{"type":"ScholarlyArticle","title":"A Diffusion Model with Contrastive Learning for ICU False Arrhythmia Alarm Reduction","authors":[{"name":"Feng Wu","slug":"feng-wu","given":"Feng","family":"Wu","affiliation":"The Institute for Medical Engineering & Science , Massachusetts Institute of Technology"},{"name":"Guoshuai Zhao","slug":"guoshuai-zhao","given":"Guoshuai","family":"Zhao","affiliation":"an Jiaotong University"},{"name":"Xueming Qian","slug":"xueming-qian","given":"Xueming","family":"Qian","affiliation":"an Jiaotong University"},{"name":"Li-wei H. Lehman","slug":"li-wei-h-lehman","given":"Li-wei H.","family":"Lehman","affiliation":"The Institute for Medical Engineering & Science , Massachusetts Institute of Technology"}],"abstract":"The high rate of false arrhythmia alarms in intensive care units (ICUs) can negatively impact patient care and lead to slow staff response time due to alarm fatigue. To reduce false alarms in ICUs, previous works proposed conventional supervised learning methods which have inherent limitations in dealing with high-dimensional, sparse, unbalanced, and limited data. We propose a deep generative approach based on the conditional denoising diffusion model to detect false arrhythmia alarms in the ICUs. Conditioning on past waveform data of a patient, our approach generates waveform predictions of the patient during an actual arrhythmia event, and uses the distance between the generated and the observed samples to classify the alarm. We design a network with residual links and self-attention mechanism to capture long-term dependencies in signal sequences, and leverage the contrastive learning mechanism to maximize distances between true and false arrhythmia alarms. We demonstrate the effectiveness of our approach on the MIMIC II arrhythmia dataset for detecting false alarms in both retrospective and real-time settings.","doi":"10.24963/ijcai.2023/546","url":"https://ijcai.org/proceedings/2023/546","pdf":"https://ijcai.org/proceedings/2023/0546.pdf","markdown":"https://ijcai.org/proceedings/2023/546.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/546","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":546,"pages":{"first":4912,"last":4920},"keywords":["Multidisciplinary Topics and Applications: MDA: Health and medicine","Machine Learning: ML: Applications","Machine Learning: ML: Time series and data streams"],"videos":[],"references":[{"position":1,"raw":"[Aboukhalil et al., 2008] Anton Aboukhalil, Larry Nielsen, Mohammed Saeed, Roger G Mark, and Gari D Clifford. 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