Collaborative
Blood Pressure Prediction
Long Short-term Memory (LSTM)
Deep Learning
Author(s) Name: Luo He , Hongyan Liu , Yinghui Yang , Bei Wang
Journal name: ACM Transactions on Management Information Systems
Conferrence name:
Publisher name: ACM
DOI: 10.1145/3471571
Volume Information: Volume 13, Issue 2
Paper Link: https://dl.acm.org/doi/abs/10.1145/3471571
