Deep neural network models, especially Long Short Term Memory (LSTM), have shown great success in analyzing Electronic Health Records (EHRs) due to their ability to capture temporal dependencies in time series data. When applying the deep learning models to EHRs, we are generally confronted with two major challenges: high rate of missingness and time irregularity. Motivated by the original PACIFIER framework which utilized matrix decomposition for data imputation, we applied and further extended it by including three components: forecasting future events, a time-aware mechanism, and a subgroup basis approach. We evaluated the proposed framework with real-world EHRs which consists of 52,919 visits and 4,224,567 events on a task of early prediction of septic shock. We compared our work against multiple baselines including the original PACIFIER using both LSTM and Time-aware LSTM (T-LSTM). Experimental results showed that our proposed framework significantly outperformed all competitive baseline approaches. More importantly, the extracted interpretative latent patterns from subgroups could shed some lights for clinicians to discover the progression of septic shock patients.
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Forecasting Gas Usage for Big Buildings Using Generalized Additive Models and Deep Learning
Time series behavior of gas consumption is highly irregular, non-stationary, and volatile due to its dependency on the weather, users' habits and lifestyle. This complicates the modeling and forecasting of gas consumption with most of the existing time series modeling techniques, specifically when missing values and outliers are present. To demonstrate and overcome these problems, we investigate two approaches to model the gas consumption, namely Generalized Additive Models (GAM) and Long Short-Term Memory (LSTM). We perform our evaluations on two building datasets from two different continents. We present each selected feature's influence, the tuning parameters, and the characteristics of the gas consumption on their forecasting abilities. We compare the performances of GAM and LSTM with other state-of-the-art forecasting approaches. We show that LSTM outperforms GAM and other existing approaches, however, GAM provides better interpretable results for building management systems (BMS).
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- Award ID(s):
- 1544687
- PAR ID:
- 10073257
- Date Published:
- Journal Name:
- 2018 IEEE International Conference on Smart Computing (SMARTCOMP)
- Page Range / eLocation ID:
- 203 to 210
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SMARTCOMP.2018.00092
