Article2024 IEEE International Conference on E-health Networking, Application & Services (HealthCom)2024
Decoding a Hidden Energy State Based on Marked Point Process Cortisol Secretory Events During Cardiac Surgery.
Article in 2024 IEEE International Conference on E-health Networking, Application & Services (HealthCom), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Cortisol is critical in regulating one's energy state in response to stressful events such as surgical procedures. Decoding a cortisol-related energy state during surgery can assist in managing one's overall health status under inflammation. In this study, we decode a hidden cortisol-related energy state from each patient's cortisol profile during coronary arterial bypass grafting surgery. In particular, we employ a Bayesian state estimation approach within an expectation-maximization framework and estimate the energy state from the observation vector, which consists of the inferred cortisol secretory events coupled with a reconstructed high frequency cortisol profile. This reconstructed cortisol profile has a one-minute resolution and is obtained by using the estimates from deconvolution of cortisol data sampled at every 10 minutes. We find a higher energy state within the post-surgery phase compared to the surgery phase for all the studied patients (10 patients), which may depict the decoder's reliability in manifesting clinically relevant information. Tracking the person-specific cortisol-related energy state during surgery could provide insights into intervention design procedures and treatment plans.
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