IS in Healthcare
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Paper Number
1383
Paper Type
short
Description
One of the central veins of the Information System (IS) discipline is to help practitioners in other fields to solve their real-world problems. This article focuses on building the decision support system for patients with Traumatic Brain Injuries (TBI) using Hidden Markov Models (HMM). Most of the existing literature focuses on the long-term outcomes arising from TBIs. Our development’s key distinguishing factor is that we predict the likelihood of stability within the next 24 hours to undergo a non-cranial surgery of polytrauma patients with TBI in acute settings. Our goal in this work is to build an HMM-based Bayesian optimization framework to help doctors to determine the optimal release schedule for TBI patients.
Recommended Citation
Zavadskiy, Gleb; Zantedeschi, Daniel; and Jank, Wolfgang, "A Bayesian Optimal Stopping Framework for Traumatic Brain Injuries Patients" (2021). ICIS 2021 Proceedings. 2.
https://aisel.aisnet.org/icis2021/is_health/is_health/2
A Bayesian Optimal Stopping Framework for Traumatic Brain Injuries Patients
One of the central veins of the Information System (IS) discipline is to help practitioners in other fields to solve their real-world problems. This article focuses on building the decision support system for patients with Traumatic Brain Injuries (TBI) using Hidden Markov Models (HMM). Most of the existing literature focuses on the long-term outcomes arising from TBIs. Our development’s key distinguishing factor is that we predict the likelihood of stability within the next 24 hours to undergo a non-cranial surgery of polytrauma patients with TBI in acute settings. Our goal in this work is to build an HMM-based Bayesian optimization framework to help doctors to determine the optimal release schedule for TBI patients.
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