Abstract
When the body’s defense against an infection damages its own tissues and causes organ malfunction, it develops sepsis, a catastrophic medical illness. Administering intravenous fluids and antibiotics promptly can increase the patient’s chances of survival. In order to determine the best treatment plans for septic patients, this study investigates the application of deep reinforcement learning and continuous state-space models. The method produces clinically comprehensible policies that could assist doctors in intensive care in empowering medical professionals to make informed decisions that ultimately enhance the prospects of patient survival.
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Singh, L., Kamra, L. (2024). Synergizing Reinforcement Learning for Cognitive Medical Decision-Making in Sepsis Detection. In: Lopata, A., Gudonienė, D., Butkienė, R. (eds) Information and Software Technologies. ICIST 2023. Communications in Computer and Information Science, vol 1979. Springer, Cham. https://doi.org/10.1007/978-3-031-48981-5_13
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