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題名 Improved inpatient deterioration detection in general wards by using time-series vital signs
作者 邱淑怡
Chiu, Shu-I;Su, Chang-Fu;Jang, Jyh-Shing Roger;Lai, Feipei
貢獻者 資訊系
日期 2022-07
上傳時間 5-三月-2024 16:26:55 (UTC+8)
摘要 Although in-hospital cardiac arrest is uncommon, it has a high mortality rate. Risk identification of at-risk patients is critical for post-cardiac arrest survival rates. Early warning scoring systems are generally used to identify hospitalized patients at risk of deterioration. However, these systems often require clinical data that are not always regularly measured. We developed a more accurate, machine learning-based model to predict clinical deterioration. The time series early warning score (TEWS) used only heart rate, systolic blood pressure, and respiratory data, which are regularly measured in general wards. We tested the performance of the TEWS in two tasks performed with data from the electronic medical records of 16,865 adult admissions and compared the results with those of other classifications. The TEWS detected more deteriorations with the same level of specificity as the different algorithms did when inputting vital signs data from 48 h before an event. Our framework improved in-hospital cardiac arrest prediction and demonstrated that previously obtained vital signs data can be used to identify at-risk patients in real-time. This model may be an alternative method for detecting patient deterioration.
關聯 Scientific Reports, Vol.12, 11901
資料類型 article
DOI https://doi.org/10.1038/s41598-022-16195-2
dc.contributor 資訊系
dc.creator (作者) 邱淑怡
dc.creator (作者) Chiu, Shu-I;Su, Chang-Fu;Jang, Jyh-Shing Roger;Lai, Feipei
dc.date (日期) 2022-07
dc.date.accessioned 5-三月-2024 16:26:55 (UTC+8)-
dc.date.available 5-三月-2024 16:26:55 (UTC+8)-
dc.date.issued (上傳時間) 5-三月-2024 16:26:55 (UTC+8)-
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/150398-
dc.description.abstract (摘要) Although in-hospital cardiac arrest is uncommon, it has a high mortality rate. Risk identification of at-risk patients is critical for post-cardiac arrest survival rates. Early warning scoring systems are generally used to identify hospitalized patients at risk of deterioration. However, these systems often require clinical data that are not always regularly measured. We developed a more accurate, machine learning-based model to predict clinical deterioration. The time series early warning score (TEWS) used only heart rate, systolic blood pressure, and respiratory data, which are regularly measured in general wards. We tested the performance of the TEWS in two tasks performed with data from the electronic medical records of 16,865 adult admissions and compared the results with those of other classifications. The TEWS detected more deteriorations with the same level of specificity as the different algorithms did when inputting vital signs data from 48 h before an event. Our framework improved in-hospital cardiac arrest prediction and demonstrated that previously obtained vital signs data can be used to identify at-risk patients in real-time. This model may be an alternative method for detecting patient deterioration.
dc.format.extent 106 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Scientific Reports, Vol.12, 11901
dc.title (題名) Improved inpatient deterioration detection in general wards by using time-series vital signs
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1038/s41598-022-16195-2
dc.doi.uri (DOI) https://doi.org/10.1038/s41598-022-16195-2