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An exploratory data quality analysis of time series physiologic signals using a large-scale intensive care unit database.
Afshar, Ali S; Li, Yijun; Chen, Zixu; Chen, Yuxuan; Lee, Jae Hun; Irani, Darius; Crank, Aidan; Singh, Digvijay; Kanter, Michael; Faraday, Nauder; Kharrazi, Hadi.
Afiliación
  • Afshar AS; Department of Anesthesiology and Critical Care Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland USA.
  • Li Y; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
  • Chen Z; Department of Computer Science, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland, USA.
  • Chen Y; Department of Computer Science, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland, USA.
  • Lee JH; Department of Computer Science, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland, USA.
  • Irani D; Department of Computer Science, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland, USA.
  • Crank A; Department of Computer Science, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland, USA.
  • Singh D; Department of Computer Science, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland, USA.
  • Kanter M; Department of Clinical Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, California, USA.
  • Faraday N; Department of Anesthesiology and Critical Care Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland USA.
  • Kharrazi H; Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
JAMIA Open ; 4(3): ooab057, 2021 Jul.
Article en En | MEDLINE | ID: mdl-34350392
Physiological data, such as heart rate and blood pressure, are critical to clinical decision-making in the intensive care unit (ICU). Vital signs data, which are available from electronic health records, can be used to diagnose and predict important clinical outcomes; While there have been some reports on the data quality of nurse-verified vital sign data, little has been reported on the data quality of higher frequency time-series vital signs acquired in ICUs, that would enable such predictive modeling. In this study, we assessed the data quality issues, defined as the completeness, accuracy, and timeliness, of minute-by-minute time series vital signs data within the MIMIC-III data set, captured from 16009 patient-ICU stays and corresponding to 9410 unique adult patients. We measured data quality of four time-series vital signs data streams in the MIMIC-III data set: heart rate (HR), respiratory rate (RR), blood oxygen saturation (SpO2), and arterial blood pressure (ABP). Approximately, 30% of patient-ICU stays did not have at least 1 min of data during the time-frame of the ICU stay for HR, RR, and SpO2. The percentage of patient-ICU stays that did not have at least 1 min of ABP data was ∼56%. We observed ∼80% coverage of the total duration of the ICU stay for HR, RR, and SpO2. Finally, only 12.5%%, 9.9%, 7.5%, and 4.4% of ICU lengths of stay had ≥ 99% data available for HR, RR, SpO2, and ABP, respectively, that would meet the three data quality requirements we looked into in this study. Our findings on data completeness, accuracy, and timeliness have important implications for data scientists and informatics researchers who use time series vital signs data to develop predictive models of ICU outcomes.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: JAMIA Open Año: 2021 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
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XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: JAMIA Open Año: 2021 Tipo del documento: Article Pais de publicación: Estados Unidos