Long short-term memory model identifies ARDS and in-hospital mortality in both non-COVID-19 and COVID-19 cohort.

IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES
Jen-Ting Chen, Rahil Mehrizi, Boudewijn Aasman, Michelle Ng Gong, Parsa Mirhaji
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引用次数: 0

Abstract

Objective: To identify the risk of acute respiratory distress syndrome (ARDS) and in-hospital mortality using long short-term memory (LSTM) framework in a mechanically ventilated (MV) non-COVID-19 cohort and a COVID-19 cohort.

Methods: We included MV ICU patients between 2017 and 2018 and reviewed patient records for ARDS and death. Using active learning, we enriched this cohort with MV patients from 2016 to 2019 (MV non-COVID-19, n=3905). We collected a second validation cohort of hospitalised patients with COVID-19 in 2020 (COVID+, n=5672). We trained an LSTM model using 132 structured features on the MV non-COVID-19 training cohort and validated on the MV non-COVID-19 validation and COVID-19 cohorts.

Results: Applying LSTM (model score 0.9) on the MV non-COVID-19 validation cohort had a sensitivity of 86% and specificity of 57%. The model identified the risk of ARDS 10 hours before ARDS and 9.4 days before death. The sensitivity (70%) and specificity (84%) of the model on the COVID-19 cohort are lower than MV non-COVID-19 cohort. For the COVID-19 + cohort and MV COVID-19 + patients, the model identified the risk of in-hospital mortality 2.4 days and 1.54 days before death, respectively.

Discussion: Our LSTM algorithm accurately and timely identified the risk of ARDS or death in MV non-COVID-19 and COVID+ patients. By alerting the risk of ARDS or death, we can improve the implementation of evidence-based ARDS management and facilitate goals-of-care discussions in high-risk patients.

Conclusion: Using the LSTM algorithm in hospitalised patients identifies the risk of ARDS or death.

Abstract Image

Abstract Image

长短期记忆模型确定了非COVID-19和COVID-19队列中ARDS和住院死亡率。
目的:利用长短期记忆(LSTM)框架识别机械通气(MV)非COVID-19队列和COVID-19队列中急性呼吸窘迫综合征(ARDS)的风险和住院死亡率。方法:我们纳入2017年至2018年期间的MV ICU患者,并回顾ARDS和死亡的患者记录。通过主动学习,我们将2016年至2019年的MV患者(MV非covid -19, n=3905)纳入该队列。我们收集了2020年住院的COVID-19患者的第二个验证队列(COVID+, n=5672)。我们在MV非COVID-19训练队列上使用132个结构化特征训练LSTM模型,并在MV非COVID-19验证和COVID-19队列上进行验证。结果:LSTM(模型评分0.9)对MV非covid -19验证队列的敏感性为86%,特异性为57%。该模型在ARDS发生前10小时和死亡前9.4天确定了ARDS的风险。该模型对COVID-19队列的敏感性(70%)和特异性(84%)低于MV非COVID-19队列。对于COVID-19 +队列和MV COVID-19 +患者,该模型分别在死亡前2.4天和1.54天确定了院内死亡风险。讨论:我们的LSTM算法准确、及时地识别了MV非COVID-19和COVID-19 +患者发生ARDS或死亡的风险。通过提醒ARDS或死亡的风险,我们可以改善ARDS循证管理的实施,并促进高危患者的护理目标讨论。结论:应用LSTM算法识别住院患者发生ARDS或死亡的风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.10
自引率
4.90%
发文量
40
审稿时长
18 weeks
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