Modeling patient disease progression using Electronic Health Records (EHRs) is critical to assist clinical decision making. Long-Short Term Memory (LSTM) is an effective model to handle sequential data, such as EHRs, but it encounters two major limitations when applied to EHRs: it is unable to interpret the prediction results and it ignores the irregular time intervals between consecutive events. To tackle these limitations, we propose an attention-based time-aware LSTM Networks (ATTAIN), to improve the interpretability of LSTM and to identify the critical previous events for current diagnosis by modeling the inherent time irregularity. We validate ATTAIN on modeling the progression of an extremely challenging disease, septic shock, by using real-world EHRs. Our results demonstrate that the proposed framework outperforms the state-of-the-art models such as RETAIN and T-LSTM. Also, the generated interpretative time-aware attention weights shed some lights on the progression behaviors of septic shock.
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Multi-series Time-aware Sequence Partitioning for Disease Progression Modeling.
Electronic healthcare records (EHRs) are comprehensive longitudinal collections of patient data that
play a critical role in modeling the disease progression to facilitate clinical decision-making. Based
on EHRs, in this work, we focus on sepsis – a broad
syndrome that can develop from nearly all types of
infections (e.g., influenza, pneumonia). The symptoms of sepsis, such as elevated heart rate, fever,
and shortness of breath, are vague and common
to other illnesses, making the modeling of its progression extremely challenging. Motivated by the
recent success of a novel subsequence clustering
approach: Toeplitz Inverse Covariance-based Clustering (TICC), we model the sepsis progression as
a subsequence partitioning problem and propose a
Multi-series Time-aware TICC (MT-TICC), which
incorporates multi-series nature and irregular time
intervals of EHRs. The effectiveness of MT-TICC
is first validated via a case study using a real-world
hand gesture dataset with ground-truth labels. Then
we further apply it for sepsis progression modeling
using EHRs. The results suggest that MT-TICC can
significantly outperform competitive baseline models, including the TICC. More importantly, it unveils interpretable patterns, which sheds some light
on better understanding the sepsis progression.
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- Award ID(s):
- 1916417
- NSF-PAR ID:
- 10324504
- Date Published:
- Journal Name:
- Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI)
- Page Range / eLocation ID:
- 3581-3587
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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