석흥일 (고려대학교)

한빛사논문

조회85

고려대학교

CV File 213 kb

IEEE Trans. Neural Netw. Learn. Syst., Aug 29 2024, PP | https://doi.org/10.1109/TNNLS.2024.3442243 ADT2R: Adaptive Decision Transformer for Dynamic Treatment Regimes in Sepsis

Eunjin Jeon1, Jae-Hun Choi2, and Heung-Il Suk3

1Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea

2Digital Convergence Research Laboratory, Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129,Republic of Korea

3Department of Artificial Intelligence and the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea

Corresponding author: Heung-Il Suk.

Abstract

Dynamic treatment regimes (DTRs), which comprise a series of decisions taken to select adequate treatments, have attracted considerable attention in the clinical domain, especially from sepsis researchers. Existing sepsis DTR learning studies are mainly based on offline reinforcement learning (RL) approaches working on electronic healthcare records data. However, a trained policy may choose a treatment different from a human clinician's prescription. Furthermore, most of them do not consider: 1) heterogeneity in sepsis; 2) short-term transitions; and 3) the relationship between a patient's health state and the prescription. We propose a novel framework, an adaptive decision transformer for DTR (ADT² R), which recommends an optimal treatment action for each time step depending on the heterogeneity of the sepsis and a patient's evolving health states. Specifically, we devise a trajectory-optimization-based module to be trained with supervision for treatments and adaptively aggregate the multihead self-attentions by deliberating on various inherent time-varying patterns among sepsis patients. Furthermore, we estimate the patient's health state by adopting an actor-critic (AC) algorithm and inform the treatment recommendation by learning about its short-term changes. We validated the effectiveness of the proposed framework on the Medical Information Mart for Intensive Care III (MIMIC-III) dataset, an extensive intensive care database, by demonstrating performance comparable to the state-of-the-art methods.

논문정보

형식Research article
게재일2024년 08월 (BRIC 등록일 2024-09-03)
연구진국내 연구진
분야 바이오·의료융합 > 바이오·의료융합 기타

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