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. 2019 Aug 21:264:148-152.
doi: 10.3233/SHTI190201.

Leveraging Electronic Health Records to Learn Progression Path for Severe Maternal Morbidity

Cheng Gao  1 , Sarah Osmundson  2 , Xiaowei Yan  3 , Digna Velez Edwards  1   2 , Bradley A Malin  1   4   5 , You Chen  1
Affiliations

Leveraging Electronic Health Records to Learn Progression Path for Severe Maternal Morbidity

Cheng Gao et al. Stud Health Technol Inform. .

Abstract

Severe maternal morbidity (SMM) encompasses a wide range of serious health complications that would likely result in death without in-time medical attention. It has been recognized that various demographic factors (e.g., age and race) and medical conditions (e.g., preeclampsia and organ failure) are associated with SMM. However, how medical conditions develop into SMM is seldom investigated. We hypothesize that SMM has a progression path, which is associated with a sequence of risk factors rather than a set of independent individual factors. We implemented a data-driven framework that leverages electronic health records (EHRs) in the antepartum period to learn the temporal patterns and measure their relationships with SMM during the delivery hospitalization. We evaluate the framework with two years of data from 6,184 women who had delivery hospitalizations at Vanderbilt University Medical Center. We discovered 69 temporal patterns, 12 of which were confirmed to be significantly associated with SMM.

Keywords: Electronic health records; Pregnancy Risk Factors.

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Figures

Figure 1.
Figure 1.
An overview of the data-driven framework to learn temporal patterns related to SMM.
Figure 2.
Figure 2.
An example of four temporal perinatal encounters of a patient. Codes were assigned in each of the three antepartum encounters.
Figure 3.
Figure 3.
An example of learning temporal relations from six code sequences extracted from five encounters
Figure 4.
Figure 4.
The adjusted relative risk of relationships between temporal patterns and SMM.
See this image and copyright information in PMC

References

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